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severe sepsis and septic shock have been evaluated in the ICU&#44;<a class="elsevierStyleCrossRef" href="#bib0205"><span class="elsevierStyleSup">5</span></a> customized versions for severe sepsis and septic shock of in-hospital mortality classification systems have also been developed&#44;<a class="elsevierStyleCrossRefs" href="#bib0205"><span class="elsevierStyleSup">5&#44;6</span></a> and even particular models have been created for in hospital mortality prediction of ICU patients with sepsis&#44; severe sepsis and septic shock&#46;<a class="elsevierStyleCrossRefs" href="#bib0215"><span class="elsevierStyleSup">7&#8211;10</span></a></p><p id="par0025" class="elsevierStylePara elsevierViewall">Cited works report better performance than traditionally severity of disease scores and tend to focus on the prediction of in-hospital mortality&#44; however&#44; long-term outcomes from sepsis are poorly understood&#46; Winters et al&#46; concluded that patients with sepsis have ongoing mortality beyond short-term&#44; so the use of 28-day mortality or in-hospital mortality as end points for clinical studies may lead to inaccurate inferences&#46;<a class="elsevierStyleCrossRef" href="#bib0235"><span class="elsevierStyleSup">11</span></a> Shankar-Hari and Rubenfeld assert that in the first year following a sepsis episode&#44; approximately 60&#37; of sepsis survivors have at least one rehospitalization episode&#44; which is most often due to infection and one in six sepsis survivors die&#46;<a class="elsevierStyleCrossRef" href="#bib0240"><span class="elsevierStyleSup">12</span></a> In 2007 Yende et al&#46; insure that long-term mortality following severe sepsis is high&#44; and fewer than half of patients who experience severe sepsis are alive at 1 year&#46;<a class="elsevierStyleCrossRef" href="#bib0245"><span class="elsevierStyleSup">13</span></a> In a different study published in 2016 Yende et al&#46; studied the long-Term Quality of Life Among Survivors of Severe Sepsis and concluded that&#44; among individuals enrolled in the clinical trial who lived independently prior to severe sepsis&#44; one third had died and of those who survived&#44; a further one third had not returned to independent living by 6 months&#46;<a class="elsevierStyleCrossRef" href="#bib0250"><span class="elsevierStyleSup">14</span></a></p><p id="par0030" class="elsevierStylePara elsevierViewall">According to all of the above&#44; the main objective of this study is to develop a model that goes beyond the prediction of in-hospital mortality&#44; for this reason&#44; this paper presents the development of a model for the 1-year mortality prediction of sepsis diagnosed patients in an ICU that outperforms commonly used severity-of-disease classification systems&#46; This model would help identify those patients at greatest risk&#44; and will be the first step to detect signs of alarm from a worse outcome beyond the hospital discharge&#46;</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Methods and procedures</span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Data base</span><p id="par0035" class="elsevierStylePara elsevierViewall">For this study we used MIMIC-III &#40;Medical Information Mart for Intensive Care&#41; database&#46; It is the latest version of MIMIC&#44; an open database &#40;<a href="https://mimic.physionet.org/">https&#58;&#47;&#47;mimic&#46;physionet&#46;org</a>&#41;&#44; and the third version was published in November 2015&#46; MIMIC-III provides demographic information&#44; vital signs measures&#44; laboratory test results&#44; drug information&#44; procedures&#44; fluid balance&#44; length of stay and mortality both inside and outside the medical center&#46; MIMIC-III uses the Social Security Administration Death Master File to obtain the Out-of-hospital mortality dates&#46;<a class="elsevierStyleCrossRef" href="#bib0255"><span class="elsevierStyleSup">15</span></a> MIMIC-III contains data associated with 58&#44;977 different hospital admissions for 46&#44;520 patients over 16 years old admitted to the ICU at Beth Israel Medical Center in Boston&#44; United States between 2001 and 2012&#46;<a class="elsevierStyleCrossRefs" href="#bib0255"><span class="elsevierStyleSup">15&#8211;17</span></a></p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Sepsis criteria</span><p id="par0040" class="elsevierStylePara elsevierViewall">Traditionally sepsis has been linked to a systemic inflammatory response syndrome in response to an infectious process&#44; and presented in three stages&#58; sepsis&#44; severe sepsis and septic shock&#46; However&#44; recently the Third International Consensus Definitions for Sepsis and Septic Shock has recommended the elimination of the terms sepsis syndrome&#44; septicemia&#44; and severe sepsis and instead defined sepsis as &#8220;life-threatening organ dysfunction due to a dysregulated host response to infection&#8221;&#46; The consensus&#44; also&#44; proposed replacing the Systemic inflammatory response syndrome &#40;SIRS&#41; as diagnostic tool&#44; and substituting with SOFA for encounters in the ICU&#44; as an indicator of organ dysfunction that helps predict a poor prognosis in patients&#46;<a class="elsevierStyleCrossRefs" href="#bib0270"><span class="elsevierStyleSup">18&#8211;20</span></a></p><p id="par0045" class="elsevierStylePara elsevierViewall">In spite of the rigor of the methodology used by the consensus&#44; currently&#44; there remains some controversy around the new definitions&#44;<a class="elsevierStyleCrossRefs" href="#bib0285"><span class="elsevierStyleSup">21&#8211;23</span></a> since the new definitions did not involve low or middle income countries&#44; and SOFA is a score that is routinely calculated in some&#44; but not all&#44; ICUs&#46; Even the experts in sepsis pathobiology of the third international consensus recognized some limitations since some of the definitions and clinical criteria were generated through voting&#44; and unanimity was not always presented&#46;<a class="elsevierStyleCrossRef" href="#bib0275"><span class="elsevierStyleSup">19</span></a></p><p id="par0050" class="elsevierStylePara elsevierViewall">Seeking to follow the definition of the consensus&#44; but without forgetting the doubts regarding the new way of doing the diagnosis&#44; we used the Angus criteria in this study to identify ICU patients with sepsis<a class="elsevierStyleCrossRef" href="#bib0300"><span class="elsevierStyleSup">24</span></a>&#59; therefore&#44; from the 58&#44;977 MIMIC-III admissions&#44; all the ones that complying with the following&#58; &#40;i&#41; ICD-9-CM codes for both a bacterial or fungal infections and a diagnosis of acute organ dysfunction were selected and &#40;ii&#41; explicit sepsis related diagnosis&#58; severe sepsis or septic shock&#46; 15&#44;254 admissions were obtained&#46;</p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0105">Working dataset and <span class="elsevierStyleItalic">exclusion criteria</span></span><p id="par0055" class="elsevierStylePara elsevierViewall">The working dataset was extracted from the 15&#44;254 admissions with a diagnosis of sepsis according to the Angus criteria&#59; First&#44; we selected the admissions of patients aged 16 or older with stays longer than 24 h&#44; resulting in a dataset with 13&#44;836 patients&#46; Then&#44; only the admissions that had at least 70&#37; of the laboratory measurements and at least 70&#37; of routine charted data presented in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a> were included in the working dataset&#44; getting 5650 admissions &#40;with a 1-year mortality rate of 43&#46;3&#37;&#41;&#46;</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><p id="par0060" class="elsevierStylePara elsevierViewall">The data listed in the first two segments of <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a> were extracted during the first 24<span class="elsevierStyleHsp" style=""></span>h of each admission&#46; Since the variables are not measured with the same frequency&#44; we calculated statistical indices that allowed their description&#58; mean&#44; maximum&#44; minimum&#44; variance and range&#46; <a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a> presents two of the variables as an example&#44; the 24 h time window is also shown&#46;</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><p id="par0065" class="elsevierStylePara elsevierViewall">The data listed in the last three segments of <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a> represent single values throughout the entire duration of a patient admission&#59; therefore&#44; they do not require indicators for their description&#46;</p><p id="par0070" class="elsevierStylePara elsevierViewall">Of all variables listed in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>&#44; only four presented more than 5&#37; of missing data being bilirubin the most critical with 34&#37; of absent values&#44; followed by Fraction of Inspired O2 with 15&#37;&#44; Lactate with 13&#37; and Base excess with 7&#37;&#46; The SGB algorithm used for the development of the model is based on decision trees&#44; so it is possible to handle missing values without using imputation&#46;<a class="elsevierStyleCrossRef" href="#bib0305"><span class="elsevierStyleSup">25</span></a></p><p id="par0075" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#44; presents the description of the admissions selected as study cohort by first care unit type&#59; it is evident the sensitivity of the condition of patients with sepsis&#44; since&#44; when compared to the general MIMIC-III population&#44;<a class="elsevierStyleCrossRef" href="#bib0255"><span class="elsevierStyleSup">15</span></a> they present a longer length of stays &#40;both ICU and hospital&#41; and higher in-hospital mortality&#46; <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#44; also shows the 1-year mortality which is almost twice the hospital mortality&#46;</p><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><p id="par0080" class="elsevierStylePara elsevierViewall">The study cohort&#44; containing 5650 admissions&#44; was randomly divided into two groups&#58; a train subset with 3955 admissions &#40;70&#37; of the working set&#41;&#44; and a validation subset of 1695 admissions&#46; <a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 2</a> presents the accrual of admissions included in the study cohort&#46;</p><elsevierMultimedia ident="fig0010"></elsevierMultimedia></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0110">Model development</span><p id="par0085" class="elsevierStylePara elsevierViewall">The data listed in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>&#44; were converted into 140 predictors&#44; 115 of which were the statistical descriptions of the laboratory measurements and the routine charted data&#44; 20 were the presences of comorbidities and organ dysfunctions&#44; two were the numerical values for age and Glasgow Coma Score &#40;GCS&#41;&#44; and 3 corresponded to the gender and admission type categorical data&#44; since each of these variables were binarized using one hot encoding&#46;</p><p id="par0090" class="elsevierStylePara elsevierViewall">To select the most important predictors for the 1-year mortality prediction model two techniques were used&#59; the first one was Least Absolute Shrinkage and Selection Operator &#40;LASSO&#41; based on maximum likelihood logistic regression&#59; for this methodology mean imputation was used&#46; The second technique was based on Stochastic Gradient Boosting &#40;SGB&#41; variable importance&#44; a procedure that indicates the contributions of each of the predictors to the model&#44; therefore it is possible to choose the most relevant predictors that represent the majority of the performance on the model&#46;</p><p id="par0095" class="elsevierStylePara elsevierViewall">Stochastic gradient boosting &#40;SGB&#41; is a type of ensemble algorithm&#46; An ensemble algorithms consist of multiple base models &#40;Small decision trees for SGB&#41;&#44; each one of those provides a different solution to the problem&#59; The solutions of all the base models&#44; are finally combined &#40;usually by weighted voting or averaging&#41; into a single final model output&#44; which is usually a more stable and accurate prediction&#46; The SGB algorithm involves a parameter-tuning process&#46; the three main parameters are&#58; <span class="elsevierStyleItalic">M</span>&#44; the number of trees that are aggregated in the model&#59; <span class="elsevierStyleItalic">&#957;</span>&#44; the learning rate that helps to control over-fitting by controlling the gradient steps and <span class="elsevierStyleItalic">L</span>&#44; the number of splits performed on each Tree&#46;<a class="elsevierStyleCrossRefs" href="#bib0310"><span class="elsevierStyleSup">26&#8211;28</span></a> Least Absolute Shrinkage and Selection Operator &#40;LASSO&#41;&#44;<a class="elsevierStyleCrossRef" href="#bib0325"><span class="elsevierStyleSup">29</span></a> is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the statistical model&#46; LASSO puts a constraint on the sum of the absolute values of the model parameters&#44; applying a regularization process where it penalizes the coefficients of the regression variables and set some of them exactly to zero&#46; In practice there is tuning parameter <span class="elsevierStyleItalic">&#955;</span>&#44; that controls the controls the amount of shrinkage that is applied to the estimates&#46; The SGB and LASSO models were implemented with R-packages&#46;<a class="elsevierStyleCrossRefs" href="#bib0330"><span class="elsevierStyleSup">30&#8211;32</span></a> A detailed description of the used methodologies and the parameters tuning process is found in the supplementary material&#46;</p><p id="par0100" class="elsevierStylePara elsevierViewall">After the predictors were selected with both methods five SGB models were developed&#44; two with the predictors selected with each of the methods&#44; one with the intersection of the predictors&#44; one with the union of the predictors and one with all the predictors&#46; To assess performance in the five SGB models developed with the different sets of predictors and the three severity-of-disease classification systems &#40;SOFA&#44; SAPS II and OASIS&#41; we calculate the Area Under an ROC Curve &#40;AUROC&#41; using the PRROC R package&#46;<a class="elsevierStyleCrossRef" href="#bib0345"><span class="elsevierStyleSup">33</span></a> This was repeated for 5000 random samples of size 320 to generate the distributions of metrics shown in a comparison boxplot&#46; The goodness of fit of the proposed SGB models was evaluated over the entire validation subset using the Pearson&#39;s Chi-square test&#44; to measure the discrepancy between the observed and the predicted mortality distribution&#59; and the Hosmer&#8211;Lemeshow Test was used to assess whether or not the observed event rates match predicted event rates in subgroups of increasing probability of the outcome&#46; <a class="elsevierStyleCrossRef" href="#fig0015">Fig&#46; 3</a> illustrates the methodology that was followed to develop the model&#46;</p><elsevierMultimedia ident="fig0015"></elsevierMultimedia></span></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0115">Results</span><p id="par0105" class="elsevierStylePara elsevierViewall">For the model based on all the predictors the parameters that presented a better AUROC and an adequate calibration were <span class="elsevierStyleItalic">M</span> &#40;number of trees&#41;<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>1150&#44; <span class="elsevierStyleItalic">L</span> &#40;number of splits on each tree&#41;<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>9 and <span class="elsevierStyleItalic">&#957;</span> &#40;learning rate&#41;<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;01&#46; The predictive power of the SGB model with all the variables over the 1695 admissions of the validation subset were evaluated and an AUROC of 0&#46;8039 &#40;95&#37; confidence