Early mortality risk stratification after SARS-CoV-2 infection

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Lundon, B.D. Kelly, S. Nair, D.M. Bolton, N. Kyprianou, P. Wiklund, A. Tewari" "autores" => array:7 [ 0 => array:4 [ "nombre" => "D.J." "apellidos" => "Lundon" "email" => array:1 [ 0 => "Dara.Lundon@mountsinai.org" ] "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "*" "identificador" => "cor0005" ] ] ] 1 => array:3 [ "nombre" => "B.D." "apellidos" => "Kelly" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "b" "identificador" => "aff0010" ] ] ] 2 => array:3 [ "nombre" => "S." "apellidos" => "Nair" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "a" "identificador" => "aff0005" ] ] ] 3 => array:3 [ "nombre" => "D.M." "apellidos" => "Bolton" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "b" "identificador" => "aff0010" ] ] ] 4 => array:3 [ "nombre" => "N." 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"apellidos" => "Tewari" "email" => array:1 [ 0 => "Ash.Tewari@mountsinai.org" ] "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "*" "identificador" => "cor0005" ] ] ] ] "afiliaciones" => array:2 [ 0 => array:3 [ "entidad" => "Department of Urology, Icahn School of Medicine, Mount Sinai Hospitals, New York, United States" "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:3 [ "entidad" => "Department of Urology, University of Melbourne, Austin Health, Melbourne, United States" "etiqueta" => "b" "identificador" => "aff0010" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "⁎" "correspondencia" => "Corresponding authors." ] ] ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Estratificación de riesgo de mortalidad temprana en infección por SARS-CoV-2" ] ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0005" "etiqueta" => "Figure 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 3690 "Ancho" => 3333 "Tamanyo" => 804404 ] ] "descripcion" => array:1 [ "en" => "(a) Correlation matrix demonstrating the statistically significant (p<0.05) correlations between risk factors and the ‘outcome’ variable of death within 7 days from symptom onset: the larger the size and the stronger the color, the higher the correlation. (b) Receiver operating characteristic curves and AUC values for the model constructed to predict death within 7 days from the listed variables (Age, Sex, Systolic BP, Max Temperature, O2 saturations and Zip code) in the independent holdout dataset demonstrating the discriminative ability of this model (AUC=0.8612; 95% CI 0.804–0.919). (c) Calibration curves of the model in the holdout dataset demonstrating the agreement between predicted and observed probabilities of a positive Covid-19 test. (d) Decision curve analysis for positive Covid-19 test prediction in the holdout dataset. (e) Screenshot of the deployed web-app which is freely available for use online at The risk tool is freely available online at www.tiny.cc/covidcalc (f) Kaplan–Meier Curve of risk strata as defined by the derived risk model derived from 3 demographic and 4 clinical features.; risk strata of low (33% risk of death within 7 days) intermediate (34–66%) and high risk (>66%) are plotted in an independent hold-out test set from the NY cohort." ] ] ] "textoCompleto" => "As of June 3rd there are over 6.4 million confirmed cases in over 188 countries and territories and 382,435 confirmed deaths.<a class="elsevierStyleCrossRef" href="#bib0030">1</a> New York has reported 30,007 of these deaths, or 7.8% of all COVID-19 deaths in the world to date, and has just passed the inevitable mantle of epicenter of the pandemic to Latin America.<a class="elsevierStyleCrossRef" href="#bib0035">2</a>Discerning those patients whom are most likely to die due to COVID-19 is a challenge given the novelty of the syndrome. A better understanding of which patient progresses to serious disease with critical need of care, and which patient experiences rapid recovery could help better inform our understanding of the COVID-19 syndrome and better care for all patients.<a class="elsevierStyleCrossRef" href="#bib0040">3</a> Such information could also assist in the allocation of intensive care beds, ventilators, and planning palliative care and hospice support.While the majority of those who test positive for SARS-CoV-2 will not require hospital admission, intensive care or mechanical ventilation, some will (data from our institution suggest ∼13% of those hospitalized due to COVID-19 underwent mechanical ventilation). Concerns over a shortage of ventilators have transformed into commentary about ventilators being overused, while it is now shortages of medications that are anticipated.<a class="elsevierStyleCrossRef" href="#bib0040">3</a>A recent publication outlined the unprecedented opportunities of prediction models to inform, personalize and improve care, as well as reiterating principles for the creation and use of such models.<a class="elsevierStyleCrossRef" href="#bib0050">4</a> Using the Mount Sinai Health Systems’ database of all SARS-Co-V-2 positive patients with an encounter in New York City, we developed a prediction model to identify those patients most likely to succumb to the disease thin 7 days (the median time to death of those who died) from the onset of COVID-19 symptoms. We did this by first identifying all those whom died following a positive SARS-CoV-2 test, and all of those whom were positive for SARS-CoV-2, but surviving after at least 7 days post-diagnosis (<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>). The data was randomly divided into a training and independent holdout dataset (80%:20%). Feature selection included least absolute shrinkage and selection operator (LASSO) regression method. Prediction models were built using machine learning methods including SVM, k-NN and random forest. The performance of the constructed model was assessed by area under the receiver operating characteristic curve (AUC), calibration (calibration plots and Hosmer–Lemeshow calibration test) and clinical utility assessed by decision curve analysis.<elsevierMultimedia ident="tbl0005"></elsevierMultimedia>During the study period, there were 14,651 patients who tested positive for SARS-CoV-2; complete data was available for 5152. Of these 464 (9%) died; the median time to death from the onset of symptoms was 7 days in this cohort.