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    "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0105">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">Invasive mechanical ventilation &#40;IMV&#41; is a cornerstone of organ support in severe COVID-19 patients with acute respiratory distress syndrome &#40;ARDS&#41;&#46; As widely experienced in ICUs during the SARS-CoV-2 pandemic&#44; IMV frequently causes complications&#46;<a class="elsevierStyleCrossRefs" href="#bib0005"><span class="elsevierStyleSup">1&#44;2</span></a> Hospital services were overwhelmed not only by the surge of patients&#44; but also by scarce human resources and equipment&#44; lack of sufficient mechanical ventilators being probably the most relevant&#46; In surge scenarios&#44; appropriate triage strategies are therefore needed to allocate IMV or alternatives such as high flow nasal prongs&#46; These strategies should be based on the knowledge and understanding of specific potential predictors<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a> that could help clinicians to personalize decisions regarding IMV&#46;</p><p id="par0010" class="elsevierStylePara elsevierViewall">There is still considerable controversy regarding who and when to intubate&#46; Several recent studies have addressed the subject&#44;<a class="elsevierStyleCrossRef" href="#bib0020"><span class="elsevierStyleSup">4</span></a> although bias cannot be excluded in observational non-randomized trials&#46; A retrospective study suggested that early intubation and IMV is associated with favorable outcomes but included only intubated patients instead of the whole population at risk&#46;</p><p id="par0015" class="elsevierStylePara elsevierViewall">Previous studies have identified covid-19 progression predictors including age&#44; comorbidities&#44; renal function&#44; or immunodeficiency<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a> using traditional statistical approaches&#44; where collinearity of data cannot be ruled out&#46; Artificial intelligence &#40;AI&#41; is currently being used for COVID-19 risk stratification&#44;<a class="elsevierStyleCrossRef" href="#bib0030"><span class="elsevierStyleSup">6</span></a> studying multiple clinical features to increase effectiveness and efficiency in diagnosis&#44; treatment&#44; and prognosis&#46; Self-explainable Machine learning &#40;ML&#41; techniques can help with risk factor selection through ranking methodologies&#46;<a class="elsevierStyleCrossRef" href="#bib0035"><span class="elsevierStyleSup">7</span></a> In this context&#44; the utilization of artificial intelligence &#40;AI&#41; holds potential in facilitating the development of a conceptual model aimed at comparing the significance of variables&#46; This can be achieved by employing regularization models<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">8</span></a> to enhance predictor selection&#44; followed by the implementation of the Generalized Linear Mixed-effects Model &#40;GLMM&#41;<a class="elsevierStyleCrossRefs" href="#bib0045"><span class="elsevierStyleSup">9&#8211;11</span></a> to construct the said conceptual model&#46; Such an approach becomes particularly relevant when assessing and comparing outcomes across different AI models&#44; enabling a comprehensive evaluation of variable significance&#46; This is a novel methodology&#44; leveraging modern machine learning techniques to provide rigorous and applicable insight into relevant clinical questions when randomized clinical trials are not feasible&#46; From here on&#44; in this paper&#44; we aim to determine if potential predictors for invasive mechanical ventilation &#40;IMV&#41; are also determinants for mortality in COVID-19-associated acute respiratory distress syndrome &#40;C-ARDS&#41; while comparing the significance of variables in both cases&#46;</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0110">Patients and methods</span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0115">Selection and description of patients</span><p id="par0020" class="elsevierStylePara elsevierViewall">In our retrospective observational study&#44; we have collected and curated data from our electronic medical records &#40;EMR&#41; from March 3rd of 2020 through February 28th of 2021&#46; We selected patients admitted to our ICU at San Carlos Hospital &#40;HCSC&#41; in Madrid &#40;<a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>&#41; but were initially not mechanically ventilated&#46; The selection of patients considered just COVID-19 pneumonia patients&#44; incidental COVID-19 was excluded&#46; The age range for inclusion was restricted to individuals aged 18 years or older&#46;</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><p id="par0025" class="elsevierStylePara elsevierViewall">The database comprises hourly data points for each patient during the first five days&#46; Afterwards&#44; we utilized multi-stage machine learning algorithms to assess the most significant variables in predicting invasive mechanical ventilation &#40;IMV&#41; and <span class="elsevierStyleBold">ICU</span> mortality &#40;<a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 2</a>&#41;&#46; It is worth noting that 28-day mortality&#44; while frequently used in large studies like RECOVERY&#44; may not be a suitable outcome measure in COVID-19 patients due to the possibility of delayed mortality&#46;</p><elsevierMultimedia ident="fig0010"></elsevierMultimedia><p id="par0030" class="elsevierStylePara elsevierViewall">All data were registered in our electronic medical record &#40;ICCA Philips&#41;&#46; A total of 12&#44;163 longitudinal sets of hourly clinical and lab data were gathered&#46; Longitudinal sets are grouped in clustered events associated with patients&#46; Each entry contains demographics data&#44; first or second wave admission&#44; time elapsed from start of symptoms to O2 therapy and ICU admission&#44; APACHE II score&#44; monitoring&#44; blood gases and therapy-related data&#46; We discarded variables with more than 33&#37; of missing values for consistency&#46; We used mode imputation or mean imputation to complete missing values of the remaining variables&#46; <a class="elsevierStyleCrossRefs" href="#tbl0005">Tables 1 and 2</a> show the predictors that were finally used for the purposes of the study&#46;</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><p id="par0035" class="elsevierStylePara elsevierViewall">Data were anonymized&#44; excluding demographic or temporal information&#46; The study protocol was approved by the local ethics committee &#40;approval code 22&#47;007-E&#41;&#44; who waived the need for informed consent due to the retrospective non-interventional nature of the study&#46;</p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0120">Methods and techniques</span><p id="par0040" class="elsevierStylePara elsevierViewall">Data collected as described above were used to fit the model<a class="elsevierStyleCrossRef" href="#bib0060"><span class="elsevierStyleSup">12</span></a> following four steps for the whole process&#44; as shown in <a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 2</a>&#46; Considering that our data involve a concatenation of longitudinal data for each patient in different events&#44; it was necessary to identify correlations within the cluster when trying to build an accurate prediction model&#46;<a class="elsevierStyleCrossRef" href="#bib0050"><span class="elsevierStyleSup">10</span></a></p><p id="par0045" class="elsevierStylePara elsevierViewall">The different regression approaches to select potential predictors for IMV and ICU mortality risk tested were&#58; LASSO&#44;<a class="elsevierStyleCrossRef" href="#bib0065"><span class="elsevierStyleSup">13</span></a> Ridge&#44;<a class="elsevierStyleCrossRef" href="#bib0070"><span class="elsevierStyleSup">14</span></a> Elastic-net&#44;<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">15</span></a> Boruta<a class="elsevierStyleCrossRef" href="#bib0080"><span class="elsevierStyleSup">16</span></a> and R-Part&#46;<a class="elsevierStyleCrossRef" href="#bib0085"><span class="elsevierStyleSup">17</span></a> LASSO&#44; Ridge and Elastic-net perform an automatic predictor selection supported by L1 and L2 regularization terms<a class="elsevierStyleCrossRef" href="#bib0090"><span class="elsevierStyleSup">18</span></a> that minimizes the risk of overfitting&#44; reducing variance and reaching an attenuation effect over the correlation between features&#46; Boruta<a class="elsevierStyleCrossRef" href="#bib0095"><span class="elsevierStyleSup">19</span></a> is a feature selection model based on a Random Forest algorithm that selects all the risk predictors that are relevant for classification purposes defined as <span class="elsevierStyleItalic">all-relevant problems</span>&#46; R-Part<a class="elsevierStyleCrossRef" href="#bib0085"><span class="elsevierStyleSup">17</span></a> builds a classification model based on binary trees&#46; R-Part <span class="elsevierStyleItalic">varImp function</span><a class="elsevierStyleCrossRef" href="#bib0100"><span class="elsevierStyleSup">20</span></a> identifies the effect of model predictors based on the loss function mean squared error&#46; In any case&#44; potential predictors have been analyzed and confirmed or rejected based on clinical criteria&#46;</p><p id="par0050" class="elsevierStylePara elsevierViewall">After identifying the optimal set of potential predictors &#40;Figure 10&#8211;14 in Supplementary material&#41;&#44; clustering effects by patient and temporal distribution&#44; as well as cutoff points of the significant variables and their interactions were assessed with GLMM Trees&#46;<a class="elsevierStyleCrossRefs" href="#bib0045"><span class="elsevierStyleSup">9&#8211;11</span></a> To build these trees&#44; we took the entire dataset into account&#44; grouping data by patient and data charting time as random variables to fit the model&#46;<a class="elsevierStyleCrossRef" href="#bib0060"><span class="elsevierStyleSup">12</span></a> This fitting methodology avoids both over and underfitting effects that could impact the model&#8217;s performance&#46;<a class="elsevierStyleCrossRef" href="#bib0105"><span class="elsevierStyleSup">21</span></a> Models were implemented based on a 10-fold cross validation strategy using a four-depth-of-layers &#40;full&#44; 5&#44; 10 and 20&#41; strategy&#46; This means the fitting procedure was executed ten times per algorithm implementation&#46; It&#8217;s necessary to remark that the positive class for the invasive mechanical ventilation &#40;IMV&#41; variable refers to cases where IMV is required&#44; while the positive class for the <span class="elsevierStyleBold">ICU</span> mortality variable is related to cases where patients die&#46; It is worth mentioning that the focus of the study is on identifying independent variables and their associated thresholds with IMV and <span class="elsevierStyleBold">ICU</span> mortality&#44; without defining specific categories to predict&#46;</p><p id="par0055" class="elsevierStylePara elsevierViewall">We used a GLMM Tree to build conceptual models that explain the association between the potential predictors and the two outcome variables&#46; This algorithm accounts for data clusters and temporal characteristics of the dataset&#44; utilizing a mixed-effect strategy to combine the potential predictors that influence the outcome variables&#46; Additionally&#44; the algorithm provides a cut-off value for variables&#44; allowing for comparison with clinical experience&#46;</p><p id="par0060" class="elsevierStylePara elsevierViewall">GLMM Tree performance metrics were Area Under the Curve of Sensibility-Specificity &#40;AUC&#41;&#44; the Akaike Information Criterion &#40;AIC&#41; and the Bayesian Information Criterion &#40;BIC&#41;&#44;<a class="elsevierStyleCrossRef" href="#bib0110"><span class="elsevierStyleSup">22</span></a> as well as the deviance&#44; the likelihood statistical&#44;<a class="elsevierStyleCrossRef" href="#bib0115"><span class="elsevierStyleSup">23</span></a> and the sensitivity and specificity parameters&#46; All the regression and GLMM Tree models were fitted with the same subset of variables shown in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>&#46;</p><p id="par0065" class="elsevierStylePara elsevierViewall">We used both regressions and GLMM family trees to gain a wider understanding of potential predictors for IMV and <span class="elsevierStyleBold">ICU</span> mortality&#46; This combined approach offers more intuitive decision-making compared to black-box modeling strategies&#46; We assessed each predictor&#39;s effectiveness and used the same set of variables &#40;<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#41; to build an <span class="elsevierStyleBold">ICU</span> mortality model for the entire cohort&#46; The study&#39;s anonymized database and scripts can be found on the associated GitHub repository&#46;<a class="elsevierStyleCrossRef" href="#bib0120"><span class="elsevierStyleSup">24</span></a> The database will be published in PhysioNet<a class="elsevierStyleCrossRef" href="#bib0125"><span class="elsevierStyleSup">25</span></a> project in order to disseminate and exchange the anonymized clinical records looking for cooperative project replication&#46;</p></span></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0125">Results</span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0130">Patient characteristics</span><p id="par0070" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleBold">The complete cohort consisted of 280 patients who were included in the study&#46;</span> A total of 154 patients &#40;55 &#37;&#41; required IMV after ICU admission &#40;<a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>&#41;&#44; 65 of 80 patients &#40;81&#46;2 &#37;&#41; during the first and 89 of 200 patients &#40;44&#46;5 &#37;&#41; during the second wave&#46; ICU mortality of the whole cohort was 25&#46;7&#37; &#40;72 of 280 patients&#41;&#44; 33&#46;7&#37; &#40;27 of 80 patients&#41; during the first and 22&#46;5&#37; &#40;45 of 200 patients&#41; in the second wave&#46; <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a> shows IMV and <span class="elsevierStyleBold">ICU</span> mortality predictors for the whole patient&#8217;s cohort&#46; Mean registers per patient was 43&#46;4&#44; for a total of 12&#44;163 hourly registers in the whole database &#40;Figure 12 in complementary material&#41;&#46;</p></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0135">Significance of predictors</span><p id="par0075" class="elsevierStylePara elsevierViewall">R-Part classification achieves the best and most clinically plausible results in selecting the twelve most representative predictors for IMV and <span class="elsevierStyleBold">ICU</span> mortality from the whole group of available potential predictors &#40;<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#41;&#46; Concerning this subset of predictors&#44; the final selection is based on decreasing order of importance&#44; according to results reached by the loss function &#40;mean squared error&#41;&#44; scaled from 0 to 100 points&#46; Taking into account this premise&#44; the predictors are&#58; days from first symptoms to ICU admission &#40;100&#41;&#44; the APACHE II score &#40;92&#46;25&#41;&#44; the oxygenation index&#44; ROX index &#40;72&#46;46&#41;&#44; blood procalcitonin &#40;69&#46;59&#41;&#44; serum lactic dehydrogenase &#40;54&#46;45&#41;&#44; total serum bilirubin &#40;36&#46;54&#41;&#44; the COVID-19 wave &#40;31&#46;18&#41;&#44; the dose of corticosteroids administered during the first five days of admission &#40;30&#46;96&#41;&#44; lymphocyte count &#40;15&#46;57&#41;&#44; pH &#40;13&#46;29&#41;&#44; BMI &#40;12&#46;76&#41;&#44; C-reactive protein &#40;12&#46;74&#41;&#44; time to oxygen therapy &#40;12&#46;42&#41; and body temperature &#40;10&#46;82&#41;&#46;</p></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0140">Modeling performance</span><p id="par0080" class="elsevierStylePara elsevierViewall">In <a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a>&#44; the performance of the IMV model is presented&#46; The R-part predictors Regression-GLMTREE pair achieved the highest performance with an AUROC of 0&#46;87&#44; as shown in Figure 8 in the Supplementary material&#46; Additionally&#44; the <span class="elsevierStyleBold">ICU</span> mortality model performed well&#44; with an AUROC of 0&#46;88&#44; as demonstrated in Figure 9 in the Supplementary material&#46; The IMV likelihood ratio &#40;RV&#43; 3&#46;16&#44; RV- 0&#46;177&#41; suggests that the test result is moderately useful for identifying or discharge patients susceptible to being treated with IMV&#46; Related to the CI &#40;95&#37;&#41;&#44; the reached interval &#40;0&#46;918 and <span class="elsevierStyleBold">0&#46;928</span>&#41; suggests a high level of precision considering the sensitivity&#44; specificity&#44; and accuracy of the model&#46; Related to <span class="elsevierStyleBold">ICU</span> mortality&#44; the IMV likelihood ratio &#40;RV&#43; 5&#44;105&#44; RV&#8722; 0&#46;424&#41; and CI &#40;95&#37;&#41; interval &#40;<span class="elsevierStyleBold">0&#46;817</span> and 0&#46;833&#41;&#44; results are also moderately useful&#46; <a class="elsevierStyleCrossRef" href="#fig0015">Fig&#46; 3</a> illustrates the <span class="elsevierStyleBold">ICU</span> Mortality