interval &#40;CI&#41;&#58; &#91;0&#46;8033 0&#46;8045&#93;&#41; was obtained&#59; with this methodology the relative influence of each variable is scaled so that the sum adds 100&#44; with higher numbers indicating stronger influence on the response&#44; the 37 most important predictors for the 1-year mortality are presented in <a class="elsevierStyleCrossRef" href="#fig0020">Fig&#46; 4</a>&#46;</p><elsevierMultimedia ident="fig0020"></elsevierMultimedia><p id="par0110" class="elsevierStylePara elsevierViewall">After applying LASSO regularization process some of the coefficients of the regression are set exactly to zero&#46; <a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a> presents the LASSO selected predictors &#40;with shrinkage parameter <span class="elsevierStyleItalic">&#955;</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;01288503&#41;&#46; For the SGB model with the intersection variables &#40;18 predictors&#41; the AUROC was 0&#46;792 &#40;95&#37; confidence interval &#40;CI&#41;&#58; &#91;0&#46;791 0&#46;793&#93;&#41;&#46; The other three SGB models were not different in their performance to the model with all the variables&#46;</p><elsevierMultimedia ident="tbl0015"></elsevierMultimedia><p id="par0115" class="elsevierStylePara elsevierViewall">To benchmark the proposed SGB models&#44; three severity-of-disease classification systems were used to evaluate the 1-year mortality on the same validation subset&#46; The AUROC values for the reference scores were&#58; OASIS 0&#46;631 &#40;95&#37; confidence interval &#40;CI&#41;&#58; &#91;0&#46;630&#8211;0&#46;632&#93;&#41;&#44; SOFA 0&#46;588 &#40;95&#37; confidence interval &#40;CI&#41;&#58; &#91;0&#46;587&#8211;0&#46;589&#93;&#41; and SAPS2 0&#46;702 &#40;95&#37; confidence interval &#40;CI&#41;&#58; &#91;0&#46;701&#8211;0&#46;703&#93;&#41;&#46; <a class="elsevierStyleCrossRef" href="#fig0025">Fig&#46; 5</a> presents the box plots of the AUROC and the accuracy of the three reference severity of disease scores and the SGB models with all the predictors and the intersection predictors&#46;</p><elsevierMultimedia ident="fig0025"></elsevierMultimedia><p id="par0120" class="elsevierStylePara elsevierViewall">The calibration of the proposed SGB models was adequate with <span class="elsevierStyleItalic">p</span>-values of 0&#46;0916 and 0&#46;127 for the model with all the variables and the model with the intersection variables respectively&#46; Goodness of fit was also adequate &#40;<span class="elsevierStyleItalic">p</span>-values for all variables model&#58; 0&#46;1857 and <span class="elsevierStyleItalic">p</span>-values for intersection model&#58; 0&#46;9219&#41;&#46;</p><p id="par0125" class="elsevierStylePara elsevierViewall">For SGB model with the intersection predictors&#44; observed versus predicted of numbers of deaths were compared graphically within deciles of increasing probability of the 1-year mortality &#40;<a class="elsevierStyleCrossRef" href="#fig0030">Fig&#46; 6</a>&#41;&#44; and it is observed that estimated and observed mortality pairs are similar and shows that the number of outcome events is indeed increasing along the probability deciles&#46; The relative importance of the 18 predictors of the intersection models are listed in <a class="elsevierStyleCrossRef" href="#tbl0020">Table 4</a>&#46; These predictors allow to identify features that could become prognostic markers for the 1-year mortality of the sepsis diagnosed patients within the ICU and could be an input for a new severity score for patients with sepsis in the ICU&#46;</p><elsevierMultimedia ident="fig0030"></elsevierMultimedia><elsevierMultimedia ident="tbl0020"></elsevierMultimedia></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0120">Discussion and conclusions</span><p id="par0130" class="elsevierStylePara elsevierViewall">Accuracy and AUROC analysis over the validation data indicate that custom mortality prediction models for a specific disease presents a better performance that traditional scores&#44; which could lead to better management of illness within the ICU&#46; Accuracy and AUROC analysis also ratify the complex interdependence among different physiological systems in response to sepsis&#44; because the SGB models are composed of between 450 and 1150 trees&#44; which are difficult to interpret&#59; therefore&#44; it is necessary to develop easy-to-use computer tools that allow these types of models to be implemented within the ICU&#46; This study indicates that it is possible to generate a specific model for the prediction of mortality of patients admitted to an ICU with a diagnosis of sepsis&#44; that includes variables that are now commonly evaluated and been widely used&#46;</p><p id="par0135" class="elsevierStylePara elsevierViewall">SGB variable importance and LASSO methodologies allowed to develop models that preserve the same performance as the one generated with all the predictors but with less predictors&#46; Also the intersection of the predictors selected by the two methods leads to the development of a much simpler model with only 18 predictors and less trees&#44; which also presents good performance&#46;</p><p id="par0140" class="elsevierStylePara elsevierViewall">As expected&#44; older patients are at greater risk in consequence the most important parameter for the outcome is the age&#46; Urine output is used as a marker of acute kidney injury&#44; a disease that is associated with substantial in-hospital mortality&#44; beside this&#44; it is important to note that it is a relatively simple and widely used variable in the ICU that has a high relevance in predicting 1-year mortality with the SGB methodology&#46;</p><p id="par0145" class="elsevierStylePara elsevierViewall">Minimum lactate over the first 24<span class="elsevierStyleHsp" style=""></span>h of the ICU admission is the ninth most important variable for the outcome prediction in this study&#59; Lactate is currently used within the ICU as a diagnostic tool and as a prognostic marker&#44; since the higher the value&#44; the greater the risk of mortality&#46; However&#44; if the lactate of a patient does not reach below a threshold&#44; it will also have a higher mortality risk&#46; For this reason&#44; the minimum lactate during the first 24<span class="elsevierStyleHsp" style=""></span>h must also be analyzed in ICUs&#46; Mean lactate is also considered an important predictor&#44; which agrees with what is reported in the literature&#44; since hyperlactatemia is related with a poor outcome in ICU&#46;<a class="elsevierStyleCrossRef" href="#bib0350"><span class="elsevierStyleSup">34</span></a> An elevated blood urea nitrogen &#40;BUN&#41; is associated with increased mortality in critically ill patients&#46;<a class="elsevierStyleCrossRef" href="#bib0355"><span class="elsevierStyleSup">35</span></a></p><p id="par0150" class="elsevierStylePara elsevierViewall">The main objective of this work is to present a model for the 1-year mortality prediction of the patients that are admitted in a ICU with a sepsis diagnosis&#59; and shows that the use of ensemble based algorithms &#40;SGB in this study&#41; and the inclusion of statistical descriptors that are not usually taken into account in the traditional severity-of-disease classification systems &#40;for example mean&#44; minimum and maximum values of the same variable&#41;&#44; improves the performance of the prediction of prognosis models in patients admitted to an ICU with diagnosis of sepsis&#44; however&#44; this means that the model can only be used after the first 24<span class="elsevierStyleHsp" style=""></span>h of observation&#46;</p><p id="par0155" class="elsevierStylePara elsevierViewall">Other limitations of this study include the fact that it is based on the data taken at only one institution&#44; however&#44; despite the limitation of being single-centered&#44; the main advantages of MIMIC-III are that&#44; right now&#44; it is the only freely accessible critical care database of its kind&#44; the dataset spans more than a decade and it has detailed information about individual patient care that includes time-stamped nurse-verified physiological measurements&#46; For this reasons MIMIC-III &#40;and specially it previous version MIMIC-II&#41; are widely used internationally&#46; For this study in particular&#44; an important advantage is that besides in-hospital mortality&#44; MIMIC-III provides Out-of-hospital mortality dates through the Social Security Administration Death Master File&#46; On the other hand&#44; there have been few validated methods of medical record data extraction for estimating sepsis&#44; particularly in this work&#44; the Angus criteria was used&#44; which is one of the first protocols using administrative data&#44; and was validated by comparing a nurse-driven identification of a population of patients with the clinical syndrome of sepsis&#44; however&#44; Angus criteria has shown to be capable of capture most of the patients with severe sepsis but not exclusively and cohorts identified by different methodologies &#40;for instances Angus criteria and Martin Criteria&#41; yielded widely different patient groups&#46;<a class="elsevierStyleCrossRef" href="#bib0360"><span class="elsevierStyleSup">36</span></a></p><p id="par0205" class="elsevierStylePara elsevierViewall">It is also important to note that the criteria used to select patients is based on the ICD codes&#44; which in MIMIC-III are generated for billing purposes at the end of the hospital stay&#44; hence it does not guarantee that patients suffer from sepsis at the time of admission&#44; even so&#44; we evaluate the data of the first 24 hours after admission since we considered that the majority of patients are usually already infected at ICU admission&#46;</p><p id="par0160" class="elsevierStylePara elsevierViewall">The emergence of machine learning techniques in the field of health is a fact&#46; Specifically&#44; in the field of Intensive Care&#44; it is undeniable that the potential for its application is immense&#46; Specifically&#44; the use of assembly algorithms&#44; and in particular the SGB&#44; allows the development of prediction models&#44; which despite being complex show significantly better discrimination than traditional severity of disease scores &#40;Like OASIS&#44; SAPS II or SOFA&#41;&#46; This could be explained by the fact that the base models in the SGB algorithm are not fitted independently&#44; but sequentially&#44; this mean that the subsequent predictors are based on the results of previous predictors&#44; moreover&#44; SGB is based on a steepest gradient algorithm which places emphasis on misclassified training data that are close to their correct classification&#44; which reduces the number of misclassified observations and facilitates mortality prediction models to be used at the individual level&#46;</p></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0125">Author&#39;s contribution</span><p id="par0165" class="elsevierStylePara elsevierViewall">Javier E&#46; Garc&#237;a-Gallo contributed in the concepts&#44; design&#44; definition of intellectual content&#44; literature search&#44; experimental studies&#44; data acquisition&#44; data analysis&#44; statistical analysis&#44; manuscript preparation&#44; manuscript editing&#44; manuscript review&#46;</p><p id="par0170" class="elsevierStylePara elsevierViewall">Nelson J&#46; Fonseca-Ruiz contributed in the concepts&#44; design&#44; definition of intellectual content&#44; literature search&#44; data analysis&#44; manuscript editing&#44; manuscript review&#46;</p><p id="par0175" class="elsevierStylePara elsevierViewall">Leo Anthony Celi contributed in the concepts&#44; design&#44; definition of intellectual content&#44; literature search&#44; manuscript preparation&#44; manuscript editing&#44; manuscript review&#46;</p><p id="par0180" class="elsevierStylePara elsevierViewall">John F&#46; Duitama-Mu&#241;oz contributed in the concepts&#44; design&#44; definition of intellectual content&#44; data analysis&#44; manuscript preparation&#44; manuscript editing&#44; manuscript review&#46;</p></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0130">Conflicts of interest</span><p id="par0185" class="elsevierStylePara elsevierViewall">The authors have no conflicts of interest to declare&#46;</p></span></span>"
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          "identificador" => "sec0050"
          "titulo" => "Conflicts of interest"
        ]
        10 => array:2 [
          "identificador" => "xack455721"
          "titulo" => "Acknowledgements"
        ]
        11 => array:1 [
          "titulo" => "References"
        ]
      ]
    ]
    "pdfFichero" => "main.pdf"
    "tienePdf" => true
    "fechaRecibido" => "2018-04-09"
    "fechaAceptado" => "2018-07-25"
    "PalabrasClave" => array:2 [
      "en" => array:1 [
        0 => array:4 [
          "clase" => "keyword"
          "titulo" => "Keywords"
          "identificador" => "xpalclavsec1218927"
          "palabras" => array:5 [
            0 => "Prognosis prediction"
            1 => "Sepsis"
            2 => "Stochastic gradient boosting"
            3 => "Intensive care unit"
            4 => "Least absolute shrinkage and selection operator"
          ]
        ]
      ]
      "es" => array:1 [
        0 => array:4 [
          "clase" => "keyword"
          "titulo" => "Palabras clave"
          "identificador" => "xpalclavsec1218928"
          "palabras" => array:5 [
            0 => "Predicci&#243;n de pron&#243;stico"
            1 => "Sepsis"
            2 => "Stochastic Gradient Boosting"
            3 => "Unidad de Cuidados Intensivos"
            4 => "Least Absolute Shrinkage and Selection Operator"
          ]
        ]
      ]
    ]
    "tieneResumen" => true
    "resumen" => array:2 [
      "en" => array:3 [
        "titulo" => "Abstract"
        "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Introduction</span><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Sepsis is associated to a high mortality rate&#44; and its severity must be evaluated quickly&#46; The severity of illness scores used are intended to be applicable to all patient populations&#44; and generally evaluate in-hospital mortality&#46; However&#44; patients with sepsis continue to be at risk of death after hospital discharge&#46;</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Objective</span><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">To develop a model for predicting 1-year mortality in critical patients diagnosed with sepsis&#46;</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Patients</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">The data corresponding to 5650 admissions of patients with sepsis from the Medical Information Mart for Intensive Care &#40;MIMIC-III&#41; database were evaluated&#44; randomly divided as follows&#58; 70&#37; for training and 30&#37; for validation&#46;</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Design</span><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">A retrospective register-based cohort study was carried out&#46; The clinical information of the first 24<span class="elsevierStyleHsp" style=""></span>h after admission was used to develop a 1-year mortality prediction model based on Stochastic Gradient Boosting &#40;SGB&#41; methodology&#46; Variable selection was addressed using Least Absolute Shrinkage and Selection Operator &#40;LASSO&#41; and SGB variable importance methodologies&#46; The predictive power was evaluated using the area under the ROC curve &#40;AUROC&#41;&#46;</p></span> <span id="abst0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0030">Results</span><p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">An AUROC of 0&#46;8039 &#40;95&#37; confidence interval &#40;CI&#41;&#58; &#91;0&#46;8033 0&#46;8045&#93;&#41; was obtained in the validation subset&#46; The model exceeded the predictive performances obtained with traditional severity of disease scores in the same subset&#46;</p></span> <span id="abst0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0035">Conclusion</span><p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">The use of assembly algorithms&#44; such as SGB&#44; for the generation of a customized model for sepsis yields more accurate 1-year mortality prediction than the traditional scoring systems such as SAPS II&#44; SOFA or OASIS&#46;</p></span>"
        "secciones" => array:6 [
          0 => array:2 [
            "identificador" => "abst0005"
            "titulo" => "Introduction"
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          1 => array:2 [
            "identificador" => "abst0010"
            "titulo" => "Objective"
          ]
          2 => array:2 [
            "identificador" => "abst0015"
            "titulo" => "Patients"
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          3 => array:2 [
            "identificador" => "abst0020"
            "titulo" => "Design"
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          4 => array:2 [
            "identificador" => "abst0025"
            "titulo" => "Results"
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          5 => array:2 [
            "identificador" => "abst0030"
            "titulo" => "Conclusion"
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      "es" => array:3 [
        "titulo" => "Resumen"
        "resumen" => "<span id="abst0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Introducci&#243;n</span><p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">La sepsis conlleva una elevada mortalidad&#44; y su gravedad debe evaluarse r&#225;pidamente&#46; Los sistemas utilizados para clasificar la intensidad de la enfermedad pretenden ser aplicables a todos los pacientes&#44; y generalmente eval&#250;an la mortalidad intrahospitalaria&#46; Sin embargo&#44; los pacientes con sepsis contin&#250;an estando en riesgo de muerte despu&#233;s del alta hospitalaria&#46;</p></span> <span id="abst0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">Objetivo</span><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">Desarrollar un modelo para la predicci&#243;n de la mortalidad a un a&#241;o de pacientes en UCI con diagn&#243;stico de sepsis&#46;</p></span> <span id="abst0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0055">Pacientes</span><p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Se evaluaron los datos de 5650 admisiones de pacientes con sepsis de la base de datos Medical Information Mart for Intensive Care &#40;MIMIC-III&#41;&#44; los cuales fueron divididos aleatoriamente as&#237;&#58; 70&#37; para entrenamiento y 30&#37; para validaci&#243;n&#46;</p></span> <span id="abst0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0060">Dise&#241;o</span><p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">Estudio retrospectivo de cohorte basado en registros&#46; Se utiliz&#243; la informaci&#243;n cl&#237;nica de las primeras 24 horas despu&#233;s de la admisi&#243;n para desarrollar un modelo de predicci&#243;n de mortalidad a un a&#241;o basado en la metodolog&#237;a Stochastic Gradient Boosting &#40;SGB&#41;&#46; La selecci&#243;n de variables se abord&#243; utilizando las metodolog&#237;as Least Absolute Shrinkage and Selection Operator &#40;LASSO&#41; e importancia de variables por SGB&#46; El poder predictivo del modelo fue evaluado usando el AUROC&#46;</p></span> <span id="abst0055" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0065">Resultados</span><p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">Se obtuvo un AUROC de 0&#46;8039 &#40;intervalo de confianza &#91;IC&#93; del 95&#37;&#58; &#91;0&#46;8033-0&#46;8045&#93;&#41;&#46; El modelo supera los resultados obtenidos con algunos puntajes tradicionales en el mismo subconjunto de validaci&#243;n&#46;</p></span> <span id="abst0060" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Conclusi&#243;n</span><p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">El uso de algoritmos de ensamblaje&#44; como SGB&#44; para la generaci&#243;n de un modelo adaptado para la sepsis&#44; proporcionan estimaciones de mortalidad a un a&#241;o m&#225;s precisas que los sistemas de puntuaci&#243;n tradicionales como SAPS II&#44; SOFA u OASIS&#46;</p></span>"
        "secciones" => array:6 [
          0 => array:2 [
            "identificador" => "abst0035"
            "titulo" => "Introducci&#243;n"
          ]
          1 => array:2 [
            "identificador" => "abst0040"
            "titulo" => "Objetivo"
          ]
          2 => array:2 [
            "identificador" => "abst0045"
            "titulo" => "Pacientes"
          ]
          3 => array:2 [
            "identificador" => "abst0050"
            "titulo" => "Dise&#241;o"
          ]
          4 => array:2 [
            "identificador" => "abst0055"
            "titulo" => "Resultados"
          ]
          5 => array:2 [
            "identificador" => "abst0060"
            "titulo" => "Conclusi&#243;n"
          ]
        ]
      ]
    ]
    "apendice" => array:1 [
      0 => array:1 [
        "seccion" => array:1 [
          0 => array:4 [
            "apendice" => "<p id="par0200" class="elsevierStylePara elsevierViewall"><elsevierMultimedia ident="upi0005"></elsevierMultimedia></p>"
            "etiqueta" => "Appendix A"
            "titulo" => "Supplementary data"
            "identificador" => "sec0060"
          ]
        ]
      ]
    ]
    "multimedia" => array:11 [
      0 => array:7 [
        "identificador" => "fig0005"
        "etiqueta" => "Figure 1"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr1.jpeg"
            "Alto" => 1299
            "Ancho" => 2377
            "Tamanyo" => 133496
          ]
        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0065" class="elsevierStyleSimplePara elsevierViewall">Example variables&#46; The box represents the 24-h window in which the data are extracted and evaluated&#46;</p>"
        ]
      ]
      1 => array:7 [
        "identificador" => "fig0010"
        "etiqueta" => "Figure 2"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr2.jpeg"
            "Alto" => 1401
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            "Tamanyo" => 118980
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        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">Accrual of admissions included in the study cohort&#46;</p>"
        ]
      ]
      2 => array:7 [
        "identificador" => "fig0015"
        "etiqueta" => "Figure 3"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr3.jpeg"
            "Alto" => 1580
            "Ancho" => 2833
            "Tamanyo" => 338957
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        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0075" class="elsevierStyleSimplePara elsevierViewall">Model development&#46; Stochastic gradient boosting &#40;SGB&#41; tuning parameters&#58; <span class="elsevierStyleItalic">M</span>&#44; the number of trees that are aggregated in the model&#59; <span class="elsevierStyleItalic">&#957;</span>&#44; the learning rate and <span class="elsevierStyleItalic">L</span>&#44; the number of splits performed on each tree&#46; Least Absolute Shrinkage and Selection Operator &#40;LASSO&#41; tuning parameter&#58; <span class="elsevierStyleItalic">&#955;</span>&#44; controls the controls the amount of shrinkage that is applied to the estimates&#46;</p>"
        ]
      ]
      3 => array:7 [
        "identificador" => "fig0020"
        "etiqueta" => "Figure 4"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr4.jpeg"
            "Alto" => 1286
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            "Tamanyo" => 188204
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        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0080" class="elsevierStyleSimplePara elsevierViewall">SGB relative importance of the predictors for the 1-year mortality prediction model&#46; Abbreviations&#58; Bun&#58; blood urea nitrogen&#59; Max&#58; maximum&#59; WBC&#58; white blood cell&#59; Min&#58; minimum&#59; SpO<span class="elsevierStyleInf">2</span>&#58; peripheral capillary oxygen saturation&#59; PaO<span class="elsevierStyleInf">2</span>&#47;FiO<span class="elsevierStyleInf">2</span>&#58; partial pressure arterial oxygen and fraction of inspired oxygen ratio&#59; FiO<span class="elsevierStyleInf">2</span>&#58; fraction of inspired oxygen&#59; Mechanical vent&#58; mechanical ventilation&#59; DABP&#58; diastolic arterial blood pressure&#59; SABP&#58; systolic arterial blood pressure&#59; MABP&#58; mean arterial blood pressure&#46;</p>"
        ]
      ]
      4 => array:7 [
        "identificador" => "fig0025"
        "etiqueta" => "Figure 5"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr5.jpeg"
            "Alto" => 2163
            "Ancho" => 2207
            "Tamanyo" => 127954
          ]
        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0085" class="elsevierStyleSimplePara elsevierViewall">Box plots of AUROC and accuracy for the 1-year mortality on the validation subset&#46; For the proposed SGB models with all variables &#40;140 predictors&#41;&#44; and the variables from the intersection between the SGB variable importance selected variables and the LASSO selected variables &#40;18 predictors&#41;&#46;</p>"
        ]
      ]
      5 => array:7 [
        "identificador" => "fig0030"
        "etiqueta" => "Figure 6"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr6.jpeg"
            "Alto" => 1195
            "Ancho" => 2172
            "Tamanyo" => 89826
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        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0090" class="elsevierStyleSimplePara elsevierViewall">Comparison of observed versus predicted 1-year mortality in the deciles of predicted mortality based on the SGB model with the intersection variables&#46;</p>"
        ]
      ]
      6 => array:8 [
        "identificador" => "tbl0005"
        "etiqueta" => "Table 1"
        "tipo" => "MULTIMEDIATABLA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "detalles" => array:1 [
          0 => array:3 [
            "identificador" => "at1"
            "detalle" => "Table "
            "rol" => "short"
          ]
        ]
        "tabla" => array:1 [
          "tablatextoimagen" => array:2 [
            0 => array:2 [
              "tabla" => array:1 [
                0 => """
                  <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" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Parameter&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">Unit&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" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="2" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleItalic">Laboratory measurements</span></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>Platelet count&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">10<span class="elsevierStyleSup">9</span>&#47;L&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"><span class="elsevierStyleHsp" style=""></span>Bilirubin&nbsp;\t\t\t\t\t\t\n
                  \t\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">mg&#47;dL&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"><span class="elsevierStyleHsp" style=""></span>Creatinine&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">mg&#47;dL&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"><span class="elsevierStyleHsp" style=""></span>Fraction of inspired oxygen&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#37;&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"><span class="elsevierStyleHsp" style=""></span>Partial pressure arterial oxygen and fraction of inspired oxygen ratio&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Ratio&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"><span class="elsevierStyleHsp" style=""></span>White blood cell count&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\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<span class="elsevierStyleSup">3</span>&#47;mm<span class="elsevierStyleSup">3</span>&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"><span class="elsevierStyleHsp" style=""></span>Potassium&nbsp;\t\t\t\t\t\t\n
                  \t\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">mEq&#47;L&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"><span class="elsevierStyleHsp" style=""></span>Sodium&nbsp;\t\t\t\t\t\t\n
                  \t\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">mEq&#47;L&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"><span class="elsevierStyleHsp" style=""></span>Bicarbonate&nbsp;\t\t\t\t\t\t\n
                  \t\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">mEq&#47;L&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"><span class="elsevierStyleHsp" style=""></span>Lactate&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">mg&#47;dL&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"><span class="elsevierStyleHsp" style=""></span>Arterial pH&nbsp;\t\t\t\t\t\t\n
                  \t\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">pH&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"><span class="elsevierStyleHsp" style=""></span>Hematocrit&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#37;&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"><span class="elsevierStyleHsp" style=""></span>Hemoglobin&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">mg&#47;dL&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"><span class="elsevierStyleHsp" style=""></span>Blood urea nitrogen&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">mg&#47;dL&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="2" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="2" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleItalic">Routine charted data</span></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>Temperature&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#176;C&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"><span class="elsevierStyleHsp" style=""></span>Heart rate&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Bpm&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"><span class="elsevierStyleHsp" style=""></span>Systolic arterial blood pressure&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">mmHg&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"><span class="elsevierStyleHsp" style=""></span>Diastolic arterial blood pressure&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">mmHg&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"><span class="elsevierStyleHsp" style=""></span>Mean arterial blood pressure&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">mmHg&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"><span class="elsevierStyleHsp" style=""></span>Urine output&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">mL&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"><span class="elsevierStyleHsp" style=""></span>Base excess&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">mEq&#47;L&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"><span class="elsevierStyleHsp" style=""></span>Glucose&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">mg&#47;dL&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"><span class="elsevierStyleHsp" style=""></span>Peripheral capillary oxygen saturation&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="2" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="2" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleItalic">Data taken at the time of ICU admission</span></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>Gender&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Female&#44; male&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " rowspan="2" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Admission type</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Medical&#44; scheduled surgical&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">Unscheduled surgical&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"><span class="elsevierStyleHsp" style=""></span>Age&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Years&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"><span class="elsevierStyleHsp" style=""></span>Glasgow Coma Scale&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Integer 3&#8211;15&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="2" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="2" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleItalic">Comorbidities</span></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>Diabetes&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " rowspan="10" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Binary &#40;presence&#41;</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>Immunosuppressive diseases&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"><span class="elsevierStyleHsp" style=""></span>Malignancy&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"><span class="elsevierStyleHsp" style=""></span>Hematologic malignancy&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"><span class="elsevierStyleHsp" style=""></span>Metastatic cancer&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"><span class="elsevierStyleHsp" style=""></span>Heart failure&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"><span class="elsevierStyleHsp" style=""></span>Pulmonary diseases&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"><span class="elsevierStyleHsp" style=""></span>Vascular diseases&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"><span class="elsevierStyleHsp" style=""></span>Coronary diseases&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"><span class="elsevierStyleHsp" style=""></span>Obesity&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