decision tree&#44; while Figure 7 in the Supplementary material presents the IMV decision tree&#46; The optimal cut-off point for the prediction model was determined based on the IMV and <span class="elsevierStyleBold">ICU</span> mortality AUC&#44; using Youden&#39;s Index&#44;<a class="elsevierStyleCrossRef" href="#bib0130"><span class="elsevierStyleSup">26</span></a> which identifies the point of maximum sum of sensitivity and specificity in ROC curve analysis&#46;</p><elsevierMultimedia ident="tbl0015"></elsevierMultimedia><elsevierMultimedia ident="fig0015"></elsevierMultimedia><p id="par0085" class="elsevierStylePara elsevierViewall">The trees in Figures 6 and 7 of the Supplementary material indicate that oxygenation status &#40;ROX index&#41; has the most significant influence on IMV&#44; with a threshold near 5&#46;2&#46; On the other hand&#44; <span class="elsevierStyleBold">ICU</span> mortality is mainly influenced by comorbidities &#40;APACHE II score&#41; and LDH&#44; as revealed by the same trees&#46;</p></span></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0145">Discussion</span><p id="par0090" class="elsevierStylePara elsevierViewall">The results of the present study include some highly relevant clinical results&#46; First&#44; the variable sets predicting IMV&#44; and <span class="elsevierStyleBold">ICU</span> mortality are different&#46; Whereas oxygenation variables are independent predictors of IMV&#44; <span class="elsevierStyleBold">ICU</span> mortality is associated with increased age and LDH and the presence of comorbidities&#46; The latter variables may be considered markers of two processes&#58; COVID-19-associated inflammation and ICU-acquired superinfection &#40;see Figure 4 in the Supplementary material&#41;&#46; Secondly&#44; the characteristics of pharmacological therapy&#44; including the administration of steroid drugs&#44; has little influence on both the need for IMV and <span class="elsevierStyleBold">ICU</span> mortality&#44; considering our results&#46; We included in the analysis 64 patients not receiving steroids and 216 receiving this treatment&#44; at the usual 6<span class="elsevierStyleHsp" style=""></span>mg dexamethasone or equivalent daily dose&#46; This is a remarkable finding&#44; because the effect of steroids on mortality identified in a previous trial<a class="elsevierStyleCrossRef" href="#bib0135"><span class="elsevierStyleSup">27</span></a> have influenced recommendations&#44; as well as clinical practice&#44; since its publication&#46; It may be speculated that the decision to include and randomize or not at the discretion of the attending physicians&#44; and based on undisclosed criteria&#44; rendered different results by selecting a study subset of COVID-19 cases with different characteristics&#46; In comparison&#44; no inclusion-exclusion criteria for selection process were applied in our &#8220;pragmatic&#8221; type of cohort&#46; Steroids were given to almost every patient unless a severe contraindication existed&#44; after the results of the RECOVERY trial were made available&#46;</p><p id="par0095" class="elsevierStylePara elsevierViewall">The present study applied a novel methodology &#40;logistic regression with regularization plus GLMM Tree mixed models&#41; to evaluate the relative importance of several variables as predictors of significant clinical events&#46; Using machine learning and a fine-grained longitudinal multifaceted database&#44; we have established relevant variable value thresholds to support clinical decisions&#46; Although the model would perform quite well as predictor for IMV and <span class="elsevierStyleBold">ICU</span> mortality&#44; with good positive predictive values&#44; it is important to emphasize that this is not a predictive model in the classical sense&#44; but an attempt to pinpoint the most important clinical events that represent turning points during the studied process &#40;in this case&#44; clinical management of patients not initially under IMV&#41;&#46; In this sense&#44; we should say that the inclusion of the likelihood ratio as an evaluation factor for comparing performance model was reach great results&#46; However&#44; following the premise of model explainability&#44; we believe it is important to take this element into account as a final selection factor for the set of predictors that best fit daily clinical practice&#46; This study demonstrates that predictor-ranking methodologies using self-explainable machine learning may support therapeutic decision-making using observational data&#44; when randomized clinical trials are unfeasible or unethical&#46;</p><p id="par0100" class="elsevierStylePara elsevierViewall">Regarding with the strengths of our study&#44; we would like to mention the quantity and quality of the data set&#46; Collected data have a high level of detail&#44; leveraging the power of strategically devised electronic health records &#40;EHR&#41;&#44; which include relevant information in a highly structured and recoverable format&#46; Every effort was made to configure our EHR to optimally gather all relevant information about COVID-19 patients&#46; Also&#44; our anonymized database is available in the repository along with the script we used for statistical analysis&#44; is highly detailed and has been extensively curated to reflect temporal evolution and to improve data quality as much as possible&#46; In any case&#44; the collection of variables from Electronic Health Records &#40;EHR&#41; may be biased&#44; affecting data quality&#46; Age and gender biases are possible&#44; as well as biases related to the selection and measurement of clinical variables&#46; These biases can lead to incomplete or skewed representations of certain population groups and may impact the validity and generalizability of research findings and clinical decision-making&#46; It is important to be aware of these biases to ensure proper interpretation and use of EHR data&#46;</p><p id="par0105" class="elsevierStylePara elsevierViewall">On the other hand&#44; the limitations of our study results relate mainly to its single-centered nature and require confirmation in a multicenter dataset to gain external validity&#46; Our methodology would be perfectly suited for a multicenter study&#44; including &#8220;center&#8221; as a random factor in the second &#40;GLMM Tree&#41; part of the process&#46; We suggest that future research applying this methodology could focus on designing clinical studies using observational data to answer relevant clinical questions without the logistic requirements of a randomized clinical trial or for hypothesis-generating purposes&#46; Furthermore&#44; when considering the limitations of using generalized linear mixed effects models &#40;GLMMs&#41; for modeling causation in critical care medicine research&#44; it is important to highlight the absence of explicit causality assumptions&#46; GLMMs primarily focus on association or correlation analysis&#44; lacking the ability to address the assumptions necessary for establishing causal relationships&#46; Specifically&#44; GLMMs do not provide frameworks for the identification of causal effects or account for unmeasured confounding variables&#44; which are crucial considerations in causal inference&#46; In contrast&#44; causal inference methods&#44; such as the potential outcomes framework&#44; explicitly address these assumptions&#44; offering a more comprehensive approach for investigating causality&#46; Therefore&#44; when establishing causal relationships between variables&#44; researchers should carefully consider the limitations of GLMMs and opt for causal inference methods&#44; which provide a more robust approach for investigating causality in critical care medicine research&#46;</p><p id="par0110" class="elsevierStylePara elsevierViewall">In conclusion&#44; different variables predict IMV and <span class="elsevierStyleBold">ICU</span> mortality in severe COVID-19 patients&#44; suggesting that the therapeutic decision of when to use IMV has little impact on <span class="elsevierStyleBold">ICU</span> mortality&#46; Our methodology is a valid option to assess therapeutic decisions using observational data when randomized clinical trials are not feasible or ethical&#46;</p></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0150">Author&#39;s contribution</span><p id="par0115" class="elsevierStylePara elsevierViewall">SM&#44; MA and AN conceived the presented idea&#46; <span class="elsevierStyleBold">SM and MA contributed equally as first authors&#46;</span> SM and MA developed the theory and performed the computations&#46; AN conducted an independent literature search to identify potentially relevant studies&#46; MS independently reviewed the search results to identify pertinent articles&#46; MS&#44; AN&#44; TF and VY contributed to the interpretation of the results&#46; SM&#44; MA&#44; AN&#44; MS&#44; FL and AC took the lead in writing the manuscript&#46; All authors provided critical feedback and helped shape the research&#44; analysis&#44; and manuscript&#46;</p></span><span id="sec0055" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0155">Funding</span><p id="par0120" class="elsevierStylePara elsevierViewall">This research did not receive any specific grant from funding agencies in the public&#44; commercial&#44; or not-for-profit sectors&#46;</p></span><span id="sec0060" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0160">Conflict of interest</span><p id="par0125" class="elsevierStylePara elsevierViewall">The authors declare that they have no conflict of interest&#46;</p></span></span>"
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          "identificador" => "sec0060"
          "titulo" => "Conflict of interest"
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        11 => array:2 [
          "identificador" => "xack720014"
          "titulo" => "Acknowledgements"
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        12 => array:1 [
          "titulo" => "References"
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      ]
    ]
    "pdfFichero" => "main.pdf"
    "tienePdf" => true
    "fechaRecibido" => "2023-03-03"
    "fechaAceptado" => "2023-06-22"
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        0 => array:4 [
          "clase" => "keyword"
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          "palabras" => array:6 [
            0 => "Acute respiratory distress syndrome"
            1 => "Invasive mechanical ventilation"
            2 => "COVID-19"
            3 => "Machine learning"
            4 => "Artificial intelligence"
            5 => "Predictors"
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          "palabras" => array:6 [
            0 => "S&#237;ndrome de distr&#233;s respiratorio agudo"
            1 => "Ventilaci&#243;n mec&#225;nica invasiva"
            2 => "COVID-19"
            3 => "Aprendizaje autom&#225;tico"
            4 => "Inteligencia artificilal"
            5 => "Predictores"
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    "resumen" => array:2 [
      "en" => array:3 [
        "titulo" => "Abstract"
        "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Objective</span><p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">To determine if potential predictors for invasive mechanical ventilation &#40;IMV&#41; are also determinants for mortality in COVID-19-associated acute respiratory distress syndrome &#40;C-ARDS&#41;&#46;</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Design</span><p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">Single center highly detailed longitudinal observational study&#46;</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Setting</span><p id="spar0065" class="elsevierStyleSimplePara elsevierViewall">Tertiary hospital ICU&#58; two first COVID-19 pandemic waves&#44; Madrid&#44; Spain&#46;</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Patients or participants</span><p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">&#58; 280 patients with C-ARDS&#44; not requiring IMV on admission&#46;</p></span> <span id="abst0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0030">Interventions</span><p id="spar0075" class="elsevierStyleSimplePara elsevierViewall">None&#46;</p></span> <span id="abst0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0035">Main variables of interest</span><p id="spar0080" class="elsevierStyleSimplePara elsevierViewall">&#58; Target&#58; endotracheal intubation and IMV&#44; mortality&#46;</p><p id="spar0085" class="elsevierStyleSimplePara elsevierViewall">Predictors&#58; demographics&#44; hourly evolution of oxygenation&#44; clinical data&#44; and laboratory results&#46;</p></span> <span id="abst0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">Results</span><p id="spar0090" class="elsevierStyleSimplePara elsevierViewall">The time between symptom onset and ICU admission&#44; the APACHE II score&#44; the ROX index&#44; and procalcitonin levels in blood were potential predictors related to both IMV and mortality&#46; The ROX index was the most significant predictor associated with IMV&#44; while APACHE II&#44; LDH&#44; and DaysSympICU were the most with mortality&#46;</p></span> <span id="abst0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Conclusions</span><p id="spar0095" class="elsevierStyleSimplePara elsevierViewall">According to the results of the analysis&#44; there are significant predictors linked with IMV and mortality in C-ARDS patients&#44; including the time between symptom onset and ICU admission&#44; the severity of the COVID-19 waves&#44; and several clinical and laboratory measures&#46; These findings may help clinicians to better identify patients at risk for IMV and mortality and improve their management&#46;</p></span>"
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            "titulo" => "Setting"
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            "titulo" => "Patients or participants"
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        "titulo" => "Resumen"
        "resumen" => "<span id="abst0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0055">Objetivo</span><p id="spar0100" class="elsevierStyleSimplePara elsevierViewall">Determinar si las variables cl&#237;nicas independientes que condicionan el inicio de ventilaci&#243;n mec&#225;nica invasiva &#40;VMI&#41; son los mismos que condicionan la mortalidad en el s&#237;ndrome de distr&#233;s respiratorio agudo asociado con COVID-19 &#40;C-SDRA&#41;&#46;</p></span> <span id="abst0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0060">Dise&#241;o</span><p id="spar0105" class="elsevierStyleSimplePara elsevierViewall">Estudio observacional longitudinal en un solo centro&#46;</p></span> <span id="abst0055" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0065">&#193;mbito</span><p id="spar0110" class="elsevierStyleSimplePara elsevierViewall">UCI&#44; hospital terciario&#58; primeras dos olas de COVID-19 en Madrid&#44; Espa&#241;a&#46;</p></span> <span id="abst0060" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Pacientes o participantes</span><p id="spar0115" class="elsevierStyleSimplePara elsevierViewall">280 pacientes con C-SDRA que no requieren VMI al ingreso en UCI&#46;</p></span> <span id="abst0065" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">Intervenciones</span><p id="spar0120" class="elsevierStyleSimplePara elsevierViewall">Ninguna&#46;</p></span> <span id="abst0070" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Principales variables de inter&#233;s</span><p id="spar0125" class="elsevierStyleSimplePara elsevierViewall">Objetivo&#58; VMI y Mortalidad&#46;</p><p id="spar0130" class="elsevierStyleSimplePara elsevierViewall">Predictores&#58; demogr&#225;ficos&#44; variables cl&#237;nicas&#44; resultados de laboratorio y evoluci&#243;n de la oxigenaci&#243;n&#46;</p></span> <span id="abst0075" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Resultados</span><p id="spar0135" class="elsevierStyleSimplePara elsevierViewall">El tiempo entre el inicio de los s&#237;ntomas y el ingreso en la UCI&#44; la puntuaci&#243;n APACHE II&#44; el &#237;ndice ROX y los niveles de procalcitonina en sangre eran posibles predictores relacionados tanto con la IMV como con la mortalidad&#46; El &#237;ndice ROX fue el predictor m&#225;s significativo asociada con la IMV&#44; mientras que APACHE II&#44; LDH y DaysSympICU fueron los m&#225;s influyentes en la mortalidad&#46;</p></span> <span id="abst0080" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Conclusiones</span><p id="spar0140" class="elsevierStyleSimplePara elsevierViewall">Seg&#250;n los resultados obtenidos se identifican predictores significativos vinculados con la VMI y mortalidad en pacientes con C-ARDS&#44; incluido el tiempo entre el inicio de los s&#237;ntomas y el ingreso en la UCI&#44; la gravedad de las olas de COVID-19 y varias medidas cl&#237;nicas y de laboratorio&#46; Estos hallazgos pueden ayudar a los m&#233;dicos a identificar mejor a los pacientes en riesgo de IMV y mortalidad y mejorar su manejo&#46;</p></span>"
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            "titulo" => "Dise&#241;o"
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            "titulo" => "&#193;mbito"
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            "titulo" => "Pacientes o participantes"