                  """
              ]
              "imagenFichero" => array:1 [
                0 => "xTab2264751.png"
              ]
            ]
            1 => array:2 [
              "tabla" => array:1 [
                0 => """
                  <table border="0" frame="\n
                  \t\t\t\t\tvoid\n
                  \t\t\t\t" class=""><tbody title="tbody"><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>Alcohol abuse&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " rowspan="4" align="left" valign="\n
                  \t\t\t\t\ttop\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"><span class="elsevierStyleHsp" style=""></span>Collagen diseases&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"><span class="elsevierStyleHsp" style=""></span>Drug abuse&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"><span class="elsevierStyleHsp" style=""></span>Malnutrition&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="2" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="2" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleItalic">Organ dysfunction</span></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>Cardiovascular&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " rowspan="6" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Binary &#40;presence&#41;</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>Neurologic&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"><span class="elsevierStyleHsp" style=""></span>Hepatic&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"><span class="elsevierStyleHsp" style=""></span>Hematologic&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"><span class="elsevierStyleHsp" style=""></span>Renal&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"><span class="elsevierStyleHsp" style=""></span>Mechanical ventilation&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
                  """
              ]
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                0 => "xTab2264752.png"
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        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0095" class="elsevierStyleSimplePara elsevierViewall">Extracted data from each admission&#46;</p>"
        ]
      ]
      7 => array:8 [
        "identificador" => "tbl0010"
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        "tipo" => "MULTIMEDIATABLA"
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          ]
        ]
        "tabla" => array:2 [
          "leyenda" => "<p id="spar0105" class="elsevierStyleSimplePara elsevierViewall">Abbreviations&#58; MIMIC-III&#58; medical information mart for intensive care&#44; ICU&#58; intensive care unit&#46;</p>"
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            0 => array:2 [
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                0 => """
                  <table border="0" frame="\n
                  \t\t\t\t\tvoid\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">MIMIC-III&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Medical ICU&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">Coronary care&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Cardiac surgery recovery&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\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Surgical trauma ICU&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">Total&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">Hospital admissions&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t\ttop\n
                  \t\t\t\t">735 &#40;13&#46;01&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">404 &#40;7&#46;15&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">608 &#40;10&#46;76&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">5650 &#40;100&#37;&#41;&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">Different ICU stays&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">3402 &#40;53&#46;64&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">934 &#40;14&#46;73&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">828 &#40;13&#46;06&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">483 &#40;7&#46;62&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">695 &#40;10&#46;96&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">6342 &#40;100&#37;&#41;&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">Age&#44; median years&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">67&#46;5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">64&#46;72&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">71&#46;75&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">70&#46;36&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">61&#46;63&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">67&#46;54&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">Gender &#40;masculine&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1642 &#40;52&#46;32&#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
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                  \t\t\t\t\ttop\n
                  \t\t\t\t">393 &#40;51&#46;37&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">406 &#40;55&#46;23&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">248 &#40;61&#46;38&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">395 &#40;64&#46;96&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">3084 &#40;54&#46;58&#37;&#41;&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">ICU length of stay&#44; median days&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">5&#46;06&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">6&#46;68&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">5&#46;81&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">8&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">7&#46;88&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">5&#46;9&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">Hospital length of stay&#44; median days&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">10&#46;29&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">14&#46;99&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">10&#46;63&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">15&#46;88&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">17&#46;13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">11&#46;88&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">Hospital mortality&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">757 &#40;24&#46;12&#37;&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">165 &#40;21&#46;56&#37;&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">168 &#40;22&#46;85&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">76 &#40;18&#46;81&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">111 &#40;18&#46;25&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1277 &#40;22&#46;6&#37;&#41;&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">One-year mortality&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
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                  \t\t\t\t">1459 &#40;46&#46;49&#37;&#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">301 &#40;39&#46;34&#37;&#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">346 &#40;47&#46;07&#37;&#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="char" valign="\n
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                  \t\t\t\t">161 &#40;39&#46;85&#37;&#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">183 &#40;30&#46;09&#37;&#41;&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t">2450 &#40;43&#46;36&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
                  """
              ]
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                0 => "xTab2264753.png"
              ]
            ]
          ]
        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0100" class="elsevierStyleSimplePara elsevierViewall">Description of the study cohort&#46;</p>"
        ]
      ]
      8 => array:8 [
        "identificador" => "tbl0015"
        "etiqueta" => "Table 3"
        "tipo" => "MULTIMEDIATABLA"
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        "detalles" => array:1 [
          0 => array:3 [
            "identificador" => "at3"
            "detalle" => "Table "
            "rol" => "short"
          ]
        ]
        "tabla" => array:1 [
          "tablatextoimagen" => array:1 [
            0 => array:2 [
              "tabla" => array:1 [
                0 => """
                  <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" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Predictor&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
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Haematologic malignancy&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">Metastatic cancer&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">Admission type&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">Gender&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">Age&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 maximum&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">Systolic arterial blood pressure minimum&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">Systolic arterial blood pressure maximum&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 minimum&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 maximum&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">Urine output&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 urea nitrogen maximum&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">White blood cell count maximum&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">Bilirubin maximum&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">Glasgow Coma Scale Minimum&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">Diastolic arterial blood pressure minimum&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">Base excess maximum&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">Fraction of inspired oxygen maximum&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">Glucose minimum&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">Peripheral capillary oxygen saturation minimum&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">Peripheral capillary oxygen saturation mean&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 maximum&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 range&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 maximum&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 minimum&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 mean&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">Platelet count maximum&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">Malignancy&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 failure&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">Vascular&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">Obesity&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">Alcohol abuse&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">Hypertension&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">Cardiovascular&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">Haematologic dysfunction&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">Renal dysfunction&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">Mechanical ventilation&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
                  """
              ]
              "imagenFichero" => array:1 [
                0 => "xTab2264750.png"
              ]
            ]
          ]
        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0110" class="elsevierStyleSimplePara elsevierViewall">LASSO selected predictors&#46;</p>"
        ]
      ]
      9 => array:8 [
        "identificador" => "tbl0020"
        "etiqueta" => "Table 4"
        "tipo" => "MULTIMEDIATABLA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "detalles" => array:1 [
          0 => array:3 [
            "identificador" => "at4"
            "detalle" => "Table "
            "rol" => "short"
          ]
        ]
        "tabla" => array:1 [
          "tablatextoimagen" => array:1 [
            0 => array:2 [
              "tabla" => array:1 [
                0 => """
                  <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" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Predictor&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
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Relative importance&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
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Age&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">17&#46;77&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">Urine output&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">16&#46;17&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 urea nitrogen maximum&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">7&#46;28&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">Metastatic cancer&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">5&#46;82&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 maximum&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">5&#46;03&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">Bilirubin maximum&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">5&#46;01&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 maximum&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">4&#46;98&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 mean&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">4&#46;38&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 minimum&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">4&#46;3&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 minimum&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">4&#46;1&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">Platelet count maximum&nbsp;\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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Original article
A machine learning-based model for 1-year mortality prediction in patients admitted to an Intensive Care Unit with a diagnosis of sepsis
Modelo para la predicción de la mortalidad a un año en pacientes ingresados en una unidad de cuidados intensivos con diagnóstico de sepsis
J.E. García-Galloa,
Autor para correspondencia
jesteban.garcia@udea.edu.co

Corresponding author.