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            "titulo" => "Intervenciones"
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            "titulo" => "Principales variables de inter&#233;s"
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            "apendice" => "<p id="par0140" class="elsevierStylePara elsevierViewall">The following is Supplementary data to this article&#58;<elsevierMultimedia ident="upi0005"></elsevierMultimedia></p>"
            "etiqueta" => "Appendix A"
            "titulo" => "Supplementary data"
            "identificador" => "sec0070"
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      0 => array:8 [
        "identificador" => "fig0005"
        "etiqueta" => "Figure 1"
        "tipo" => "MULTIMEDIAFIGURA"
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          "en" => "<p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">COVID-19 patients admitted during first and second pandemic waves&#46; The cohort comprises 280 severe COVID-19 patients admitted to the ICU Department at HCSC in Madrid&#44; Spain&#44; between March 3&#44; 2020&#44; and February 28&#44; 2021&#46; During this time period&#44; SARS-COV-2 wild-type and subsequently alpha variants were prevalent in Spain&#46; Over the study time period 4229 covid-19 patients were admitted to HCSC&#44; 405 of whom required ICU admission &#40;first wave&#58; 153&#44; second wave&#58; 252 patients&#41;&#46;</p>"
        ]
      ]
      1 => array:8 [
        "identificador" => "fig0010"
        "etiqueta" => "Figure 2"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
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        "figura" => array:1 [
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        "detalles" => array:1 [
          0 => array:3 [
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        "descripcion" => array:1 [
          "en" => "<p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">Methodology for fitting the machine learning algorithms&#46; In a previous stage&#44; Figure 5 in Supplementary material shows the complete workflow&#44; from the cohort selection according to clinical needs to the implementation of the algorithms that have been included in the explanation&#46; The first step involves the cohort selection as well as the initial group of variables considered in this study&#44; The second step consists in the implementation of a statistical study of each variable&#46; This step also involves correlation &#40;Figure 6 in Supplementary material&#41; imputation and transformations procedures in order to dispose of the most accurate data in the following steps&#46; The third step analyzed the most significant predictors based on five <span class="elsevierStyleBold">Machine</span> Learning &#40;ML&#41; techniques linked with regression analysis based on 10-fold cross-validation regressions&#46; The fourth and last step identifies the behavior of each predictor attending to different proposes&#46; The first one is related to mechanical ventilation needs attending to different settings in the Generalized Linear Mixed Model &#40;GLMM&#41; Tree &#40;depth of layers&#41; looking for the best balance between performance &#40;Akaike Information Criterion &#40;AIC&#41;&#44; Bayesian information criterion &#40;BIC&#41;&#44; Area Under the Roc Curve &#40;ROC&#41; and more parameters within the table III&#41; and explainability of the model&#46; The second one is related to the most representative mortality predictors but following the same balance objective&#46;</p>"
        ]
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        "identificador" => "fig0015"
        "etiqueta" => "Figure 3"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
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        "figura" => array:1 [
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            "imagen" => "gr3.jpeg"
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        "detalles" => array:1 [
          0 => array:3 [
            "identificador" => "at0085"
            "detalle" => "Figure "
            "rol" => "short"
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        "descripcion" => array:1 [
          "en" => "<p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">ICU Mortality Tree Predictors&#46; The predictors appear in different branches attending to their significance in the predictive model&#46; Values in bold letters represent the registries per branch&#46; Values in red bold letters represent the percentage of registries with positive outcome&#46; The variable named as &#8220;DAYS&#95;SIMPTONS&#95;ADMISION&#8221; is related with the number of days from first symptoms to ICU admission&#46; The variable &#8220;linf&#95;total&#8221;&#44; is related to lymphocyte count per mm3&#46; The variable named as &#8220;dosis&#95;equiv&#95;mpred&#95;5d&#8221; is related with the corticosteroid dose&#44; during the first five days of admission &#40;mg of equivalent methylprednisolone dose&#41;&#46; The variable named as &#8220;bbTot&#8221; is related with the total levels of bilirubin in blood&#46; The variable names as &#8220;ldh&#8221; is related to the lactate dehydrogenase serum level&#46; The variable DAYS&#95;UNTIL&#95;O2 is related to the number of days until the patient requires O2&#46;</p>"
        ]
      ]
      3 => array:8 [
        "identificador" => "tbl0005"
        "etiqueta" => "Table 1"
        "tipo" => "MULTIMEDIATABLA"
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          "leyenda" => "<p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">This group of predictors will be applied in the selection procedure linked with the five regression algorithms&#58; Ridge&#44; LASSO&#44; Elastic&#44; Boruta and R-part Based on the reached results&#44; the group of predictors are going to be reduced attending to its behavior related to IMV needs&#46; Figures 7&#8211;11 &#40;Supplementary material&#41; shows the results from each regression procedure where R-Part was finally selected due to its good balance between model performance and explicability of results&#46;</p><p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">Data updated June 22&#44; 2023&#46;</p>"
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                  <table border="0" frame="\n
                  \t\t\t\t\tvoid\n
                  \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td-with-role" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t ; entry_with_role_colgroup " colspan="6" align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Dataset clinical and biochemical characteristics</th></tr><tr title="table-row"><th class="td-with-role" title="\n
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                  \t\t\t\t ; entry_with_role_colgroup " colspan="6" align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">Invasive Mechanical Ventilation &#40;IMV&#41;</span></th></tr><tr title="table-row"><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col">Variable&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" scope="col"><span class="elsevierStyleItalic">N</span>&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col">Overall&#44; <span class="elsevierStyleItalic">N</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>12&#44;163<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">a</span></a>&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
                  \t\t\t\t ; entry_with_role_colgroup " colspan="2" align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Invasive Mechanical Ventilation &#40;IMV&#41;</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
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                  \t\t\t\t" scope="col">p-value<a class="elsevierStyleCrossRef" href="#tblfn0010"><span class="elsevierStyleSup">b</span></a>&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">No&#44; <span class="elsevierStyleItalic">N</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>9093<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">a</span></a>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Yes&#44; <span class="elsevierStyleItalic">N</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>3070<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">a</span></a>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t">Age&#44; years&#44; Median &#40;Q1-Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">59 &#40;51&#8211;68&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">58 &#40;50&#8211;67&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">63 &#40;54&#8211;70&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">Gender&#44; n &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">Ethnicity&#44; n &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Spanish&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">Wave&#44; n &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Second&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Body mass index&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;70&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Heart rate&#44; median bpm &#40;IQR&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">73 &#40;65&#8211;84&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">73 &#40;64&#8211;83&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">76 &#40;67&#8211;87&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Temperature in &#186;C&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">36&#46;80 &#40;36&#46;50&#8211;37&#46;10&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">36&#46;73 &#40;36&#46;44&#8211;37&#46;02&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">36&#46;97 &#40;36&#46;64&#8211;37&#46;37&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Arterial pressure in mmHg&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">87 &#40;79&#8211;95&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">87 &#40;80&#8211;95&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">87 &#40;78&#8211;95&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Lactate in mEq&#47;l&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1&#46;42 &#40;1&#46;20&#8211;1&#46;70&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1&#46;42 &#40;1&#46;14&#8211;1&#46;65&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1&#46;50 &#40;1&#46;33&#8211;1&#46;80&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Procalcitonin&#44; ng&#47;mL Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;13 &#40;0&#46;08 &#8211; 0&#46;23&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;13 &#40;0&#46;07 &#8211; 0&#46;20&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;14 &#40;0&#46;13 &#8211; 0&#46;35&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Eosinophile count per cubic mm&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">4 &#40;0&#8211;20&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">4 &#40;0&#8211;22&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">4 &#40;0&#8211;13&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;011&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">C reactive protein&#44; mg&#47;L Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">8 &#40;6&#8211;11&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">8 &#40;4&#8211;10&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">8 &#40;8&#8211;15&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Alkaline phosphatase U&#47;L&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">82 &#40;68&#8211;101&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">82 &#40;65&#8211;99&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">82 &#40;76&#8211;104&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;006&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Total bilirubin mg&#47;dL&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;53 &#40;0&#46;44 &#8211; 0&#46;62&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;53 &#40;0&#46;42 &#8211; 0&#46;59&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;53 &#40;0&#46;51 &#8211; 0&#46;71&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Oxygenation index &#40;ROX Index&#41;&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">5&#46;93 &#40;4&#46;52&#8211;7&#46;92&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">6&#46;18 &#40;5&#46;22&#8211;8&#46;67&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">4&#46;46 &#40;3&#46;62&#8211;5&#46;93&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Creatinine&#44; mg&#47;dL Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;67 &#40;0&#46;59&#8211;0&#46;78&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;67 &#40;0&#46;58&#8211;0&#46;77&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;67 &#40;0&#46;65&#8211;0&#46;82&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Leukocyte count per mm3&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">8925 &#40;7160&#8211;10&#44;804&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">8925 &#40;6857&#8211;10&#44;548&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">8925 &#40;8400&#8211;11&#44;478&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Hemoglobin g&#47;l&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">13&#46;16 &#40;12&#46;28&#8211;13&#46;96&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">13&#46;16 &#40;12&#46;20&#8211;13&#46;93&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">13&#46;16 &#40;12&#46;63&#8211;14&#46;03&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Amylase U&#47;L&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">63 &#40;50&#8211;79&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">63 &#40;51&#8211;84&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">63 &#40;48&#8211;64&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Lactate dehydrogenase&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">882 &#40;749 &#8211; 1038&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">882 &#40;682&#8211;964&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">939 &#40;882&#8211;1172&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Lymphocyte count per mm3&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">829 &#40;638&#8211;1049&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">829 &#40;657&#8211;1148&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">829 &#40;570&#8211;871&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">AST &#40;Aspartate Aminotransferase&#41; U&#47;L&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">45 &#40;34&#8211;60&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">45 &#40;34&#8211;63&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">45 &#40;33&#8211;54&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Hours from ICU admission to this register&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">31 &#40;14&#8211;50&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">33 &#40;16&#8211;51&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">23 &#40;9&#8211;45&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">APACHE &#40;Acute Physiology and Chronic Health Evaluation&#41; II&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">13&#46;0 &#40;10&#46;0&#8211;17&#46;0&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#46;0 &#40;10&#46;0&#8211;16&#46;0&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">15&#46;0 &#40;13&#46;0&#8211;17&#46;0&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Days from first symptoms to O2 therapy&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">7&#46;00 &#40;6&#46;00&#8211;8&#46;00&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">7&#46;00 &#40;6&#46;00&#8211;8&#46;00&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">7&#46;00 &#40;6&#46;00&#8211;8&#46;00&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;008&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Days from first symptoms to ICU admission&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">9&#46;0 &#40;8&#46;0&#8211;11&#46;0&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">9&#46;0 &#40;8&#46;0&#8211;11&#46;0&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">9&#46;0 &#40;7&#46;0&#8211;13&#46;0&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Arterial pH&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">7&#46;43 &#40;7&#46;41&#8211;7&#46;45&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">7&#46;43 &#40;7&#46;41&#8211;7&#46;46&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">7&#46;43 &#40;7&#46;39&#8211;7&#46;44&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Arterial pCO2&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">38&#46;1 &#40;35&#46;7&#8211;41&#46;0&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">38&#46;1 &#40;35&#46;6&#8211;40&#46;7&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">38&#46;4 &#40;36&#46;0&#8211;42&#46;4&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Type of blood sample&#44; n &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Arterial&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">696 &#40;5&#46;7&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">496 &#40;5&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">200 &#40;6&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>BLDO &#40;Capillary blood gas analysis&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">5 &#40;&#60;0&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">5 &#40;&#60;0&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0 &#40;0&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Arterial&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">91 &#40;0&#46;7&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">27 &#40;0&#46;3&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">64 &#40;2&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Mixed&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">28 &#40;0&#46;2&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">28 &#40;0&#46;3&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0 &#40;0&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Venous&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t">1405 &#40;12&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">931 &#40;10&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">474 &#40;15&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Venous&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t">9845 &#40;81&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">7529 &#40;83&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">2316 &#40;75&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Mixed venous&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">93 &#40;0&#46;8&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">77 &#40;0&#46;8&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">16 &#40;0&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Blood gas sat&#46; O2&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">85 &#40;75&#8211;91&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">85 &#40;77&#8211;91&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">84 &#40;72&#8211;89&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Corticosteroid dose&#44; first 5 days of admission &#40;mg of equivalent methylprednisolone dose&#41;&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">36 &#40;30&#8211;60&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">36 &#40;30&#8211;60&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">36 &#40;30&#8211;78&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">0&#46;32&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Melatonin dose in mg&#47;day&#44; n &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">D dimer&#44; ng&#47;mL Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Dataset variables statistical characteristics</th></tr><tr title="table-row"><th class="td-with-role" title="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">ICU mortality</span></th></tr><tr title="table-row"><th class="td" title="\n
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                  \t\t\t\t" scope="col">Variable&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col"><span class="elsevierStyleItalic">N</span>&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col">Overall&#44; <span class="elsevierStyleItalic">N</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>12&#44;163<a class="elsevierStyleCrossRef" href="#tblfn0015"><span class="elsevierStyleSup">a</span></a>&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Alive&#44; <span class="elsevierStyleItalic">N</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>9777<a class="elsevierStyleCrossRef" href="#tblfn0015"><span class="elsevierStyleSup">a</span></a>&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">Days elapsed from first symptoms to ICU admission&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">9&#46;0 &#40;8&#46;0&#8211;11&#46;0&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">9&#46;0 &#40;7&#46;0&#8211;13&#46;0&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t">APACHE &#40;Acute Physiology and Chronic Health Evaluation&#41; II&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t">12&#46;0 &#40;10&#46;0&#8211;16&#46;0&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">15&#46;0 &#40;13&#46;0&#8211;17&#46;0&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t">Corticosteroids administered during the first 5d of admission as mg of equivalent methylprednisolone dose&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">36 &#40;30&#8211;60&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">36 &#40;30&#8211;60&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">36 &#40;30&#8211;80&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Oxygenation index&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">5&#46;93 &#40;4&#46;52&#8211;7&#46;92&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">5&#46;93 &#40;4&#46;95&#8211;8&#46;42&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">4&#46;58 &#40;3&#46;63&#8211;5&#46;93&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Serum Lactate dehydrogenase&#44; U&#47;L Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">882 &#40;749 &#8211; 1038&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">882 &#40;695&#8211;964&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1026 &#40;882&#8211;1279&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Body mass index&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">27&#46;8 &#40;26&#46;0&#8211;31&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">27&#46;8 &#40;26&#46;0&#8211;31&#46;8&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">27&#46;8 &#40;26&#46;0&#8211;29&#46;4&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Temperature in &#186;C&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">36&#46;80 &#40;36&#46;50&#8211;37&#46;10&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">36&#46;78 &#40;36&#46;50&#8211;37&#46;10&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">36&#46;86 &#40;36&#46;50&#8211;37&#46;20&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Days elapsed from first symptoms to O2 therapy&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">7&#46;00 &#40;6&#46;00&#8211;8&#46;00&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">7&#46;00 &#40;6&#46;00&#8211;8&#46;00&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">7&#46;00 &#40;6&#46;00&#8211;7&#46;00&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;073&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Total bilirubin mg&#47;dL&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;53 &#40;0&#46;44&#8211;0&#46;62&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;53 &#40;0&#46;42&#8211;0&#46;60&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;53 &#40;0&#46;51&#8211;0&#46;68&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Wave&#44; n &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">First&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1490 &#40;12&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1031 &#40;11&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">459 &#40;19&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Second&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">10&#44;673 &#40;88&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">8746 &#40;89&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1927 &#40;81&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Lymphocyte count per mm3&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">829 &#40;638&#8211;1049&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">829 &#40;667&#8211;1120&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">800 &#40;499&#8211;886&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Arterial pH&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">7&#46;43 &#40;7&#46;41&#8211;7&#46;45&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">7&#46;43 &#40;7&#46;41&#8211;7&#46;46&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">7&#46;43 &#40;7&#46;38&#8211;7&#46;45&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t">C reactive protein levels mg&#47;L&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">8 &#40;6&#8211;11&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">8 &#40;5&#8211;10&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">8 &#40;8&#8211;14&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t">Hours from ICU admission to this register&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">31 &#40;14&#8211;50&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">31 &#40;14&#8211;51&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">28 &#40;11&#8211;47&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
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          "leyenda" => "<p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">The Akaike Information Criterion &#40;AIC&#41; reports the information score of the whole models&#58; the smaller the <span class="elsevierStyleBold"><span class="elsevierStyleItalic">AIC value</span></span>&#44; the better the model fit&#46; AIC is calculated from the number of independent variables to build the model and the maximum likelihood estimate of the model &#40;how well the model reproduces the data&#41;&#46; The best-fit model according to AIC is the one that explains the greatest amount of variation using the fewest possible independent variables&#46; <span class="elsevierStyleBold"><span class="elsevierStyleItalic">Bayesian information criterion &#40;BIC&#41;</span></span> is another criteria for model selection that measures the trade-off between model fit and complexity of the model&#46; A lower AIC or BIC value indicates a better fit&#46; The <span class="elsevierStyleBold"><span class="elsevierStyleItalic">log-likelihood &#40;log Lik&#41;</span></span> value of a regression model is a way to measure the goodness of fit for a model&#46; The higher the value of the log-likelihood&#44; the better a model fits a dataset&#46; <span class="elsevierStyleBold"><span class="elsevierStyleItalic">Deviance</span></span> is a goodness-of-fit metric for statistical models&#44; particularly used for GLMs&#46; It is defined as the difference between the Saturated and Proposed Models and can be thought as how much variation in the data does our Proposed Model account for&#46; Therefore&#44; the lower the deviance&#44; the better the model&#46; <span class="elsevierStyleBold"><span class="elsevierStyleItalic">Sensitivity</span></span> is the metric that evaluates a model&#39;s ability to predict true positives of each available category&#46; Specificity is the metric that evaluates a model&#39;s ability to predict true negatives of each available category&#46; The higher value of sensitivity would mean higher value of true positive and lower value of false negative&#46; For the healthcare domain&#44; models with high sensitivity will be desired&#46; <span class="elsevierStyleBold"><span class="elsevierStyleItalic">Specificity</span></span> is the metric that evaluates a model&#39;s ability to predict true negatives of each available category&#46; These metrics apply to any categorical model&#46; Specificity is defined as the proportion of actual negatives&#44; which got predicted as the negative &#40;or true negative&#41;&#46; Specificity is a measure of the proportion of people not suffering from the disease who got predicted correctly as the ones who are not suffering from the disease&#46; In other words&#44; the person who is healthy actually got predicted as healthy&#46; The likelihood ratio is often used in statistical hypothesis testing and model selection to compare the fit of different models to the observed data&#46; It is also commonly used in medical diagnostic testing to evaluate the diagnostic accuracy of a particular test or combination of tests&#46; LR&#43; &#40;likelihood ratio positive&#41; is a statistical measure used to evaluate the diagnostic accuracy of a medical test&#46; It is the ratio of the probability of a positive test result given the presence of the disease to the probability of a positive test result given the absence of the disease&#46; In other words&#44; the LR&#43; compares the likelihood of a positive test result in patients with the disease versus the likelihood of a positive test result in patients without the disease&#46; In our case&#44; a high LR&#43; indicates that the test is more accurate at correctly identifying patients how could need IMV&#44; while a low LR&#43; suggests that the test is not providing strong evidence for IMV&#46; By the way&#44; LR&#8722; compares the likelihood of a negative test result in patients with the disease versus the likelihood of a negative test result in patients without the disease&#46; A low LR- indicates that the test is more accurate at correctly identifying patients without the need of IMV&#44; while a high LR- suggests that the test is not providing strong evidence for the absence of IMV&#46; The LR&#43; and LR&#8722; are often used in conjunction with other measures of diagnostic accuracy&#44; such as sensitivity&#44; specificity to assess the overall performance of a medical test&#46; It can help clinicians and researchers determine the optimal use of a particular test in diagnosing a disease or condition&#46; CI stands for &#34;confidence interval&#46;&#34; A confidence interval CI is a range of values that is likely to contain the true value of a population parameter &#40;such as a mean or a proportion&#41;&#44; with a certain degree of confidence &#40;usually expressed as a percentage&#44; such as 95&#37; or 99&#37;&#41;&#46; A narrower interval indicates greater precision&#44; while a wider interval indicates greater uncertainty&#46; The exact range of a &#34;good&#34; CI can vary depending on the context and the specific research question&#44; but typically&#44; a narrower interval is preferred as it provides a more precise estimate&#46; In the case of the area under the receiver operating characteristic curve &#40;AUROC&#41;&#44; which is commonly used in binary classification problems&#44; a CI that includes a value of 0&#46;5 &#40;indicating no discrimination between the two groups&#41; is generally considered to be uninformative&#46; On the other hand&#44; a CI that does not include 0&#46;5 and has a range of&#44; for example&#44; 0&#46;7&#8211;0&#46;8&#44; may be considered good&#44; indicating that the model has reasonably good discriminative ability&#46; However&#44; the interpretation of the AUROC and its associated CI should always be considered in the context of the specific research question and the particular field of study&#46;</p>"
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">GLMM &#40;Generalized Linear Mixed Model&#41; trees results</th></tr><tr title="table-row"><th class="td-with-role" title="\n
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                  \t\t\t\t ; entry_with_role_colgroup " colspan="12" align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">Mechanical ventilation</span></th></tr><tr title="table-row"><th class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Regressions&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">N&#186; Predictors&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">C&#46;I&#40;95&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">AIC&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">BIC&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Deviance&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Log Lik&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Sensitivity&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Specificity&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">LR&#43;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">LR&#8722;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t">Ridge criteria&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">25&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">0&#46;852&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t"><span class="elsevierStyleBold">0&#46;859&#8722;0&#46;872</span>&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">9263&#46;82&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">9493&#46;41&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;856&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">0&#46;689&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">2&#46;75&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">0&#46;206&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">LASSO criteria&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">22&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">0&#46;852&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleBold">0&#46;847&#8211;0&#46;862</span>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t">9263&#46;82&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">94&#44;939&#46;41&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">9201&#46;82&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">&#8722;4&#46;600&#46;91&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;856&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t"><span class="elsevierStyleBold">0&#46;852</span>&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">5&#46;78&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;166&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
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Original article
Predictors of mechanical ventilation and mortality in critically ill patients with COVID-19 pneumonia
Predictores de ventilación mecánica y mortalidad en pacientes críticos con neumonía por COVID-19
Sergio Muñoz Lezcanoa,1,
Corresponding author
smunozle@gmail.com

Corresponding author.
, Miguel Ángel Armengol de la Hozb,1, Alberto Corbic, Fernando Lópezd, Miguel Sánchez Garcíae, Antonio Nuñez Reizf, Tomás Fariña Gonzálezg, Viktor Yordanov Zlatkova
a PhD Student of the Program in Computer Science, Universidad Internacional de La Rioja (UNIR), Avenida de La Paz, 137, 26006 Logroño, La Rioja, Spain
b Big Data Department, PMC-FPS, Consejería de Salud y Consumo, Junta de Andalucía, Spain
c Research Institute for Innovation & Technology in Education (iTED), Universidad Internacional de La Rioja (UNIR), Avenida de La Paz, 137, 26006 Logroño, La Rioja, Spain
d Mathematical Analysis and Applied Mathematics Department, Faculty of Mathematics. Universidad Complutense de Madrid, Spain
e Critical Care Department, Hospital Clínico San Carlos, Martín Lagos s/n, 28040 Madrid, Spain
f Critical Care Department, Hospital Universitario Clínico San Carlos, Martín Lagos s/n, 28040 Madrid, Spain
g Critical Care Department, Hospital Universitario Infanta Sofía, Spain
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Several recent studies have addressed the subject&#44;<a class="elsevierStyleCrossRef" href="#bib0020"><span class="elsevierStyleSup">4</span></a> although bias cannot be excluded in observational non-randomized trials&#46; A retrospective study suggested that early intubation and IMV is associated with favorable outcomes but included only intubated patients instead of the whole population at risk&#46;</p><p id="par0015" class="elsevierStylePara elsevierViewall">Previous studies have identified covid-19 progression predictors including age&#44; comorbidities&#44; renal function&#44; or immunodeficiency<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a> using traditional statistical approaches&#44; where collinearity of data cannot be ruled out&#46; Artificial intelligence &#40;AI&#41; is currently being used for COVID-19 risk stratification&#44;<a class="elsevierStyleCrossRef" href="#bib0030"><span class="elsevierStyleSup">6</span></a> studying multiple clinical features to increase effectiveness and efficiency in diagnosis&#44; treatment&#44; and prognosis&#46; Self-explainable Machine learning &#40;ML&#41; techniques can help with risk factor selection through ranking methodologies&#46;<a class="elsevierStyleCrossRef" href="#bib0035"><span class="elsevierStyleSup">7</span></a> In this context&#44; the utilization of artificial intelligence &#40;AI&#41; holds potential in facilitating the development of a conceptual model aimed at comparing the significance of variables&#46; This can be achieved by employing regularization models<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">8</span></a> to enhance predictor selection&#44; followed by the implementation of the Generalized Linear Mixed-effects Model &#40;GLMM&#41;<a class="elsevierStyleCrossRefs" href="#bib0045"><span class="elsevierStyleSup">9&#8211;11</span></a> to construct the said conceptual model&#46; Such an approach becomes particularly relevant when assessing and comparing outcomes across different AI models&#44; enabling a comprehensive evaluation of variable significance&#46; This is a novel methodology&#44; leveraging modern machine learning techniques to provide rigorous and applicable insight into relevant clinical questions when randomized clinical trials are not feasible&#46; From here on&#44; in this paper&#44; we aim to determine if potential predictors for invasive mechanical ventilation &#40;IMV&#41; are also determinants for mortality in COVID-19-associated acute respiratory distress syndrome &#40;C-ARDS&#41; while comparing the significance of variables in both cases&#46;</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0110">Patients and methods</span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0115">Selection and description of patients</span><p id="par0020" class="elsevierStylePara elsevierViewall">In our retrospective observational study&#44; we have collected and curated data from our electronic medical records &#40;EMR&#41; from March 3rd of 2020 through February 28th of 2021&#46; We selected patients admitted to our ICU at San Carlos Hospital &#40;HCSC&#41; in Madrid &#40;<a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>&#41; but were initially not mechanically ventilated&#46; The selection of patients considered just COVID-19 pneumonia patients&#44; incidental COVID-19 was excluded&#46; The age range for inclusion was restricted to individuals aged 18 years or older&#46;</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><p id="par0025" class="elsevierStylePara elsevierViewall">The database comprises hourly data points for each patient during the first five days&#46; Afterwards&#44; we utilized multi-stage machine learning algorithms to assess the most significant variables in predicting invasive mechanical ventilation &#40;IMV&#41; and <span class="elsevierStyleBold">ICU</span> mortality &#40;<a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 2</a>&#41;&#46; It is worth noting that 28-day mortality&#44; while frequently used in large studies like RECOVERY&#44; may not be a suitable outcome measure in COVID-19 patients due to the possibility of delayed mortality&#46;</p><elsevierMultimedia ident="fig0010"></elsevierMultimedia><p id="par0030" class="elsevierStylePara elsevierViewall">All data were registered in our electronic medical record &#40;ICCA Philips&#41;&#46; A total of 12&#44;163 longitudinal sets of hourly clinical and lab data were gathered&#46; Longitudinal sets are grouped in clustered events associated with patients&#46; Each entry contains demographics data&#44; first or second wave admission&#44; time elapsed from start of symptoms to O2 therapy and ICU admission&#44; APACHE II score&#44; monitoring&#44; blood gases and therapy-related data&#46; We discarded variables with more than 33&#37; of missing values for consistency&#46; We used mode imputation or mean imputation to complete missing values of the remaining variables&#46; <a class="elsevierStyleCrossRefs" href="#tbl0005">Tables 1 and 2</a> show the predictors that were finally used for the purposes of the study&#46;</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><p id="par0035" class="elsevierStylePara elsevierViewall">Data were anonymized&#44; excluding demographic or temporal information&#46; The study protocol was approved by the local ethics committee &#40;approval code 22&#47;007-E&#41;&#44; who waived the need for informed consent due to the retrospective non-interventional nature of the study&#46;</p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0120">Methods and techniques</span><p id="par0040" class="elsevierStylePara elsevierViewall">Data collected as described above were used to fit the model<a class="elsevierStyleCrossRef" href="#bib0060"><span class="elsevierStyleSup">12</span></a> following four steps for the whole process&#44; as shown in <a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 2</a>&#46; Considering that our data involve a concatenation of longitudinal data for each patient in different events&#44; it was necessary to identify correlations within the cluster when trying to build an accurate prediction model&#46;<a class="elsevierStyleCrossRef" href="#bib0050"><span class="elsevierStyleSup">10</span></a></p><p id="par0045" class="elsevierStylePara elsevierViewall">The different regression approaches to select potential predictors for IMV and ICU mortality risk tested were&#58; LASSO&#44;<a class="elsevierStyleCrossRef" href="#bib0065"><span class="elsevierStyleSup">13</span></a> Ridge&#44;<a class="elsevierStyleCrossRef" href="#bib0070"><span class="elsevierStyleSup">14</span></a> Elastic-net&#44;<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">15</span></a> Boruta<a class="elsevierStyleCrossRef" href="#bib0080"><span class="elsevierStyleSup">16</span></a> and R-Part&#46;<a class="elsevierStyleCrossRef" href="#bib0085"><span class="elsevierStyleSup">17</span></a> LASSO&#44; Ridge and Elastic-net perform an automatic predictor selection supported by L1 and L2 regularization terms<a class="elsevierStyleCrossRef" href="#bib0090"><span class="elsevierStyleSup">18</span></a> that minimizes the risk of overfitting&#44; reducing variance and reaching an attenuation effect over the correlation between features&#46; Boruta<a class="elsevierStyleCrossRef" href="#bib0095"><span class="elsevierStyleSup">19</span></a> is a feature selection model based on a Random Forest algorithm that selects all the risk predictors that are relevant for classification purposes defined as <span class="elsevierStyleItalic">all-relevant problems</span>&#46; R-Part<a class="elsevierStyleCrossRef" href="#bib0085"><span class="elsevierStyleSup">17</span></a> builds a classification model based on binary trees&#46; R-Part <span class="elsevierStyleItalic">varImp function</span><a class="elsevierStyleCrossRef" href="#bib0100"><span class="elsevierStyleSup">20</span></a> identifies the effect of model predictors based on the loss function mean squared error&#46; In any case&#44; potential predictors have been analyzed and confirmed or rejected based on clinical criteria&#46;</p><p id="par0050" class="elsevierStylePara elsevierViewall">After identifying the optimal set of potential predictors &#40;Figure 10&#8211;14 in Supplementary material&#41;&#44; clustering effects by patient and temporal distribution&#44; as well as cutoff points of the significant variables and their interactions were assessed with GLMM Trees&#46;<a class="elsevierStyleCrossRefs" href="#bib0045"><span class="elsevierStyleSup">9&#8211;11</span></a> To build these trees&#44; we took the entire dataset into account&#44; grouping data by patient and data charting time as random variables to fit the model&#46;<a class="elsevierStyleCrossRef" href="#bib0060"><span class="elsevierStyleSup">12</span></a> This fitting methodology avoids both over and underfitting effects that could impact the model&#8217;s performance&#46;<a class="elsevierStyleCrossRef" href="#bib0105"><span class="elsevierStyleSup">21</span></a> Models were implemented based on a 10-fold cross validation strategy using a four-depth-of-layers &#40;full&#44; 5&#44; 10 and 20&#41; strategy&#46; This means the fitting procedure was executed ten times per algorithm implementation&#46; It&#8217;s necessary to remark that the positive class for the invasive mechanical ventilation &#40;IMV&#41; variable refers to cases where IMV is required&#44; while the positive class for the <span class="elsevierStyleBold">ICU</span> mortality variable is related to cases where patients die&#46; It is worth mentioning that the focus of the study is on identifying independent variables and their associated thresholds with IMV and <span class="elsevierStyleBold">ICU</span> mortality&#44; without defining specific categories to predict&#46;</p><p id="par0055" class="elsevierStylePara elsevierViewall">We used a GLMM Tree to build conceptual models that explain the association between the potential predictors and the two outcome variables&#46; This algorithm accounts for data clusters and temporal characteristics of the dataset&#44; utilizing a mixed-effect strategy to combine the potential predictors that influence the outcome variables&#46; Additionally&#44; the algorithm provides a cut-off value for variables&#44; allowing for comparison with clinical experience&#46;</p><p id="par0060" class="elsevierStylePara elsevierViewall">GLMM Tree performance metrics were Area Under the Curve of Sensibility-Specificity &#40;AUC&#41;&#44; the Akaike Information Criterion &#40;AIC&#41; and the Bayesian Information Criterion &#40;BIC&#41;&#44;<a class="elsevierStyleCrossRef" href="#bib0110"><span class="elsevierStyleSup">22</span></a> as well as the deviance&#44; the likelihood statistical&#44;<a class="elsevierStyleCrossRef" href="#bib0115"><span class="elsevierStyleSup">23</span></a> and the sensitivity and specificity parameters&#46; All the regression and GLMM Tree models were fitted with the same subset of variables shown in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>&#46;</p><p id="par0065" class="elsevierStylePara elsevierViewall">We used both regressions and GLMM family trees to gain a wider understanding of potential predictors for IMV and <span class="elsevierStyleBold">ICU</span> mortality&#46; This combined approach offers more intuitive decision-making compared to black-box modeling strategies&#46; We assessed each predictor&#39;s effectiveness and used the same set of variables &#40;<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#41; to build an <span class="elsevierStyleBold">ICU</span> mortality model for the entire cohort&#46; The study&#39;s anonymized database and scripts can be found on the associated GitHub repository&#46;<a class="elsevierStyleCrossRef" href="#bib0120"><span class="elsevierStyleSup">24</span></a> The database will be published in PhysioNet<a class="elsevierStyleCrossRef" href="#bib0125"><span class="elsevierStyleSup">25</span></a> project in order to disseminate and exchange the anonymized clinical records looking for cooperative project replication&#46;</p></span></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0125">Results</span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0130">Patient characteristics</span><p id="par0070" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleBold">The complete cohort consisted of 280 patients who were included in the study&#46;</span> A total of 154 patients &#40;55 &#37;&#41; required IMV after ICU admission &#40;<a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>&#41;&#44; 65 of 80 patients &#40;81&#46;2 &#37;&#41; during the first and 89 of 200 patients &#40;44&#46;5 &#37;&#41; during the second wave&#46; ICU mortality of the whole cohort was 25&#46;7&#37; &#40;72 of 280 patients&#41;&#44; 33&#46;7&#37; &#40;27 of 80 patients&#41; during the first and 22&#46;5&#37; &#40;45 of 200 patients&#41; in the second wave&#46; <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a> shows IMV and <span class="elsevierStyleBold">ICU</span> mortality predictors for the whole patient&#8217;s cohort&#46; Mean registers per patient was 43&#46;4&#44; for a total of 12&#44;163 hourly registers in the whole database &#40;Figure 12 in complementary material&#41;&#46;</p></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0135">Significance of predictors</span><p id="par0075" class="elsevierStylePara elsevierViewall">R-Part classification achieves the best and most clinically plausible results in selecting the twelve most representative predictors for IMV and <span class="elsevierStyleBold">ICU</span> mortality from the whole group of available potential predictors &#40;<a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#41;&#46; Concerning this subset of predictors&#44; the final selection is based on decreasing order of importance&#44; according to results reached by the loss function &#40;mean squared error&#41;&#44; scaled from 0 to 100 points&#46; Taking into account this premise&#44; the predictors are&#58; days from first symptoms to ICU admission &#40;100&#41;&#44; the APACHE II score &#40;92&#46;25&#41;&#44; the oxygenation index&#44; ROX index &#40;72&#46;46&#41;&#44; blood procalcitonin &#40;69&#46;59&#41;&#44; serum lactic dehydrogenase &#40;54&#46;45&#41;&#44; total serum bilirubin &#40;36&#46;54&#41;&#44; the COVID-19 wave &#40;31&#46;18&#41;&#44; the dose of corticosteroids administered during the first five days of admission &#40;30&#46;96&#41;&#44; lymphocyte count &#40;15&#46;57&#41;&#44; pH &#40;13&#46;29&#41;&#44; BMI &#40;12&#46;76&#41;&#44; C-reactive protein &#40;12&#46;74&#41;&#44; time to oxygen therapy &#40;12&#46;42&#41; and body temperature &#40;10&#46;82&#41;&#46;</p></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0140">Modeling performance</span><p id="par0080" class="elsevierStylePara elsevierViewall">In <a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a>&#44; the performance of the IMV model is presented&#46; The R-part predictors Regression-GLMTREE pair achieved the highest performance with an AUROC of 0&#46;87&#44; as shown in Figure 8 in the Supplementary material&#46; Additionally&#44; the <span class="elsevierStyleBold">ICU</span> mortality model performed well&#44; with an AUROC of 0&#46;88&#44; as demonstrated in Figure 9 in the Supplementary material&#46; The IMV likelihood ratio &#40;RV&#43; 3&#46;16&#44; RV- 0&#46;177&#41; suggests that the test result is moderately useful for identifying or discharge patients susceptible to being treated with IMV&#46; Related to the CI &#40;95&#37;&#41;&#44; the reached interval &#40;0&#46;918 and <span class="elsevierStyleBold">0&#46;928</span>&#41; suggests a high level of precision considering the sensitivity&#44; specificity&#44; and accuracy of the model&#46; Related to <span class="elsevierStyleBold">ICU</span> mortality&#44; the IMV likelihood ratio &#40;RV&#43; 5&#44;105&#44; RV&#8722; 0&#46;424&#41; and CI &#40;95&#37;&#41; interval &#40;<span class="elsevierStyleBold">0&#46;817</span> and 0&#46;833&#41;&#44; results are also moderately useful&#46; <a class="elsevierStyleCrossRef" href="#fig0015">Fig&#46; 3</a> illustrates the <span class="elsevierStyleBold">ICU</span> Mortality decision tree&#44; while Figure 7 in the Supplementary material presents the IMV decision tree&#46; The optimal cut-off point for the prediction model was determined based on the IMV and <span class="elsevierStyleBold">ICU</span> mortality AUC&#44; using Youden&#39;s