, N.J. Fonseca-Ruizb,c, L.A. Celid, J.F. Duitama-Muñoza
a Engineering and Software Investigation Group, Universidad de Antioquia UdeA, Medellín, Colombia
b Critical and Intensive Care, Medellín Clinic, Medellín, Colombia
c Critical and Intensive Care Program, CES University, Medellín, Colombia
d Laboratory of Computational Physiology, Harvard-MIT Division of Health Sciences and Technology, Cambridge, USA
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have been created&#46;<a class="elsevierStyleCrossRefs" href="#bib0190"><span class="elsevierStyleSup">2&#44;3</span></a></p><p id="par0010" class="elsevierStylePara elsevierViewall">Sepsis is a life-threatening organ dysfunction due to a dysregulated host response to infection&#46; It is an important public health problem&#44; which generates high costs for the health system and carries a high morbidity and mortality &#40;in-hospital mortality ranged from 14&#46;7&#37; to 29&#46;9&#37; in the United States&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0200"><span class="elsevierStyleSup">4</span></a></p><p id="par0015" class="elsevierStylePara elsevierViewall">A model that takes into account the peculiarities of sepsis and identify sensitively and early poor patient&#39;s outcome&#44; could become a very useful tool to help the clinical group to understand the severity of the disease and could help to the generation of alerts that favor early onset of therapeutic measures&#44; thus helping to improve the prognosis of patients with sepsis admitted to an ICU&#46;</p><p id="par0020" class="elsevierStylePara elsevierViewall">The performance of mortality prediction systems in patients with suspected sepsis&#44; severe sepsis and septic shock have been evaluated in the ICU&#44;<a class="elsevierStyleCrossRef" href="#bib0205"><span class="elsevierStyleSup">5</span></a> customized versions for severe sepsis and septic shock of in-hospital mortality classification systems have also been developed&#44;<a class="elsevierStyleCrossRefs" href="#bib0205"><span class="elsevierStyleSup">5&#44;6</span></a> and even particular models have been created for in hospital mortality prediction of ICU patients with sepsis&#44; severe sepsis and septic shock&#46;<a class="elsevierStyleCrossRefs" href="#bib0215"><span class="elsevierStyleSup">7&#8211;10</span></a></p><p id="par0025" class="elsevierStylePara elsevierViewall">Cited works report better performance than traditionally severity of disease scores and tend to focus on the prediction of in-hospital mortality&#44; however&#44; long-term outcomes from sepsis are poorly understood&#46; Winters et al&#46; concluded that patients with sepsis have ongoing mortality beyond short-term&#44; so the use of 28-day mortality or in-hospital mortality as end points for clinical studies may lead to inaccurate inferences&#46;<a class="elsevierStyleCrossRef" href="#bib0235"><span class="elsevierStyleSup">11</span></a> Shankar-Hari and Rubenfeld assert that in the first year following a sepsis episode&#44; approximately 60&#37; of sepsis survivors have at least one rehospitalization episode&#44; which is most often due to infection and one in six sepsis survivors die&#46;<a class="elsevierStyleCrossRef" href="#bib0240"><span class="elsevierStyleSup">12</span></a> In 2007 Yende et al&#46; insure that long-term mortality following severe sepsis is high&#44; and fewer than half of patients who experience severe sepsis are alive at 1 year&#46;<a class="elsevierStyleCrossRef" href="#bib0245"><span class="elsevierStyleSup">13</span></a> In a different study published in 2016 Yende et al&#46; studied the long-Term Quality of Life Among Survivors of Severe Sepsis and concluded that&#44; among individuals enrolled in the clinical trial who lived independently prior to severe sepsis&#44; one third had died and of those who survived&#44; a further one third had not returned to independent living by 6 months&#46;<a class="elsevierStyleCrossRef" href="#bib0250"><span class="elsevierStyleSup">14</span></a></p><p id="par0030" class="elsevierStylePara elsevierViewall">According to all of the above&#44; the main objective of this study is to develop a model that goes beyond the prediction of in-hospital mortality&#44; for this reason&#44; this paper presents the development of a model for the 1-year mortality prediction of sepsis diagnosed patients in an ICU that outperforms commonly used severity-of-disease classification systems&#46; This model would help identify those patients at greatest risk&#44; and will be the first step to detect signs of alarm from a worse outcome beyond the hospital discharge&#46;</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0090">Methods and procedures</span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Data base</span><p id="par0035" class="elsevierStylePara elsevierViewall">For this study we used MIMIC-III &#40;Medical Information Mart for Intensive Care&#41; database&#46; It is the latest version of MIMIC&#44; an open database &#40;<a href="https://mimic.physionet.org/">https&#58;&#47;&#47;mimic&#46;physionet&#46;org</a>&#41;&#44; and the third version was published in November 2015&#46; MIMIC-III provides demographic information&#44; vital signs measures&#44; laboratory test results&#44; drug information&#44; procedures&#44; fluid balance&#44; length of stay and mortality both inside and outside the medical center&#46; MIMIC-III uses the Social Security Administration Death Master File to obtain the Out-of-hospital mortality dates&#46;<a class="elsevierStyleCrossRef" href="#bib0255"><span class="elsevierStyleSup">15</span></a> MIMIC-III contains data associated with 58&#44;977 different hospital admissions for 46&#44;520 patients over 16 years old admitted to the ICU at Beth Israel Medical Center in Boston&#44; United States between 2001 and 2012&#46;<a class="elsevierStyleCrossRefs" href="#bib0255"><span class="elsevierStyleSup">15&#8211;17</span></a></p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Sepsis criteria</span><p id="par0040" class="elsevierStylePara elsevierViewall">Traditionally sepsis has been linked to a systemic inflammatory response syndrome in response to an infectious process&#44; and presented in three stages&#58; sepsis&#44; severe sepsis and septic shock&#46; However&#44; recently the Third International Consensus Definitions for Sepsis and Septic Shock has recommended the elimination of the terms sepsis syndrome&#44; septicemia&#44; and severe sepsis and instead defined sepsis as &#8220;life-threatening organ dysfunction due to a dysregulated host response to infection&#8221;&#46; The consensus&#44; also&#44; proposed replacing the Systemic inflammatory response syndrome &#40;SIRS&#41; as diagnostic tool&#44; and substituting with SOFA for encounters in the ICU&#44; as an indicator of organ dysfunction that helps predict a poor prognosis in patients&#46;<a class="elsevierStyleCrossRefs" href="#bib0270"><span class="elsevierStyleSup">18&#8211;20</span></a></p><p id="par0045" class="elsevierStylePara elsevierViewall">In spite of the rigor of the methodology used by the consensus&#44; currently&#44; there remains some controversy around the new definitions&#44;<a class="elsevierStyleCrossRefs" href="#bib0285"><span class="elsevierStyleSup">21&#8211;23</span></a> since the new definitions did not involve low or middle income countries&#44; and SOFA is a score that is routinely calculated in some&#44; but not all&#44; ICUs&#46; Even the experts in sepsis pathobiology of the third international consensus recognized some limitations since some of the definitions and clinical criteria were generated through voting&#44; and unanimity was not always presented&#46;<a class="elsevierStyleCrossRef" href="#bib0275"><span class="elsevierStyleSup">19</span></a></p><p id="par0050" class="elsevierStylePara elsevierViewall">Seeking to follow the definition of the consensus&#44; but without forgetting the doubts regarding the new way of doing the diagnosis&#44; we used the Angus criteria in this study to identify ICU patients with sepsis<a class="elsevierStyleCrossRef" href="#bib0300"><span class="elsevierStyleSup">24</span></a>&#59; therefore&#44; from the 58&#44;977 MIMIC-III admissions&#44; all the ones that complying with the following&#58; &#40;i&#41; ICD-9-CM codes for both a bacterial or fungal infections and a diagnosis of acute organ dysfunction were selected and &#40;ii&#41; explicit sepsis related diagnosis&#58; severe sepsis or septic shock&#46; 15&#44;254 admissions were obtained&#46;</p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0105">Working dataset and <span class="elsevierStyleItalic">exclusion criteria</span></span><p id="par0055" class="elsevierStylePara elsevierViewall">The working dataset was extracted from the 15&#44;254 admissions with a diagnosis of sepsis according to the Angus criteria&#59; First&#44; we selected the admissions of patients aged 16 or older with stays longer than 24 h&#44; resulting in a dataset with 13&#44;836 patients&#46; Then&#44; only the admissions that had at least 70&#37; of the laboratory measurements and at least 70&#37; of routine charted data presented in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a> were included in the working dataset&#44; getting 5650 admissions &#40;with a 1-year mortality rate of 43&#46;3&#37;&#41;&#46;</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><p id="par0060" class="elsevierStylePara elsevierViewall">The data listed in the first two segments of <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a> were extracted during the first 24<span class="elsevierStyleHsp" style=""></span>h of each admission&#46; Since the variables are not measured with the same frequency&#44; we calculated statistical indices that allowed their description&#58; mean&#44; maximum&#44; minimum&#44; variance and range&#46; <a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a> presents two of the variables as an example&#44; the 24 h time window is also shown&#46;</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><p id="par0065" class="elsevierStylePara elsevierViewall">The data listed in the last three segments of <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a> represent single values throughout the entire duration of a patient admission&#59; therefore&#44; they do not require indicators for their description&#46;</p><p id="par0070" class="elsevierStylePara elsevierViewall">Of all variables listed in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>&#44; only four presented more than 5&#37; of missing data being bilirubin the most critical with 34&#37; of absent values&#44; followed by Fraction of Inspired O2 with 15&#37;&#44; Lactate with 13&#37; and Base excess with 7&#37;&#46; The SGB algorithm used for the development of the model is based on decision trees&#44; so it is possible to handle missing values without using imputation&#46;<a class="elsevierStyleCrossRef" href="#bib0305"><span class="elsevierStyleSup">25</span></a></p><p id="par0075" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#44; presents the description of the admissions selected as study cohort by first care unit type&#59; it is evident the sensitivity of the condition of patients with sepsis&#44; since&#44; when compared to the general MIMIC-III population&#44;<a class="elsevierStyleCrossRef" href="#bib0255"><span class="elsevierStyleSup">15</span></a> they present a longer length of stays &#40;both ICU and hospital&#41; and higher in-hospital mortality&#46; <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a>&#44; also shows the 1-year mortality which is almost twice the hospital mortality&#46;</p><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><p id="par0080" class="elsevierStylePara elsevierViewall">The study cohort&#44; containing 5650 admissions&#44; was randomly divided into two groups&#58; a train subset with 3955 admissions &#40;70&#37; of the working set&#41;&#44; and a validation subset of 1695 admissions&#46; <a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 2</a> presents the accrual of admissions included in the study cohort&#46;</p><elsevierMultimedia ident="fig0010"></elsevierMultimedia></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0110">Model development</span><p id="par0085" class="elsevierStylePara elsevierViewall">The data listed in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>&#44; were converted into 140 predictors&#44; 115 of which were the statistical descriptions of the laboratory measurements and the routine charted data&#44; 20 were the presences of comorbidities and organ dysfunctions&#44; two were the numerical values for age and Glasgow Coma Score &#40;GCS&#41;&#44; and 3 corresponded to the gender and admission type categorical data&#44; since each of these variables were binarized using one hot encoding&#46;</p><p id="par0090" class="elsevierStylePara elsevierViewall">To select the most important predictors for the 1-year mortality prediction model two techniques were used&#59; the first one was Least Absolute Shrinkage and Selection Operator &#40;LASSO&#41; based on maximum likelihood logistic regression&#59; for this methodology mean imputation was used&#46; The second technique was based on Stochastic Gradient Boosting &#40;SGB&#41; variable importance&#44; a procedure that indicates the contributions of each of the predictors to the model&#44; therefore it is possible to choose the most relevant predictors that represent the majority of the performance on the model&#46;</p><p id="par0095" class="elsevierStylePara elsevierViewall">Stochastic gradient boosting &#40;SGB&#41; is a type of ensemble algorithm&#46; An ensemble algorithms consist of multiple base models &#40;Small decision trees for SGB&#41;&#44; each one of those provides a different solution to the problem&#59; The solutions of all the base models&#44; are finally combined &#40;usually by weighted voting or averaging&#41; into a single final model output&#44; which is usually a more stable and accurate prediction&#46; The SGB algorithm involves a parameter-tuning process&#46; the three main parameters are&#58; <span class="elsevierStyleItalic">M</span>&#44; the number of trees that are aggregated in the model&#59; <span class="elsevierStyleItalic">&#957;</span>&#44; the learning rate that helps to control over-fitting by controlling the gradient steps and <span class="elsevierStyleItalic">L</span>&#44; the number of splits performed on each Tree&#46;<a class="elsevierStyleCrossRefs" href="#bib0310"><span class="elsevierStyleSup">26&#8211;28</span></a> Least Absolute Shrinkage and Selection Operator &#40;LASSO&#41;&#44;<a class="elsevierStyleCrossRef" href="#bib0325"><span class="elsevierStyleSup">29</span></a> is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the statistical model&#46; LASSO puts a constraint on the sum of the absolute values of the model parameters&#44; applying a regularization process where it penalizes the coefficients of the regression variables and set some of them exactly to zero&#46; In practice there is tuning parameter <span class="elsevierStyleItalic">&#955;</span>&#44; that controls the controls the amount of shrinkage that is applied to the estimates&#46; The SGB and LASSO models were implemented with R-packages&#46;<a class="elsevierStyleCrossRefs" href="#bib0330"><span class="elsevierStyleSup">30&#8211;32</span></a> A detailed description of the used methodologies and the parameters tuning process is found in the supplementary material&#46;</p><p id="par0100" class="elsevierStylePara elsevierViewall">After the predictors were selected with both methods five SGB models were developed&#44; two with the predictors selected with each of the methods&#44; one with the intersection of the predictors&#44; one with the union of the predictors and one with all the predictors&#46; To assess performance in the five SGB models developed with the different sets of predictors and the three severity-of-disease classification systems &#40;SOFA&#44; SAPS II and OASIS&#41; we calculate the Area Under an ROC Curve &#40;AUROC&#41; using the PRROC R package&#46;<a class="elsevierStyleCrossRef" href="#bib0345"><span class="elsevierStyleSup">33</span></a> This was repeated for 5000 random samples of size 320 to generate the distributions of metrics shown in a comparison boxplot&#46; The goodness of fit of the proposed SGB models was evaluated over the entire validation subset using the Pearson&#39;s Chi-square test&#44; to measure the discrepancy between the observed and the predicted mortality distribution&#59; and the Hosmer&#8211;Lemeshow Test was used to assess whether or not the observed event rates match predicted event rates in subgroups of increasing probability of the outcome&#46; <a class="elsevierStyleCrossRef" href="#fig0015">Fig&#46; 3</a> illustrates the