Index&#44;<a class="elsevierStyleCrossRef" href="#bib0130"><span class="elsevierStyleSup">26</span></a> which identifies the point of maximum sum of sensitivity and specificity in ROC curve analysis&#46;</p><elsevierMultimedia ident="tbl0015"></elsevierMultimedia><elsevierMultimedia ident="fig0015"></elsevierMultimedia><p id="par0085" class="elsevierStylePara elsevierViewall">The trees in Figures 6 and 7 of the Supplementary material indicate that oxygenation status &#40;ROX index&#41; has the most significant influence on IMV&#44; with a threshold near 5&#46;2&#46; On the other hand&#44; <span class="elsevierStyleBold">ICU</span> mortality is mainly influenced by comorbidities &#40;APACHE II score&#41; and LDH&#44; as revealed by the same trees&#46;</p></span></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0145">Discussion</span><p id="par0090" class="elsevierStylePara elsevierViewall">The results of the present study include some highly relevant clinical results&#46; First&#44; the variable sets predicting IMV&#44; and <span class="elsevierStyleBold">ICU</span> mortality are different&#46; Whereas oxygenation variables are independent predictors of IMV&#44; <span class="elsevierStyleBold">ICU</span> mortality is associated with increased age and LDH and the presence of comorbidities&#46; The latter variables may be considered markers of two processes&#58; COVID-19-associated inflammation and ICU-acquired superinfection &#40;see Figure 4 in the Supplementary material&#41;&#46; Secondly&#44; the characteristics of pharmacological therapy&#44; including the administration of steroid drugs&#44; has little influence on both the need for IMV and <span class="elsevierStyleBold">ICU</span> mortality&#44; considering our results&#46; We included in the analysis 64 patients not receiving steroids and 216 receiving this treatment&#44; at the usual 6<span class="elsevierStyleHsp" style=""></span>mg dexamethasone or equivalent daily dose&#46; This is a remarkable finding&#44; because the effect of steroids on mortality identified in a previous trial<a class="elsevierStyleCrossRef" href="#bib0135"><span class="elsevierStyleSup">27</span></a> have influenced recommendations&#44; as well as clinical practice&#44; since its publication&#46; It may be speculated that the decision to include and randomize or not at the discretion of the attending physicians&#44; and based on undisclosed criteria&#44; rendered different results by selecting a study subset of COVID-19 cases with different characteristics&#46; In comparison&#44; no inclusion-exclusion criteria for selection process were applied in our &#8220;pragmatic&#8221; type of cohort&#46; Steroids were given to almost every patient unless a severe contraindication existed&#44; after the results of the RECOVERY trial were made available&#46;</p><p id="par0095" class="elsevierStylePara elsevierViewall">The present study applied a novel methodology &#40;logistic regression with regularization plus GLMM Tree mixed models&#41; to evaluate the relative importance of several variables as predictors of significant clinical events&#46; Using machine learning and a fine-grained longitudinal multifaceted database&#44; we have established relevant variable value thresholds to support clinical decisions&#46; Although the model would perform quite well as predictor for IMV and <span class="elsevierStyleBold">ICU</span> mortality&#44; with good positive predictive values&#44; it is important to emphasize that this is not a predictive model in the classical sense&#44; but an attempt to pinpoint the most important clinical events that represent turning points during the studied process &#40;in this case&#44; clinical management of patients not initially under IMV&#41;&#46; In this sense&#44; we should say that the inclusion of the likelihood ratio as an evaluation factor for comparing performance model was reach great results&#46; However&#44; following the premise of model explainability&#44; we believe it is important to take this element into account as a final selection factor for the set of predictors that best fit daily clinical practice&#46; This study demonstrates that predictor-ranking methodologies using self-explainable machine learning may support therapeutic decision-making using observational data&#44; when randomized clinical trials are unfeasible or unethical&#46;</p><p id="par0100" class="elsevierStylePara elsevierViewall">Regarding with the strengths of our study&#44; we would like to mention the quantity and quality of the data set&#46; Collected data have a high level of detail&#44; leveraging the power of strategically devised electronic health records &#40;EHR&#41;&#44; which include relevant information in a highly structured and recoverable format&#46; Every effort was made to configure our EHR to optimally gather all relevant information about COVID-19 patients&#46; Also&#44; our anonymized database is available in the repository along with the script we used for statistical analysis&#44; is highly detailed and has been extensively curated to reflect temporal evolution and to improve data quality as much as possible&#46; In any case&#44; the collection of variables from Electronic Health Records &#40;EHR&#41; may be biased&#44; affecting data quality&#46; Age and gender biases are possible&#44; as well as biases related to the selection and measurement of clinical variables&#46; These biases can lead to incomplete or skewed representations of certain population groups and may impact the validity and generalizability of research findings and clinical decision-making&#46; It is important to be aware of these biases to ensure proper interpretation and use of EHR data&#46;</p><p id="par0105" class="elsevierStylePara elsevierViewall">On the other hand&#44; the limitations of our study results relate mainly to its single-centered nature and require confirmation in a multicenter dataset to gain external validity&#46; Our methodology would be perfectly suited for a multicenter study&#44; including &#8220;center&#8221; as a random factor in the second &#40;GLMM Tree&#41; part of the process&#46; We suggest that future research applying this methodology could focus on designing clinical studies using observational data to answer relevant clinical questions without the logistic requirements of a randomized clinical trial or for hypothesis-generating purposes&#46; Furthermore&#44; when considering the limitations of using generalized linear mixed effects models &#40;GLMMs&#41; for modeling causation in critical care medicine research&#44; it is important to highlight the absence of explicit causality assumptions&#46; GLMMs primarily focus on association or correlation analysis&#44; lacking the ability to address the assumptions necessary for establishing causal relationships&#46; Specifically&#44; GLMMs do not provide frameworks for the identification of causal effects or account for unmeasured confounding variables&#44; which are crucial considerations in causal inference&#46; In contrast&#44; causal inference methods&#44; such as the potential outcomes framework&#44; explicitly address these assumptions&#44; offering a more comprehensive approach for investigating causality&#46; Therefore&#44; when establishing causal relationships between variables&#44; researchers should carefully consider the limitations of GLMMs and opt for causal inference methods&#44; which provide a more robust approach for investigating causality in critical care medicine research&#46;</p><p id="par0110" class="elsevierStylePara elsevierViewall">In conclusion&#44; different variables predict IMV and <span class="elsevierStyleBold">ICU</span> mortality in severe COVID-19 patients&#44; suggesting that the therapeutic decision of when to use IMV has little impact on <span class="elsevierStyleBold">ICU</span> mortality&#46; Our methodology is a valid option to assess therapeutic decisions using observational data when randomized clinical trials are not feasible or ethical&#46;</p></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0150">Author&#39;s contribution</span><p id="par0115" class="elsevierStylePara elsevierViewall">SM&#44; MA and AN conceived the presented idea&#46; <span class="elsevierStyleBold">SM and MA contributed equally as first authors&#46;</span> SM and MA developed the theory and performed the computations&#46; AN conducted an independent literature search to identify potentially relevant studies&#46; MS independently reviewed the search results to identify pertinent articles&#46; MS&#44; AN&#44; TF and VY contributed to the interpretation of the results&#46; SM&#44; MA&#44; AN&#44; MS&#44; FL and AC took the lead in writing the manuscript&#46; All authors provided critical feedback and helped shape the research&#44; analysis&#44; and manuscript&#46;</p></span><span id="sec0055" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0155">Funding</span><p id="par0120" class="elsevierStylePara elsevierViewall">This research did not receive any specific grant from funding agencies in the public&#44; commercial&#44; or not-for-profit sectors&#46;</p></span><span id="sec0060" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0160">Conflict of interest</span><p id="par0125" class="elsevierStylePara elsevierViewall">The authors declare that they have no conflict of interest&#46;</p></span></span>"
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          "identificador" => "sec0060"
          "titulo" => "Conflict of interest"
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        11 => array:2 [
          "identificador" => "xack720014"
          "titulo" => "Acknowledgements"
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        12 => array:1 [
          "titulo" => "References"
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      ]
    ]
    "pdfFichero" => "main.pdf"
    "tienePdf" => true
    "fechaRecibido" => "2023-03-03"
    "fechaAceptado" => "2023-06-22"
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        0 => array:4 [
          "clase" => "keyword"
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          "palabras" => array:6 [
            0 => "Acute respiratory distress syndrome"
            1 => "Invasive mechanical ventilation"
            2 => "COVID-19"
            3 => "Machine learning"
            4 => "Artificial intelligence"
            5 => "Predictors"
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          "palabras" => array:6 [
            0 => "S&#237;ndrome de distr&#233;s respiratorio agudo"
            1 => "Ventilaci&#243;n mec&#225;nica invasiva"
            2 => "COVID-19"
            3 => "Aprendizaje autom&#225;tico"
            4 => "Inteligencia artificilal"
            5 => "Predictores"
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    "resumen" => array:2 [
      "en" => array:3 [
        "titulo" => "Abstract"
        "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Objective</span><p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">To determine if potential predictors for invasive mechanical ventilation &#40;IMV&#41; are also determinants for mortality in COVID-19-associated acute respiratory distress syndrome &#40;C-ARDS&#41;&#46;</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Design</span><p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">Single center highly detailed longitudinal observational study&#46;</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Setting</span><p id="spar0065" class="elsevierStyleSimplePara elsevierViewall">Tertiary hospital ICU&#58; two first COVID-19 pandemic waves&#44; Madrid&#44; Spain&#46;</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Patients or participants</span><p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">&#58; 280 patients with C-ARDS&#44; not requiring IMV on admission&#46;</p></span> <span id="abst0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0030">Interventions</span><p id="spar0075" class="elsevierStyleSimplePara elsevierViewall">None&#46;</p></span> <span id="abst0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0035">Main variables of interest</span><p id="spar0080" class="elsevierStyleSimplePara elsevierViewall">&#58; Target&#58; endotracheal intubation and IMV&#44; mortality&#46;</p><p id="spar0085" class="elsevierStyleSimplePara elsevierViewall">Predictors&#58; demographics&#44; hourly evolution of oxygenation&#44; clinical data&#44; and laboratory results&#46;</p></span> <span id="abst0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">Results</span><p id="spar0090" class="elsevierStyleSimplePara elsevierViewall">The time between symptom onset and ICU admission&#44; the APACHE II score&#44; the ROX index&#44; and procalcitonin levels in blood were potential predictors related to both IMV and mortality&#46; The ROX index was the most significant predictor associated with IMV&#44; while APACHE II&#44; LDH&#44; and DaysSympICU were the most with mortality&#46;</p></span> <span id="abst0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Conclusions</span><p id="spar0095" class="elsevierStyleSimplePara elsevierViewall">According to the results of the analysis&#44; there are significant predictors linked with IMV and mortality in C-ARDS patients&#44; including the time between symptom onset and ICU admission&#44; the severity of the COVID-19 waves&#44; and several clinical and laboratory measures&#46; These findings may help clinicians to better identify patients at risk for IMV and mortality and improve their management&#46;</p></span>"
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            "titulo" => "Setting"
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            "titulo" => "Patients or participants"
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        "titulo" => "Resumen"
        "resumen" => "<span id="abst0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0055">Objetivo</span><p id="spar0100" class="elsevierStyleSimplePara elsevierViewall">Determinar si las variables cl&#237;nicas independientes que condicionan el inicio de ventilaci&#243;n mec&#225;nica invasiva &#40;VMI&#41; son los mismos que condicionan la mortalidad en el s&#237;ndrome de distr&#233;s respiratorio agudo asociado con COVID-19 &#40;C-SDRA&#41;&#46;</p></span> <span id="abst0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0060">Dise&#241;o</span><p id="spar0105" class="elsevierStyleSimplePara elsevierViewall">Estudio observacional longitudinal en un solo centro&#46;</p></span> <span id="abst0055" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0065">&#193;mbito</span><p id="spar0110" class="elsevierStyleSimplePara elsevierViewall">UCI&#44; hospital terciario&#58; primeras dos olas de COVID-19 en Madrid&#44; Espa&#241;a&#46;</p></span> <span id="abst0060" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Pacientes o participantes</span><p id="spar0115" class="elsevierStyleSimplePara elsevierViewall">280 pacientes con C-SDRA que no requieren VMI al ingreso en UCI&#46;</p></span> <span id="abst0065" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">Intervenciones</span><p id="spar0120" class="elsevierStyleSimplePara elsevierViewall">Ninguna&#46;</p></span> <span id="abst0070" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Principales variables de inter&#233;s</span><p id="spar0125" class="elsevierStyleSimplePara elsevierViewall">Objetivo&#58; VMI y Mortalidad&#46;</p><p id="spar0130" class="elsevierStyleSimplePara elsevierViewall">Predictores&#58; demogr&#225;ficos&#44; variables cl&#237;nicas&#44; resultados de laboratorio y evoluci&#243;n de la oxigenaci&#243;n&#46;</p></span> <span id="abst0075" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Resultados</span><p id="spar0135" class="elsevierStyleSimplePara elsevierViewall">El tiempo entre el inicio de los s&#237;ntomas y el ingreso en la UCI&#44; la puntuaci&#243;n APACHE II&#44; el &#237;ndice ROX y los niveles de procalcitonina en sangre eran posibles predictores relacionados tanto con la IMV como con la mortalidad&#46; El &#237;ndice ROX fue el predictor m&#225;s significativo asociada con la IMV&#44; mientras que APACHE II&#44; LDH y DaysSympICU fueron los m&#225;s influyentes en la mortalidad&#46;</p></span> <span id="abst0080" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Conclusiones</span><p id="spar0140" class="elsevierStyleSimplePara elsevierViewall">Seg&#250;n los resultados obtenidos se identifican predictores significativos vinculados con la VMI y mortalidad en pacientes con C-ARDS&#44; incluido el tiempo entre el inicio de los s&#237;ntomas y el ingreso en la UCI&#44; la gravedad de las olas de COVID-19 y varias medidas cl&#237;nicas y de laboratorio&#46; Estos hallazgos pueden ayudar a los m&#233;dicos a identificar mejor a los pacientes en riesgo de IMV y mortalidad y mejorar su manejo&#46;</p></span>"
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            "titulo" => "Dise&#241;o"
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            "titulo" => "&#193;mbito"
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            "titulo" => "Pacientes o participantes"
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            "titulo" => "Intervenciones"
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            "titulo" => "Principales variables de inter&#233;s"
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            "apendice" => "<p id="par0140" class="elsevierStylePara elsevierViewall">The following is Supplementary data to this article&#58;<elsevierMultimedia ident="upi0005"></elsevierMultimedia></p>"
            "etiqueta" => "Appendix A"
            "titulo" => "Supplementary data"
            "identificador" => "sec0070"
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      0 => array:8 [
        "identificador" => "fig0005"
        "etiqueta" => "Figure 1"
        "tipo" => "MULTIMEDIAFIGURA"
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          "en" => "<p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">COVID-19 patients admitted during first and second pandemic waves&#46; The cohort comprises 280 severe COVID-19 patients admitted to the ICU Department at HCSC in Madrid&#44; Spain&#44; between March 3&#44; 2020&#44; and February 28&#44; 2021&#46; During this time period&#44; SARS-COV-2 wild-type and subsequently alpha variants were prevalent in Spain&#46; Over the study time period 4229 covid-19 patients were admitted to HCSC&#44; 405 of whom required ICU admission &#40;first wave&#58; 153&#44; second wave&#58; 252 patients&#41;&#46;</p>"
        ]
      ]
      1 => array:8 [
        "identificador" => "fig0010"
        "etiqueta" => "Figure 2"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
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        "figura" => array:1 [
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        "detalles" => array:1 [
          0 => array:3 [
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        "descripcion" => array:1 [
          "en" => "<p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">Methodology for fitting the machine learning algorithms&#46; In a previous stage&#44; Figure 5 in Supplementary material shows the complete workflow&#44; from the cohort selection according to clinical needs to the implementation of the algorithms that have been included in the explanation&#46; The first step involves the cohort selection as well as the initial group of variables considered in this study&#44; The second step consists in the implementation of a statistical study of each variable&#46; This step also involves correlation &#40;Figure 6 in Supplementary material&#41; imputation and transformations procedures in order to dispose of the most accurate data in the following steps&#46; The third step analyzed the most significant predictors based on five <span class="elsevierStyleBold">Machine</span> Learning &#40;ML&#41; techniques linked with regression analysis based on 10-fold cross-validation regressions&#46; The fourth and last step identifies the behavior of each predictor attending to different proposes&#46; The first one is related to mechanical ventilation needs attending to different settings in the Generalized Linear Mixed Model &#40;GLMM&#41; Tree &#40;depth of layers&#41; looking for the best balance between performance &#40;Akaike Information Criterion &#40;AIC&#41;&#44; Bayesian information criterion &#40;BIC&#41;&#44; Area Under the Roc Curve &#40;ROC&#41; and more parameters within the table III&#41; and explainability of the model&#46; The second one is related to the most representative mortality predictors but following the same balance objective&#46;</p>"
        ]
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        "identificador" => "fig0015"
        "etiqueta" => "Figure 3"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
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        "figura" => array:1 [
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            "imagen" => "gr3.jpeg"
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        "detalles" => array:1 [
          0 => array:3 [
            "identificador" => "at0085"
            "detalle" => "Figure "
            "rol" => "short"
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        "descripcion" => array:1 [
          "en" => "<p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">ICU Mortality Tree Predictors&#46; The predictors appear in different branches attending to their significance in the predictive model&#46; Values in bold letters represent the registries per branch&#46; Values in red bold letters represent the percentage of registries with positive outcome&#46; The variable named as &#8220;DAYS&#95;SIMPTONS&#95;ADMISION&#8221; is related with the number of days from first symptoms to ICU admission&#46; The variable &#8220;linf&#95;total&#8221;&#44; is related to lymphocyte count per mm3&#46; The variable named as &#8220;dosis&#95;equiv&#95;mpred&#95;5d&#8221; is related with the corticosteroid dose&#44; during the first five days of admission &#40;mg of equivalent methylprednisolone dose&#41;&#46; The variable named as &#8220;bbTot&#8221; is related with the total levels of bilirubin in blood&#46; The variable names as &#8220;ldh&#8221; is related to the lactate dehydrogenase serum level&#46; The variable DAYS&#95;UNTIL&#95;O2 is related to the number of days until the patient requires O2&#46;</p>"
        ]
      ]
      3 => array:8 [
        "identificador" => "tbl0005"
        "etiqueta" => "Table 1"
        "tipo" => "MULTIMEDIATABLA"
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          "leyenda" => "<p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">This group of predictors will be applied in the selection procedure linked with the five regression algorithms&#58; Ridge&#44; LASSO&#44; Elastic&#44; Boruta and R-part Based on the reached results&#44; the group of predictors are going to be reduced attending to its behavior related to IMV needs&#46; Figures 7&#8211;11 &#40;Supplementary material&#41; shows the results from each regression procedure where R-Part was finally selected due to its good balance between model performance and explicability of results&#46;</p><p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">Data updated June 22&#44; 2023&#46;</p>"
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                  <table border="0" frame="\n
                  \t\t\t\t\tvoid\n
                  \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td-with-role" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t ; entry_with_role_colgroup " colspan="6" align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Dataset clinical and biochemical characteristics</th></tr><tr title="table-row"><th class="td-with-role" title="\n
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                  \t\t\t\t ; entry_with_role_colgroup " colspan="6" align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">Invasive Mechanical Ventilation &#40;IMV&#41;</span></th></tr><tr title="table-row"><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col">Variable&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" scope="col"><span class="elsevierStyleItalic">N</span>&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col">Overall&#44; <span class="elsevierStyleItalic">N</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>12&#44;163<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">a</span></a>&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
                  \t\t\t\t ; entry_with_role_colgroup " colspan="2" align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Invasive Mechanical Ventilation &#40;IMV&#41;</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
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                  \t\t\t\t" scope="col">p-value<a class="elsevierStyleCrossRef" href="#tblfn0010"><span class="elsevierStyleSup">b</span></a>&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">No&#44; <span class="elsevierStyleItalic">N</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>9093<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">a</span></a>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Yes&#44; <span class="elsevierStyleItalic">N</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>3070<a class="elsevierStyleCrossRef" href="#tblfn0005"><span class="elsevierStyleSup">a</span></a>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t">Age&#44; years&#44; Median &#40;Q1-Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">59 &#40;51&#8211;68&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">58 &#40;50&#8211;67&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">63 &#40;54&#8211;70&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">Gender&#44; n &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">Ethnicity&#44; n &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Spanish&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">Wave&#44; n &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Second&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Body mass index&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;70&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Heart rate&#44; median bpm &#40;IQR&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">73 &#40;65&#8211;84&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">73 &#40;64&#8211;83&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">76 &#40;67&#8211;87&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Temperature in &#186;C&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">36&#46;80 &#40;36&#46;50&#8211;37&#46;10&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">36&#46;73 &#40;36&#46;44&#8211;37&#46;02&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">36&#46;97 &#40;36&#46;64&#8211;37&#46;37&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Arterial pressure in mmHg&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">87 &#40;79&#8211;95&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">87 &#40;80&#8211;95&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">87 &#40;78&#8211;95&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Lactate in mEq&#47;l&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1&#46;42 &#40;1&#46;20&#8211;1&#46;70&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1&#46;42 &#40;1&#46;14&#8211;1&#46;65&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1&#46;50 &#40;1&#46;33&#8211;1&#46;80&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Procalcitonin&#44; ng&#47;mL Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;13 &#40;0&#46;08 &#8211; 0&#46;23&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;13 &#40;0&#46;07 &#8211; 0&#46;20&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;14 &#40;0&#46;13 &#8211; 0&#46;35&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Eosinophile count per cubic mm&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">4 &#40;0&#8211;20&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">4 &#40;0&#8211;22&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">4 &#40;0&#8211;13&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;011&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">C reactive protein&#44; mg&#47;L Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">8 &#40;6&#8211;11&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">8 &#40;4&#8211;10&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">8 &#40;8&#8211;15&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Alkaline phosphatase U&#47;L&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">82 &#40;68&#8211;101&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">82 &#40;65&#8211;99&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">82 &#40;76&#8211;104&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;006&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Total bilirubin mg&#47;dL&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;53 &#40;0&#46;44 &#8211; 0&#46;62&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;53 &#40;0&#46;42 &#8211; 0&#46;59&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;53 &#40;0&#46;51 &#8211; 0&#46;71&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Oxygenation index &#40;ROX Index&#41;&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">5&#46;93 &#40;4&#46;52&#8211;7&#46;92&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">6&#46;18 &#40;5&#46;22&#8211;8&#46;67&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">4&#46;46 &#40;3&#46;62&#8211;5&#46;93&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Creatinine&#44; mg&#47;dL Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;67 &#40;0&#46;59&#8211;0&#46;78&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;67 &#40;0&#46;58&#8211;0&#46;77&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;67 &#40;0&#46;65&#8211;0&#46;82&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Leukocyte count per mm3&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">8925 &#40;7160&#8211;10&#44;804&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">8925 &#40;6857&#8211;10&#44;548&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">8925 &#40;8400&#8211;11&#44;478&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Hemoglobin g&#47;l&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">13&#46;16 &#40;12&#46;28&#8211;13&#46;96&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">13&#46;16 &#40;12&#46;20&#8211;13&#46;93&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">13&#46;16 &#40;12&#46;63&#8211;14&#46;03&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Amylase U&#47;L&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">63 &#40;50&#8211;79&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">63 &#40;51&#8211;84&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">63 &#40;48&#8211;64&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Lactate dehydrogenase&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">882 &#40;749 &#8211; 1038&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">882 &#40;682&#8211;964&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">939 &#40;882&#8211;1172&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Lymphocyte count per mm3&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">829 &#40;638&#8211;1049&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">829 &#40;657&#8211;1148&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">829 &#40;570&#8211;871&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">AST &#40;Aspartate Aminotransferase&#41; U&#47;L&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">45 &#40;34&#8211;60&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">45 &#40;34&#8211;63&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">45 &#40;33&#8211;54&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Hours from ICU admission to this register&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">31 &#40;14&#8211;50&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">33 &#40;16&#8211;51&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">23 &#40;9&#8211;45&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">APACHE &#40;Acute Physiology and Chronic Health Evaluation&#41; II&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">13&#46;0 &#40;10&#46;0&#8211;17&#46;0&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#46;0 &#40;10&#46;0&#8211;16&#46;0&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">15&#46;0 &#40;13&#46;0&#8211;17&#46;0&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Days from first symptoms to O2 therapy&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">7&#46;00 &#40;6&#46;00&#8211;8&#46;00&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">7&#46;00 &#40;6&#46;00&#8211;8&#46;00&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">7&#46;00 &#40;6&#46;00&#8211;8&#46;00&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;008&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Days from first symptoms to ICU admission&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">9&#46;0 &#40;8&#46;0&#8211;11&#46;0&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">9&#46;0 &#40;8&#46;0&#8211;11&#46;0&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">9&#46;0 &#40;7&#46;0&#8211;13&#46;0&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Arterial pH&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">7&#46;43 &#40;7&#46;41&#8211;7&#46;45&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">7&#46;43 &#40;7&#46;41&#8211;7&#46;46&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">7&#46;43 &#40;7&#46;39&#8211;7&#46;44&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Arterial pCO2&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">38&#46;1 &#40;35&#46;7&#8211;41&#46;0&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">38&#46;1 &#40;35&#46;6&#8211;40&#46;7&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">38&#46;4 &#40;36&#46;0&#8211;42&#46;4&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Type of blood sample&#44; n &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Arterial&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">696 &#40;5&#46;7&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">496 &#40;5&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">200 &#40;6&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>BLDO &#40;Capillary blood gas analysis&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">5 &#40;&#60;0&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">5 &#40;&#60;0&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0 &#40;0&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Arterial&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">91 &#40;0&#46;7&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">27 &#40;0&#46;3&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">64 &#40;2&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Mixed&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">28 &#40;0&#46;2&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">28 &#40;0&#46;3&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0 &#40;0&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Venous&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t">1405 &#40;12&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">931 &#40;10&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">474 &#40;15&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Venous&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t">9845 &#40;81&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">7529 &#40;83&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">2316 &#40;75&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Mixed venous&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">93 &#40;0&#46;8&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">77 &#40;0&#46;8&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">16 &#40;0&#46;5&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Blood gas sat&#46; O2&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">85 &#40;75&#8211;91&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">85 &#40;77&#8211;91&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">84 &#40;72&#8211;89&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Corticosteroid dose&#44; first 5 days of admission &#40;mg of equivalent methylprednisolone dose&#41;&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">36 &#40;30&#8211;60&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">36 &#40;30&#8211;60&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">36 &#40;30&#8211;78&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">0&#46;32&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Melatonin dose in mg&#47;day&#44; n &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">D dimer&#44; ng&#47;mL Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Dataset variables statistical characteristics</th></tr><tr title="table-row"><th class="td-with-role" title="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">ICU mortality</span></th></tr><tr title="table-row"><th class="td" title="\n