methodology that was followed to develop the model&#46;</p><elsevierMultimedia ident="fig0015"></elsevierMultimedia></span></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0115">Results</span><p id="par0105" class="elsevierStylePara elsevierViewall">For the model based on all the predictors the parameters that presented a better AUROC and an adequate calibration were <span class="elsevierStyleItalic">M</span> &#40;number of trees&#41;<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>1150&#44; <span class="elsevierStyleItalic">L</span> &#40;number of splits on each tree&#41;<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>9 and <span class="elsevierStyleItalic">&#957;</span> &#40;learning rate&#41;<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;01&#46; The predictive power of the SGB model with all the variables over the 1695 admissions of the validation subset were evaluated and an AUROC of 0&#46;8039 &#40;95&#37; confidence interval &#40;CI&#41;&#58; &#91;0&#46;8033 0&#46;8045&#93;&#41; was obtained&#59; with this methodology the relative influence of each variable is scaled so that the sum adds 100&#44; with higher numbers indicating stronger influence on the response&#44; the 37 most important predictors for the 1-year mortality are presented in <a class="elsevierStyleCrossRef" href="#fig0020">Fig&#46; 4</a>&#46;</p><elsevierMultimedia ident="fig0020"></elsevierMultimedia><p id="par0110" class="elsevierStylePara elsevierViewall">After applying LASSO regularization process some of the coefficients of the regression are set exactly to zero&#46; <a class="elsevierStyleCrossRef" href="#tbl0015">Table 3</a> presents the LASSO selected predictors &#40;with shrinkage parameter <span class="elsevierStyleItalic">&#955;</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;01288503&#41;&#46; For the SGB model with the intersection variables &#40;18 predictors&#41; the AUROC was 0&#46;792 &#40;95&#37; confidence interval &#40;CI&#41;&#58; &#91;0&#46;791 0&#46;793&#93;&#41;&#46; The other three SGB models were not different in their performance to the model with all the variables&#46;</p><elsevierMultimedia ident="tbl0015"></elsevierMultimedia><p id="par0115" class="elsevierStylePara elsevierViewall">To benchmark the proposed SGB models&#44; three severity-of-disease classification systems were used to evaluate the 1-year mortality on the same validation subset&#46; The AUROC values for the reference scores were&#58; OASIS 0&#46;631 &#40;95&#37; confidence interval &#40;CI&#41;&#58; &#91;0&#46;630&#8211;0&#46;632&#93;&#41;&#44; SOFA 0&#46;588 &#40;95&#37; confidence interval &#40;CI&#41;&#58; &#91;0&#46;587&#8211;0&#46;589&#93;&#41; and SAPS2 0&#46;702 &#40;95&#37; confidence interval &#40;CI&#41;&#58; &#91;0&#46;701&#8211;0&#46;703&#93;&#41;&#46; <a class="elsevierStyleCrossRef" href="#fig0025">Fig&#46; 5</a> presents the box plots of the AUROC and the accuracy of the three reference severity of disease scores and the SGB models with all the predictors and the intersection predictors&#46;</p><elsevierMultimedia ident="fig0025"></elsevierMultimedia><p id="par0120" class="elsevierStylePara elsevierViewall">The calibration of the proposed SGB models was adequate with <span class="elsevierStyleItalic">p</span>-values of 0&#46;0916 and 0&#46;127 for the model with all the variables and the model with the intersection variables respectively&#46; Goodness of fit was also adequate &#40;<span class="elsevierStyleItalic">p</span>-values for all variables model&#58; 0&#46;1857 and <span class="elsevierStyleItalic">p</span>-values for intersection model&#58; 0&#46;9219&#41;&#46;</p><p id="par0125" class="elsevierStylePara elsevierViewall">For SGB model with the intersection predictors&#44; observed versus predicted of numbers of deaths were compared graphically within deciles of increasing probability of the 1-year mortality &#40;<a class="elsevierStyleCrossRef" href="#fig0030">Fig&#46; 6</a>&#41;&#44; and it is observed that estimated and observed mortality pairs are similar and shows that the number of outcome events is indeed increasing along the probability deciles&#46; The relative importance of the 18 predictors of the intersection models are listed in <a class="elsevierStyleCrossRef" href="#tbl0020">Table 4</a>&#46; These predictors allow to identify features that could become prognostic markers for the 1-year mortality of the sepsis diagnosed patients within the ICU and could be an input for a new severity score for patients with sepsis in the ICU&#46;</p><elsevierMultimedia ident="fig0030"></elsevierMultimedia><elsevierMultimedia ident="tbl0020"></elsevierMultimedia></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0120">Discussion and conclusions</span><p id="par0130" class="elsevierStylePara elsevierViewall">Accuracy and AUROC analysis over the validation data indicate that custom mortality prediction models for a specific disease presents a better performance that traditional scores&#44; which could lead to better management of illness within the ICU&#46; Accuracy and AUROC analysis also ratify the complex interdependence among different physiological systems in response to sepsis&#44; because the SGB models are composed of between 450 and 1150 trees&#44; which are difficult to interpret&#59; therefore&#44; it is necessary to develop easy-to-use computer tools that allow these types of models to be implemented within the ICU&#46; This study indicates that it is possible to generate a specific model for the prediction of mortality of patients admitted to an ICU with a diagnosis of sepsis&#44; that includes variables that are now commonly evaluated and been widely used&#46;</p><p id="par0135" class="elsevierStylePara elsevierViewall">SGB variable importance and LASSO methodologies allowed to develop models that preserve the same performance as the one generated with all the predictors but with less predictors&#46; Also the intersection of the predictors selected by the two methods leads to the development of a much simpler model with only 18 predictors and less trees&#44; which also presents good performance&#46;</p><p id="par0140" class="elsevierStylePara elsevierViewall">As expected&#44; older patients are at greater risk in consequence the most important parameter for the outcome is the age&#46; Urine output is used as a marker of acute kidney injury&#44; a disease that is associated with substantial in-hospital mortality&#44; beside this&#44; it is important to note that it is a relatively simple and widely used variable in the ICU that has a high relevance in predicting 1-year mortality with the SGB methodology&#46;</p><p id="par0145" class="elsevierStylePara elsevierViewall">Minimum lactate over the first 24<span class="elsevierStyleHsp" style=""></span>h of the ICU admission is the ninth most important variable for the outcome prediction in this study&#59; Lactate is currently used within the ICU as a diagnostic tool and as a prognostic marker&#44; since the higher the value&#44; the greater the risk of mortality&#46; However&#44; if the lactate of a patient does not reach below a threshold&#44; it will also have a higher mortality risk&#46; For this reason&#44; the minimum lactate during the first 24<span class="elsevierStyleHsp" style=""></span>h must also be analyzed in ICUs&#46; Mean lactate is also considered an important predictor&#44; which agrees with what is reported in the literature&#44; since hyperlactatemia is related with a poor outcome in ICU&#46;<a class="elsevierStyleCrossRef" href="#bib0350"><span class="elsevierStyleSup">34</span></a> An elevated blood urea nitrogen &#40;BUN&#41; is associated with increased mortality in critically ill patients&#46;<a class="elsevierStyleCrossRef" href="#bib0355"><span class="elsevierStyleSup">35</span></a></p><p id="par0150" class="elsevierStylePara elsevierViewall">The main objective of this work is to present a model for the 1-year mortality prediction of the patients that are admitted in a ICU with a sepsis diagnosis&#59; and shows that the use of ensemble based algorithms &#40;SGB in this study&#41; and the inclusion of statistical descriptors that are not usually taken into account in the traditional severity-of-disease classification systems &#40;for example mean&#44; minimum and maximum values of the same variable&#41;&#44; improves the performance of the prediction of prognosis models in patients admitted to an ICU with diagnosis of sepsis&#44; however&#44; this means that the model can only be used after the first 24<span class="elsevierStyleHsp" style=""></span>h of observation&#46;</p><p id="par0155" class="elsevierStylePara elsevierViewall">Other limitations of this study include the fact that it is based on the data taken at only one institution&#44; however&#44; despite the limitation of being single-centered&#44; the main advantages of MIMIC-III are that&#44; right now&#44; it is the only freely accessible critical care database of its kind&#44; the dataset spans more than a decade and it has detailed information about individual patient care that includes time-stamped nurse-verified physiological measurements&#46; For this reasons MIMIC-III &#40;and specially it previous version MIMIC-II&#41; are widely used internationally&#46; For this study in particular&#44; an important advantage is that besides in-hospital mortality&#44; MIMIC-III provides Out-of-hospital mortality dates through the Social Security Administration Death Master File&#46; On the other hand&#44; there have been few validated methods of medical record data extraction for estimating sepsis&#44; particularly in this work&#44; the Angus criteria was used&#44; which is one of the first protocols using administrative data&#44; and was validated by comparing a nurse-driven identification of a population of patients with the clinical syndrome of sepsis&#44; however&#44; Angus criteria has shown to be capable of capture most of the patients with severe sepsis but not exclusively and cohorts identified by different methodologies &#40;for instances Angus criteria and Martin Criteria&#41; yielded widely different patient groups&#46;<a class="elsevierStyleCrossRef" href="#bib0360"><span class="elsevierStyleSup">36</span></a></p><p id="par0205" class="elsevierStylePara elsevierViewall">It is also important to note that the criteria used to select patients is based on the ICD codes&#44; which in MIMIC-III are generated for billing purposes at the end of the hospital stay&#44; hence it does not guarantee that patients suffer from sepsis at the time of admission&#44; even so&#44; we evaluate the data of the first 24 hours after admission since we considered that the majority of patients are usually already infected at ICU admission&#46;</p><p id="par0160" class="elsevierStylePara elsevierViewall">The emergence of machine learning techniques in the field of health is a fact&#46; Specifically&#44; in the field of Intensive Care&#44; it is undeniable that the potential for its application is immense&#46; Specifically&#44; the use of assembly algorithms&#44; and in particular the SGB&#44; allows the development of prediction models&#44; which despite being complex show significantly better discrimination than traditional severity of disease scores &#40;Like OASIS&#44; SAPS II or SOFA&#41;&#46; This could be explained by the fact that the base models in the SGB algorithm are not fitted independently&#44; but sequentially&#44; this mean that the subsequent predictors are based on the results of previous predictors&#44; moreover&#44; SGB is based on a steepest gradient algorithm which places emphasis on misclassified training data that are close to their correct classification&#44; which reduces the number of misclassified observations and facilitates mortality prediction models to be used at the individual level&#46;</p></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0125">Author&#39;s contribution</span><p id="par0165" class="elsevierStylePara elsevierViewall">Javier E&#46; Garc&#237;a-Gallo contributed in the concepts&#44; design&#44; definition of intellectual content&#44; literature search&#44; experimental studies&#44; data acquisition&#44; data analysis&#44; statistical analysis&#44; manuscript preparation&#44; manuscript editing&#44; manuscript review&#46;</p><p id="par0170" class="elsevierStylePara elsevierViewall">Nelson J&#46; Fonseca-Ruiz contributed in the concepts&#44; design&#44; definition of intellectual content&#44; literature search&#44; data analysis&#44; manuscript editing&#44; manuscript review&#46;</p><p id="par0175" class="elsevierStylePara elsevierViewall">Leo Anthony Celi contributed in the concepts&#44; design&#44; definition of intellectual content&#44; literature search&#44; manuscript preparation&#44; manuscript editing&#44; manuscript review&#46;</p><p id="par0180" class="elsevierStylePara elsevierViewall">John F&#46; Duitama-Mu&#241;oz contributed in the concepts&#44; design&#44; definition of intellectual content&#44; data analysis&#44; manuscript preparation&#44; manuscript editing&#44; manuscript review&#46;</p></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0130">Conflicts of interest</span><p id="par0185" class="elsevierStylePara elsevierViewall">The authors have no conflicts of interest to declare&#46;</p></span></span>"
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              "identificador" => "sec0020"
              "titulo" => "Sepsis criteria"
            ]
            2 => array:2 [
              "identificador" => "sec0025"
              "titulo" => "Working dataset and exclusion criteria"
            ]
            3 => array:2 [
              "identificador" => "sec0030"
              "titulo" => "Model development"
            ]
          ]
        ]
        6 => array:2 [
          "identificador" => "sec0035"
          "titulo" => "Results"
        ]
        7 => array:2 [
          "identificador" => "sec0040"
          "titulo" => "Discussion and conclusions"
        ]
        8 => array:2 [
          "identificador" => "sec0045"
          "titulo" => "Author&#39;s contribution"
        ]
        9 => array:2 [
          "identificador" => "sec0050"
          "titulo" => "Conflicts of interest"
        ]
        10 => array:2 [
          "identificador" => "xack455721"
          "titulo" => "Acknowledgements"
        ]
        11 => array:1 [
          "titulo" => "References"
        ]
      ]
    ]
    "pdfFichero" => "main.pdf"
    "tienePdf" => true
    "fechaRecibido" => "2018-04-09"
    "fechaAceptado" => "2018-07-25"
    "PalabrasClave" => array:2 [
      "en" => array:1 [
        0 => array:4 [
          "clase" => "keyword"
          "titulo" => "Keywords"
          "identificador" => "xpalclavsec1218927"
          "palabras" => array:5 [
            0 => "Prognosis prediction"
            1 => "Sepsis"
            2 => "Stochastic gradient boosting"
            3 => "Intensive care unit"
            4 => "Least absolute shrinkage and selection operator"
          ]
        ]
      ]
      "es" => array:1 [
        0 => array:4 [
          "clase" => "keyword"
          "titulo" => "Palabras clave"
          "identificador" => "xpalclavsec1218928"
          "palabras" => array:5 [
            0 => "Predicci&#243;n de pron&#243;stico"
            1 => "Sepsis"
            2 => "Stochastic Gradient Boosting"
            3 => "Unidad de Cuidados Intensivos"
            4 => "Least Absolute Shrinkage and Selection Operator"
          ]
        ]
      ]
    ]
    "tieneResumen" => true
    "resumen" => array:2 [
      "en" => array:3 [
        "titulo" => "Abstract"
        "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Introduction</span><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Sepsis is associated to a high mortality rate&#44; and its severity must be evaluated quickly&#46; The severity of illness scores used are intended to be applicable to all patient populations&#44; and generally evaluate in-hospital mortality&#46; However&#44; patients with sepsis continue to be at risk of death after hospital discharge&#46;</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Objective</span><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">To develop a model for predicting 1-year mortality in critical patients diagnosed with sepsis&#46;</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Patients</span><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">The data corresponding to 5650 admissions of patients with sepsis from the Medical Information Mart for Intensive Care &#40;MIMIC-III&#41; database were evaluated&#44; randomly divided as follows&#58; 70&#37; for training and 30&#37; for validation&#46;</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Design</span><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">A retrospective register-based cohort study was carried out&#46; The clinical information of the first 24<span class="elsevierStyleHsp" style=""></span>h after admission was used to develop a 1-year mortality prediction model based on Stochastic Gradient Boosting &#40;SGB&#41; methodology&#46; Variable selection was addressed using Least Absolute Shrinkage and Selection Operator &#40;LASSO&#41; and SGB variable importance methodologies&#46; The predictive power was evaluated using the area under the ROC curve &#40;AUROC&#41;&#46;</p></span> <span id="abst0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0030">Results</span><p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">An AUROC of 0&#46;8039 &#40;95&#37; confidence interval &#40;CI&#41;&#58; &#91;0&#46;8033 0&#46;8045&#93;&#41; was obtained in the validation subset&#46; The model exceeded the predictive performances obtained with traditional severity of disease scores in the same subset&#46;</p></span> <span id="abst0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0035">Conclusion</span><p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">The use of assembly algorithms&#44; such as SGB&#44; for the generation of a customized model for sepsis yields more accurate 1-year mortality prediction than the traditional scoring systems such as SAPS II&#44; SOFA or OASIS&#46;</p></span>"
        "secciones" => array:6 [
          0 => array:2 [
            "identificador" => "abst0005"
            "titulo" => "Introduction"
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          1 => array:2 [
            "identificador" => "abst0010"
            "titulo" => "Objective"
          ]
          2 => array:2 [
            "identificador" => "abst0015"
            "titulo" => "Patients"
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          3 => array:2 [
            "identificador" => "abst0020"
            "titulo" => "Design"
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          4 => array:2 [
            "identificador" => "abst0025"
            "titulo" => "Results"
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          5 => array:2 [
            "identificador" => "abst0030"
            "titulo" => "Conclusion"
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      "es" => array:3 [
        "titulo" => "Resumen"
        "resumen" => "<span id="abst0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">Introducci&#243;n</span><p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">La sepsis conlleva una elevada mortalidad&#44; y su gravedad debe evaluarse r&#225;pidamente&#46; Los sistemas utilizados para clasificar la intensidad de la enfermedad pretenden ser aplicables a todos los pacientes&#44; y generalmente eval&#250;an la mortalidad intrahospitalaria&#46; Sin embargo&#44; los pacientes con sepsis contin&#250;an estando en riesgo de muerte despu&#233;s del alta hospitalaria&#46;</p></span> <span id="abst0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">Objetivo</span><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">Desarrollar un modelo para la predicci&#243;n de la mortalidad a un a&#241;o de pacientes en UCI con diagn&#243;stico de sepsis&#46;</p></span> <span id="abst0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0055">Pacientes</span><p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Se evaluaron los datos de 5650 admisiones de pacientes con sepsis de la base de datos Medical Information Mart for Intensive Care &#40;MIMIC-III&#41;&#44; los cuales fueron divididos aleatoriamente as&#237;&#58; 70&#37; para entrenamiento y 30&#37; para validaci&#243;n&#46;</p></span> <span id="abst0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0060">Dise&#241;o</span><p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">Estudio retrospectivo de cohorte basado en registros&#46; Se utiliz&#243; la informaci&#243;n cl&#237;nica de las primeras 24 horas despu&#233;s de la admisi&#243;n para desarrollar un modelo de predicci&#243;n de mortalidad a un a&#241;o basado en la metodolog&#237;a Stochastic Gradient Boosting &#40;SGB&#41;&#46; La selecci&#243;n de variables se abord&#243; utilizando las metodolog&#237;as Least Absolute Shrinkage and Selection Operator &#40;LASSO&#41; e importancia de variables por SGB&#46; El poder predictivo del modelo fue evaluado usando el AUROC&#46;</p></span> <span id="abst0055" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0065">Resultados</span><p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">Se obtuvo un AUROC de 0&#46;8039 &#40;intervalo de confianza &#91;IC&#93; del 95&#37;&#58; &#91;0&#46;8033-0&#46;8045&#93;&#41;&#46; El modelo supera los resultados obtenidos con algunos puntajes tradicionales en el mismo subconjunto de validaci&#243;n&#46;</p></span> <span id="abst0060" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Conclusi&#243;n</span><p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">El uso de algoritmos de ensamblaje&#44; como SGB&#44; para la generaci&#243;n de un modelo adaptado para la sepsis&#44; proporcionan estimaciones de mortalidad a un a&#241;o m&#225;s precisas que los sistemas de puntuaci&#243;n tradicionales como SAPS II&#44; SOFA u OASIS&#46;</p></span>"
        "secciones" => array:6 [
          0 => array:2 [
            "identificador" => "abst0035"
            "titulo" => "Introducci&#243;n"
          ]
          1 => array:2 [
            "identificador" => "abst0040"
            "titulo" => "Objetivo"
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          2 => array:2 [
            "identificador" => "abst0045"
            "titulo" => "Pacientes"
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          3 => array:2 [
            "identificador" => "abst0050"
            "titulo" => "Dise&#241;o"
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          4 => array:2 [
            "identificador" => "abst0055"
            "titulo" => "Resultados"
          ]
          5 => array:2 [
            "identificador" => "abst0060"
            "titulo" => "Conclusi&#243;n"
          ]
        ]
      ]
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    "apendice" => array:1 [
      0 => array:1 [
        "seccion" => array:1 [
          0 => array:4 [
            "apendice" => "<p id="par0200" class="elsevierStylePara elsevierViewall"><elsevierMultimedia ident="upi0005"></elsevierMultimedia></p>"
            "etiqueta" => "Appendix A"
            "titulo" => "Supplementary data"
            "identificador" => "sec0060"
          ]
        ]
      ]
    ]
    "multimedia" => array:11 [
      0 => array:7 [
        "identificador" => "fig0005"
        "etiqueta" => "Figure 1"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr1.jpeg"
            "Alto" => 1299
            "Ancho" => 2377
            "Tamanyo" => 133496
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        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0065" class="elsevierStyleSimplePara elsevierViewall">Example variables&#46; The box represents the 24-h window in which the data are extracted and evaluated&#46;</p>"
        ]
      ]
      1 => array:7 [
        "identificador" => "fig0010"
        "etiqueta" => "Figure 2"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr2.jpeg"
            "Alto" => 1401
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            "Tamanyo" => 118980
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        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">Accrual of admissions included in the study cohort&#46;</p>"
        ]
      ]
      2 => array:7 [
        "identificador" => "fig0015"
        "etiqueta" => "Figure 3"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr3.jpeg"
            "Alto" => 1580
            "Ancho" => 2833
            "Tamanyo" => 338957
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        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0075" class="elsevierStyleSimplePara elsevierViewall">Model development&#46; Stochastic gradient boosting &#40;SGB&#41; tuning parameters&#58; <span class="elsevierStyleItalic">M</span>&#44; the number of trees that are aggregated in the model&#59; <span class="elsevierStyleItalic">&#957;</span>&#44; the learning rate and <span class="elsevierStyleItalic">L</span>&#44; the number of splits performed on each tree&#46; Least Absolute Shrinkage and Selection Operator &#40;LASSO&#41; tuning parameter&#58; <span class="elsevierStyleItalic">&#955;</span>&#44; controls the controls the amount of shrinkage that is applied to the estimates&#46;</p>"
        ]
      ]
      3 => array:7 [
        "identificador" => "fig0020"
        "etiqueta" => "Figure 4"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr4.jpeg"
            "Alto" => 1286
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            "Tamanyo" => 188204
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        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0080" class="elsevierStyleSimplePara elsevierViewall">SGB relative importance of the predictors for the 1-year mortality prediction model&#46; Abbreviations&#58; Bun&#58; blood urea nitrogen&#59; Max&#58; maximum&#59; WBC&#58; white blood cell&#59; Min&#58; minimum&#59; SpO<span class="elsevierStyleInf">2</span>&#58; peripheral capillary oxygen saturation&#59; PaO<span class="elsevierStyleInf">2</span>&#47;FiO<span class="elsevierStyleInf">2</span>&#58; partial pressure arterial oxygen and fraction of inspired oxygen ratio&#59; FiO<span class="elsevierStyleInf">2</span>&#58; fraction of inspired oxygen&#59; Mechanical vent&#58; mechanical ventilation&#59; DABP&#58; diastolic arterial blood pressure&#59; SABP&#58; systolic arterial blood pressure&#59; MABP&#58; mean arterial blood pressure&#46;</p>"
        ]
      ]
      4 => array:7 [
        "identificador" => "fig0025"
        "etiqueta" => "Figure 5"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr5.jpeg"
            "Alto" => 2163
            "Ancho" => 2207
            "Tamanyo" => 127954
          ]
        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0085" class="elsevierStyleSimplePara elsevierViewall">Box plots of AUROC and accuracy for the 1-year mortality on the validation subset&#46; For the proposed SGB models with all variables &#40;140 predictors&#41;&#44; and the variables from the intersection between the SGB variable importance selected variables and the LASSO selected variables &#40;18 predictors&#41;&#46;</p>"
        ]
      ]
      5 => array:7 [
        "identificador" => "fig0030"
        "etiqueta" => "Figure 6"
        "tipo" => "MULTIMEDIAFIGURA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "figura" => array:1 [
          0 => array:4 [
            "imagen" => "gr6.jpeg"
            "Alto" => 1195
            "Ancho" => 2172
            "Tamanyo" => 89826
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        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0090" class="elsevierStyleSimplePara elsevierViewall">Comparison of observed versus predicted 1-year mortality in the deciles of predicted mortality based on the SGB model with the intersection variables&#46;</p>"
        ]
      ]
      6 => array:8 [
        "identificador" => "tbl0005"
        "etiqueta" => "Table 1"
        "tipo" => "MULTIMEDIATABLA"
        "mostrarFloat" => true
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        "detalles" => array:1 [
          0 => array:3 [
            "identificador" => "at1"
            "detalle" => "Table "
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        "tabla" => array:1 [
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              "tabla" => array:1 [
                0 => """
                  <table border="0" frame="\n
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                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Parameter&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">Unit&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " colspan="2" align="left" valign="\n
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                  \t\t\t\t"><span class="elsevierStyleItalic">Laboratory measurements</span></td></tr><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
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Platelet count&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">10<span class="elsevierStyleSup">9</span>&#47;L&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"><span class="elsevierStyleHsp" style=""></span>Bilirubin&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">mg&#47;dL&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"><span class="elsevierStyleHsp" style=""></span>Creatinine&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">mg&#47;dL&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"><span class="elsevierStyleHsp" style=""></span>Fraction of inspired oxygen&nbsp;\t\t\t\t\t\t\n
                  \t\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">&#37;&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>Partial pressure arterial oxygen and fraction of inspired oxygen ratio&nbsp;\t\t\t\t\t\t\n
                  \t\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">Ratio&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"><span class="elsevierStyleHsp" style=""></span>White blood cell count&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">10<span class="elsevierStyleSup">3</span>&#47;mm<span class="elsevierStyleSup">3</span>&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"><span class="elsevierStyleHsp" style=""></span>Potassium&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">mEq&#47;L&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>Sodium&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">mEq&#47;L&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Bicarbonate&nbsp;\t\t\t\t\t\t\n
                  \t\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">mEq&#47;L&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></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>Arterial pH&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">pH&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"><span class="elsevierStyleHsp" style=""></span>Hematocrit&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#37;&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"><span class="elsevierStyleHsp" style=""></span>Hemoglobin&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">mg&#47;dL&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"><span class="elsevierStyleHsp" style=""></span>Blood urea nitrogen&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">mg&#47;dL&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="2" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="2" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleItalic">Routine charted data</span></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>Temperature&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#176;C&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"><span class="elsevierStyleHsp" style=""></span>Heart rate&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Bpm&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"><span class="elsevierStyleHsp" style=""></span>Systolic arterial blood pressure&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">mmHg&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"><span class="elsevierStyleHsp" style=""></span>Diastolic arterial blood pressure&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">mmHg&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"><span class="elsevierStyleHsp" style=""></span>Mean arterial blood pressure&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">mmHg&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"><span class="elsevierStyleHsp" style=""></span>Urine output&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">mL&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"><span class="elsevierStyleHsp" style=""></span>Base excess&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">mEq&#47;L&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"><span class="elsevierStyleHsp" style=""></span>Glucose&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">mg&#47;dL&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"><span class="elsevierStyleHsp" style=""></span>Peripheral capillary oxygen saturation&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#37;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="2" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="2" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleItalic">Data taken at the time of ICU admission</span></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>Gender&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Female&#44; male&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " rowspan="2" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleHsp" style=""></span>Admission type</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Medical&#44; scheduled surgical&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">Unscheduled surgical&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"><span class="elsevierStyleHsp" style=""></span>Age&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Years&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"><span class="elsevierStyleHsp" style=""></span>Glasgow Coma Scale&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Integer 3&#8211;15&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="2" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="2" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleItalic">Comorbidities</span></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>Diabetes&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " rowspan="10" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Binary &#40;presence&#41;</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>Immunosuppressive diseases&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"><span class="elsevierStyleHsp" style=""></span>Malignancy&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"><span class="elsevierStyleHsp" style=""></span>Hematologic malignancy&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"><span class="elsevierStyleHsp" style=""></span>Metastatic cancer&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"><span class="elsevierStyleHsp" style=""></span>Heart failure&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"><span class="elsevierStyleHsp" style=""></span>Pulmonary diseases&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"><span class="elsevierStyleHsp" style=""></span>Vascular diseases&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"><span class="elsevierStyleHsp" style=""></span>Coronary diseases&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"><span class="elsevierStyleHsp" style=""></span>Obesity&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