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                  \t\t\t\t" scope="col">Variable&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col"><span class="elsevierStyleItalic">N</span>&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col">Overall&#44; <span class="elsevierStyleItalic">N</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>12&#44;163<a class="elsevierStyleCrossRef" href="#tblfn0015"><span class="elsevierStyleSup">a</span></a>&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Alive&#44; <span class="elsevierStyleItalic">N</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>9777<a class="elsevierStyleCrossRef" href="#tblfn0015"><span class="elsevierStyleSup">a</span></a>&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">Days elapsed from first symptoms to ICU admission&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">9&#46;0 &#40;8&#46;0&#8211;11&#46;0&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">9&#46;0 &#40;7&#46;0&#8211;13&#46;0&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t">APACHE &#40;Acute Physiology and Chronic Health Evaluation&#41; II&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t">12&#46;0 &#40;10&#46;0&#8211;16&#46;0&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">15&#46;0 &#40;13&#46;0&#8211;17&#46;0&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t">Corticosteroids administered during the first 5d of admission as mg of equivalent methylprednisolone dose&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">36 &#40;30&#8211;60&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">36 &#40;30&#8211;60&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">36 &#40;30&#8211;80&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Oxygenation index&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">5&#46;93 &#40;4&#46;52&#8211;7&#46;92&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">5&#46;93 &#40;4&#46;95&#8211;8&#46;42&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">4&#46;58 &#40;3&#46;63&#8211;5&#46;93&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Serum Lactate dehydrogenase&#44; U&#47;L Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">882 &#40;749 &#8211; 1038&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">882 &#40;695&#8211;964&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1026 &#40;882&#8211;1279&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Body mass index&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">27&#46;8 &#40;26&#46;0&#8211;31&#46;1&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">27&#46;8 &#40;26&#46;0&#8211;31&#46;8&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">27&#46;8 &#40;26&#46;0&#8211;29&#46;4&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Temperature in &#186;C&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">36&#46;80 &#40;36&#46;50&#8211;37&#46;10&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">36&#46;78 &#40;36&#46;50&#8211;37&#46;10&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">36&#46;86 &#40;36&#46;50&#8211;37&#46;20&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Days elapsed from first symptoms to O2 therapy&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">7&#46;00 &#40;6&#46;00&#8211;8&#46;00&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">7&#46;00 &#40;6&#46;00&#8211;8&#46;00&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">7&#46;00 &#40;6&#46;00&#8211;7&#46;00&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;073&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Total bilirubin mg&#47;dL&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;53 &#40;0&#46;44&#8211;0&#46;62&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;53 &#40;0&#46;42&#8211;0&#46;60&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#46;53 &#40;0&#46;51&#8211;0&#46;68&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Wave&#44; n &#40;&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">First&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1490 &#40;12&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1031 &#40;11&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">459 &#40;19&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Second&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">10&#44;673 &#40;88&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">8746 &#40;89&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1927 &#40;81&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Lymphocyte count per mm3&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">829 &#40;638&#8211;1049&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">829 &#40;667&#8211;1120&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">800 &#40;499&#8211;886&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Arterial pH&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">7&#46;43 &#40;7&#46;41&#8211;7&#46;45&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">7&#46;43 &#40;7&#46;41&#8211;7&#46;46&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">7&#46;43 &#40;7&#46;38&#8211;7&#46;45&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t">C reactive protein levels mg&#47;L&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">12&#44;163&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">8 &#40;6&#8211;11&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">8 &#40;5&#8211;10&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">8 &#40;8&#8211;14&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t">Hours from ICU admission to this register&#44; Median &#40;Q1&#8211;Q3&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">31 &#40;14&#8211;50&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">31 &#40;14&#8211;51&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">28 &#40;11&#8211;47&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">&#60;0&#46;001&nbsp;\t\t\t\t\t\t\n
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          "leyenda" => "<p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">The Akaike Information Criterion &#40;AIC&#41; reports the information score of the whole models&#58; the smaller the <span class="elsevierStyleBold"><span class="elsevierStyleItalic">AIC value</span></span>&#44; the better the model fit&#46; AIC is calculated from the number of independent variables to build the model and the maximum likelihood estimate of the model &#40;how well the model reproduces the data&#41;&#46; The best-fit model according to AIC is the one that explains the greatest amount of variation using the fewest possible independent variables&#46; <span class="elsevierStyleBold"><span class="elsevierStyleItalic">Bayesian information criterion &#40;BIC&#41;</span></span> is another criteria for model selection that measures the trade-off between model fit and complexity of the model&#46; A lower AIC or BIC value indicates a better fit&#46; The <span class="elsevierStyleBold"><span class="elsevierStyleItalic">log-likelihood &#40;log Lik&#41;</span></span> value of a regression model is a way to measure the goodness of fit for a model&#46; The higher the value of the log-likelihood&#44; the better a model fits a dataset&#46; <span class="elsevierStyleBold"><span class="elsevierStyleItalic">Deviance</span></span> is a goodness-of-fit metric for statistical models&#44; particularly used for GLMs&#46; It is defined as the difference between the Saturated and Proposed Models and can be thought as how much variation in the data does our Proposed Model account for&#46; Therefore&#44; the lower the deviance&#44; the better the model&#46; <span class="elsevierStyleBold"><span class="elsevierStyleItalic">Sensitivity</span></span> is the metric that evaluates a model&#39;s ability to predict true positives of each available category&#46; Specificity is the metric that evaluates a model&#39;s ability to predict true negatives of each available category&#46; The higher value of sensitivity would mean higher value of true positive and lower value of false negative&#46; For the healthcare domain&#44; models with high sensitivity will be desired&#46; <span class="elsevierStyleBold"><span class="elsevierStyleItalic">Specificity</span></span> is the metric that evaluates a model&#39;s ability to predict true negatives of each available category&#46; These metrics apply to any categorical model&#46; Specificity is defined as the proportion of actual negatives&#44; which got predicted as the negative &#40;or true negative&#41;&#46; Specificity is a measure of the proportion of people not suffering from the disease who got predicted correctly as the ones who are not suffering from the disease&#46; In other words&#44; the person who is healthy actually got predicted as healthy&#46; The likelihood ratio is often used in statistical hypothesis testing and model selection to compare the fit of different models to the observed data&#46; It is also commonly used in medical diagnostic testing to evaluate the diagnostic accuracy of a particular test or combination of tests&#46; LR&#43; &#40;likelihood ratio positive&#41; is a statistical measure used to evaluate the diagnostic accuracy of a medical test&#46; It is the ratio of the probability of a positive test result given the presence of the disease to the probability of a positive test result given the absence of the disease&#46; In other words&#44; the LR&#43; compares the likelihood of a positive test result in patients with the disease versus the likelihood of a positive test result in patients without the disease&#46; In our case&#44; a high LR&#43; indicates that the test is more accurate at correctly identifying patients how could need IMV&#44; while a low LR&#43; suggests that the test is not providing strong evidence for IMV&#46; By the way&#44; LR&#8722; compares the likelihood of a negative test result in patients with the disease versus the likelihood of a negative test result in patients without the disease&#46; A low LR- indicates that the test is more accurate at correctly identifying patients without the need of IMV&#44; while a high LR- suggests that the test is not providing strong evidence for the absence of IMV&#46; The LR&#43; and LR&#8722; are often used in conjunction with other measures of diagnostic accuracy&#44; such as sensitivity&#44; specificity to assess the overall performance of a medical test&#46; It can help clinicians and researchers determine the optimal use of a particular test in diagnosing a disease or condition&#46; CI stands for &#34;confidence interval&#46;&#34; A confidence interval CI is a range of values that is likely to contain the true value of a population parameter &#40;such as a mean or a proportion&#41;&#44; with a certain degree of confidence &#40;usually expressed as a percentage&#44; such as 95&#37; or 99&#37;&#41;&#46; A narrower interval indicates greater precision&#44; while a wider interval indicates greater uncertainty&#46; The exact range of a &#34;good&#34; CI can vary depending on the context and the specific research question&#44; but typically&#44; a narrower interval is preferred as it provides a more precise estimate&#46; In the case of the area under the receiver operating characteristic curve &#40;AUROC&#41;&#44; which is commonly used in binary classification problems&#44; a CI that includes a value of 0&#46;5 &#40;indicating no discrimination between the two groups&#41; is generally considered to be uninformative&#46; On the other hand&#44; a CI that does not include 0&#46;5 and has a range of&#44; for example&#44; 0&#46;7&#8211;0&#46;8&#44; may be considered good&#44; indicating that the model has reasonably good discriminative ability&#46; However&#44; the interpretation of the AUROC and its associated CI should always be considered in the context of the specific research question and the particular field of study&#46;</p>"
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">GLMM &#40;Generalized Linear Mixed Model&#41; trees results</th></tr><tr title="table-row"><th class="td-with-role" title="\n
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                  \t\t\t\t ; entry_with_role_colgroup " colspan="12" align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black"><span class="elsevierStyleItalic">Mechanical ventilation</span></th></tr><tr title="table-row"><th class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Regressions&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">N&#186; Predictors&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">C&#46;I&#40;95&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">AIC&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">BIC&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Deviance&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Log Lik&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Sensitivity&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Specificity&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th><th class="td" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">LR&#43;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-head\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">LR&#8722;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n
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                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t">Ridge criteria&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">25&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">0&#46;852&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t"><span class="elsevierStyleBold">0&#46;859&#8722;0&#46;872</span>&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">9263&#46;82&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">9493&#46;41&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;856&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">0&#46;689&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">2&#46;75&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">0&#46;206&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">LASSO criteria&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">22&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">0&#46;852&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleBold">0&#46;847&#8211;0&#46;862</span>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
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                  \t\t\t\t">9263&#46;82&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">94&#44;939&#46;41&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">9201&#46;82&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">&#8722;4&#46;600&#46;91&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;856&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t"><span class="elsevierStyleBold">0&#46;852</span>&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">5&#46;78&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">0&#46;166&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
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Article information
ISSN: 21735727
Original language: English
DOI:10.1016/j.medine.2023.07.009
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