                  """
              ]
              "imagenFichero" => array:1 [
                0 => "xTab2264751.png"
              ]
            ]
            1 => array:2 [
              "tabla" => array:1 [
                0 => """
                  <table border="0" frame="\n
                  \t\t\t\t\tvoid\n
                  \t\t\t\t" class=""><tbody title="tbody"><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>Alcohol abuse&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " rowspan="4" align="left" valign="\n
                  \t\t\t\t\ttop\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"><span class="elsevierStyleHsp" style=""></span>Collagen diseases&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"><span class="elsevierStyleHsp" style=""></span>Drug abuse&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"><span class="elsevierStyleHsp" style=""></span>Malnutrition&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="2" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleVsp" style="height:0.5px"></span></td></tr><tr title="table-row"><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " colspan="2" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t"><span class="elsevierStyleItalic">Organ dysfunction</span></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>Cardiovascular&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " rowspan="6" align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Binary &#40;presence&#41;</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>Neurologic&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"><span class="elsevierStyleHsp" style=""></span>Hepatic&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"><span class="elsevierStyleHsp" style=""></span>Hematologic&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"><span class="elsevierStyleHsp" style=""></span>Renal&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"><span class="elsevierStyleHsp" style=""></span>Mechanical ventilation&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
                  """
              ]
              "imagenFichero" => array:1 [
                0 => "xTab2264752.png"
              ]
            ]
          ]
        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0095" class="elsevierStyleSimplePara elsevierViewall">Extracted data from each admission&#46;</p>"
        ]
      ]
      7 => array:8 [
        "identificador" => "tbl0010"
        "etiqueta" => "Table 2"
        "tipo" => "MULTIMEDIATABLA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "detalles" => array:1 [
          0 => array:3 [
            "identificador" => "at2"
            "detalle" => "Table "
            "rol" => "short"
          ]
        ]
        "tabla" => array:2 [
          "leyenda" => "<p id="spar0105" class="elsevierStyleSimplePara elsevierViewall">Abbreviations&#58; MIMIC-III&#58; medical information mart for intensive care&#44; ICU&#58; intensive care unit&#46;</p>"
          "tablatextoimagen" => array:1 [
            0 => array:2 [
              "tabla" => array:1 [
                0 => """
                  <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" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">MIMIC-III&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
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Medical ICU&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
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Surgical ICU&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
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Coronary care&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
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Cardiac surgery recovery&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
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Surgical trauma ICU&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
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Total&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
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Hospital admissions&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">3138 &#40;55&#46;54&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">765 &#40;13&#46;54&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">735 &#40;13&#46;01&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">404 &#40;7&#46;15&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">608 &#40;10&#46;76&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">5650 &#40;100&#37;&#41;&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">Different ICU stays&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">3402 &#40;53&#46;64&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">934 &#40;14&#46;73&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">828 &#40;13&#46;06&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">483 &#40;7&#46;62&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">695 &#40;10&#46;96&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">6342 &#40;100&#37;&#41;&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">Age&#44; median years&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">67&#46;5&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">64&#46;72&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">71&#46;75&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">70&#46;36&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">61&#46;63&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">67&#46;54&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">Gender &#40;masculine&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1642 &#40;52&#46;32&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">393 &#40;51&#46;37&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">406 &#40;55&#46;23&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">248 &#40;61&#46;38&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">395 &#40;64&#46;96&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">3084 &#40;54&#46;58&#37;&#41;&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">ICU length of stay&#44; median days&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">5&#46;06&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">6&#46;68&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">5&#46;81&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">8&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">7&#46;88&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">5&#46;9&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">Hospital length of stay&#44; median days&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">10&#46;29&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">14&#46;99&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">10&#46;63&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">15&#46;88&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">17&#46;13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">11&#46;88&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">Hospital mortality&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">757 &#40;24&#46;12&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">165 &#40;21&#46;56&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">168 &#40;22&#46;85&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">76 &#40;18&#46;81&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">111 &#40;18&#46;25&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1277 &#40;22&#46;6&#37;&#41;&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">One-year mortality&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">1459 &#40;46&#46;49&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">301 &#40;39&#46;34&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">346 &#40;47&#46;07&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">161 &#40;39&#46;85&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">183 &#40;30&#46;09&#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="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">2450 &#40;43&#46;36&#37;&#41;&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
                  """
              ]
              "imagenFichero" => array:1 [
                0 => "xTab2264753.png"
              ]
            ]
          ]
        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0100" class="elsevierStyleSimplePara elsevierViewall">Description of the study cohort&#46;</p>"
        ]
      ]
      8 => array:8 [
        "identificador" => "tbl0015"
        "etiqueta" => "Table 3"
        "tipo" => "MULTIMEDIATABLA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "detalles" => array:1 [
          0 => array:3 [
            "identificador" => "at3"
            "detalle" => "Table "
            "rol" => "short"
          ]
        ]
        "tabla" => array:1 [
          "tablatextoimagen" => array:1 [
            0 => array:2 [
              "tabla" => array:1 [
                0 => """
                  <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" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Predictor&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
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Haematologic malignancy&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">Metastatic cancer&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">Admission type&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">Gender&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">Age&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 maximum&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">Systolic arterial blood pressure minimum&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">Systolic arterial blood pressure maximum&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 minimum&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 maximum&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">Urine output&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 urea nitrogen maximum&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">White blood cell count maximum&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">Bilirubin maximum&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">Glasgow Coma Scale Minimum&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">Diastolic arterial blood pressure minimum&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">Base excess maximum&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">Fraction of inspired oxygen maximum&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">Glucose minimum&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">Peripheral capillary oxygen saturation minimum&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">Peripheral capillary oxygen saturation mean&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 maximum&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 range&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 maximum&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 minimum&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 mean&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">Platelet count maximum&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">Malignancy&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 failure&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">Vascular&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">Obesity&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">Alcohol abuse&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">Hypertension&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">Cardiovascular&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">Haematologic dysfunction&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">Renal dysfunction&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">Mechanical ventilation&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr></tbody></table>
                  """
              ]
              "imagenFichero" => array:1 [
                0 => "xTab2264750.png"
              ]
            ]
          ]
        ]
        "descripcion" => array:1 [
          "en" => "<p id="spar0110" class="elsevierStyleSimplePara elsevierViewall">LASSO selected predictors&#46;</p>"
        ]
      ]
      9 => array:8 [
        "identificador" => "tbl0020"
        "etiqueta" => "Table 4"
        "tipo" => "MULTIMEDIATABLA"
        "mostrarFloat" => true
        "mostrarDisplay" => false
        "detalles" => array:1 [
          0 => array:3 [
            "identificador" => "at4"
            "detalle" => "Table "
            "rol" => "short"
          ]
        ]
        "tabla" => array:1 [
          "tablatextoimagen" => array:1 [
            0 => array:2 [
              "tabla" => array:1 [
                0 => """
                  <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" title="\n
                  \t\t\t\t\ttable-head\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Predictor&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
                  \t\t\t\t\ttop\n
                  \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Relative importance&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
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Age&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">17&#46;77&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">Urine output&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">16&#46;17&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 urea nitrogen maximum&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">7&#46;28&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">Metastatic cancer&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">5&#46;82&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 maximum&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">5&#46;03&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">Bilirubin maximum&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">5&#46;01&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 maximum&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">4&#46;98&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 mean&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">4&#46;38&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 minimum&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="char" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">4&#46;3&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 minimum&nbsp;\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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ISSN: 02105691
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