<a class="elsevierStyleCrossRef" href="#bib0045">5</a> Patients who had been in hospital for at least 7 days and had not deceased accounted for 6.8% of this cohort (N=351) (<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>). Significant predictors of mortality within 7 days of symptom onset (as identified by the feature selection method described above) include age, gender, respiratory rate, oxygen saturation level, systolic blood pressure, maximum temperature and zip code (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>A).<elsevierMultimedia ident="fig0005"></elsevierMultimedia>The risk probability of death within 7 days for patients was calculated from a model developed in 80% of this cohort, using the 3 demographic and 4 clinical variables listed above. The model was assessed in the independent hold-out group using discriminative ability (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>B: AUC 0.861), calibration curves; which demonstrate that the magnitude of the assigned probabilities track the relative frequency of the observed outcomes (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>C) and decision curve analysis (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>D) which demonstrates the model offers a superior net benefit across a greater range of probabilities than other common strategies. Patients were stratified according to their calculated risk probability as follows: low risk <33%, intermediate, 34–66%, and high risk >66%. Patients classified as low risk by this model have a statistically significant longer survival than those in the intermediate and high risk groups (p<0.0001) (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 1</a>F).This model demonstrates the significant role that both clinical and social determinants play in predicting the clinical outcome for patients infected with SARS-CoV-2 and tested positive for COVID-19. The methods used to develop this risk stratification tool can be easily employed to reproduce a time and location specific model, for other cities and other countries as this pandemic continues to evolve and unfold globally. Such tools can aide clinicians in decision making, and provide health systems with insights to allow them better allocate resources in response to this pandemic." "pdfFichero" => "main.pdf" "tienePdf" => true "multimedia" => array:2 [ 0 => array:7 [ "identificador" => "fig0005" "etiqueta" => "Figure 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 3690 "Ancho" => 3333 "Tamanyo" => 804404 ] ] "descripcion" => array:1 [ "en" => "(a) Correlation matrix demonstrating the statistically significant (p<0.05) correlations between risk factors and the ‘outcome’ variable of death within 7 days from symptom onset: the larger the size and the stronger the color, the higher the correlation. (b) Receiver operating characteristic curves and AUC values for the model constructed to predict death within 7 days from the listed variables (Age, Sex, Systolic BP, Max Temperature, O2 saturations and Zip code) in the independent holdout dataset demonstrating the discriminative ability of this model (AUC=0.8612; 95% CI 0.804–0.919). (c) Calibration curves of the model in the holdout dataset demonstrating the agreement between predicted and observed probabilities of a positive Covid-19 test. (d) Decision curve analysis for positive Covid-19 test prediction in the holdout dataset. (e) Screenshot of the deployed web-app which is freely available for use online at The risk tool is freely available online at www.tiny.cc/covidcalc (f) Kaplan–Meier Curve of risk strata as defined by the derived risk model derived from 3 demographic and 4 clinical features.; risk strata of low (33% risk of death within 7 days) intermediate (34–66%) and high risk (>66%) are plotted in an independent hold-out test set from the NY cohort." ] ] 1 => 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:2 [ "leyenda" => "Percentages may not total 100, due to rounding. RIP in ≥7 days was chosen as a cut point as the median time to death from the onset of symptoms was 7 days. AA: refers to African American; COPD: chronic obstructive pulmonary disease; HTN: hypertension; CKD: chronic kidney disease; HIV: positive for human immunodeficiency virus." "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="" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col"> \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 " colspan="2" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">RIP in <7 days</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="2" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">RIP in >7 days</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="2" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Alive>7 days</th><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " colspan="2" align="center" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black">Total</th></tr><tr title="table-row"><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="\n \t\t\t\t\ttop\n \t\t\t\t" scope="col" style="border-bottom: 2px solid black"> \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">N \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">% \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">N \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">% \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">N \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">% \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">N \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">% \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">Number of patients \t\t\t\t\t\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">274 \t\t\t\t\t\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">33.6 \t\t\t\t\t\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">190 \t\t\t\t\t\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">23.3 \t\t\t\t\t\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">351 \t\t\t\t\t\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">43.1 \t\t\t\t\t\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">815 \t\t\t\t\t\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">100.0% \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="9" align="left" valign="\n \t\t\t\t\ttop\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="9" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Gender</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Male \t\t\t\t\t\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">156 \t\t\t\t\t\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">56.9% \t\t\t\t\t\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">127 \t\t\t\t\t\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">66.8% \t\t\t\t\t\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">198 \t\t\t\t\t\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">56.4% \t\t\t\t\t\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">481 \t\t\t\t\t\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">59.0% \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="9" align="left" valign="\n \t\t\t\t\ttop\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="9" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Race (rationalized)</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">AA/Black/African Ancestry \t\t\t\t\t\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">78 \t\t\t\t\t\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">28.5% \t\t\t\t\t\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">52 \t\t\t\t\t\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">27.5% \t\t\t\t\t\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">103 \t\t\t\t\t\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">29.3% \t\t\t\t\t\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">233 \t\t\t\t\t\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">28.6% \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">Asian \t\t\t\t\t\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 \t\t\t\t\t\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">5.8% \t\t\t\t\t\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 \t\t\t\t\t\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">4.2% \t\t\t\t\t\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 \t\t\t\t\t\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">2.8% \t\t\t\t\t\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">34 \t\t\t\t\t\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">4.2% \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/European Ancestry \t\t\t\t\t\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">73 \t\t\t\t\t\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">26.6% \t\t\t\t\t\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">55 \t\t\t\t\t\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">29.1% \t\t\t\t\t\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">104 \t\t\t\t\t\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">29.6% \t\t\t\t\t\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">232 \t\t\t\t\t\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">28.5% \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">Other \t\t\t\t\t\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">103 \t\t\t\t\t\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">37.6% \t\t\t\t\t\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">69 \t\t\t\t\t\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">36.5% \t\t\t\t\t\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">129 \t\t\t\t\t\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">36.8% \t\t\t\t\t\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 \t\t\t\t\t\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">37.0% \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">Unknown \t\t\t\t\t\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 \t\t\t\t\t\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">1.5% \t\t\t\t\t\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 \t\t\t\t\t\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">2.6% \t\t\t\t\t\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 \t\t\t\t\t\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">1.4% \t\t\t\t\t\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 \t\t\t\t\t\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">1.7% \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="9" align="left" valign="\n \t\t\t\t\ttop\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="9" align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Social Hx+Co-morbidieties</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Current Smoker \t\t\t\t\t\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 \t\t\t\t\t\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">3.6% \t\t\t\t\t\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 \t\t\t\t\t\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">7.9% \t\t\t\t\t\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">29 \t\t\t\t\t\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">8.3% \t\t\t\t\t\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">54 \t\t\t\t\t\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">6.6% \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">Asthma \t\t\t\t\t\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 \t\t\t\t\t\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">3.6% \t\t\t\t\t\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 \t\t\t\t\t\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">7.9% \t\t\t\t\t\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">29 \t\t\t\t\t\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">8.3% \t\t\t\t\t\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">54 \t\t\t\t\t\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">6.6% \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">COPD \t\t\t\t\t\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">22 \t\t\t\t\t\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">8.0% \t\t\t\t\t\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">13 \t\t\t\t\t\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">6.8% \t\t\t\t\t\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">20 \t\t\t\t\t\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">5.7% \t\t\t\t\t\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">55 \t\t\t\t\t\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">6.7% \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">HTN \t\t\t\t\t\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">154 \t\t\t\t\t\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">56.2% \t\t\t\t\t\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">100 \t\t\t\t\t\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">52.6% \t\t\t\t\t\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">147 \t\t\t\t\t\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">41.9% \t\t\t\t\t\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">401 \t\t\t\t\t\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">49.2% \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">Diabetes \t\t\t\t\t\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">108 \t\t\t\t\t\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">39.4% \t\t\t\t\t\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 \t\t\t\t\t\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">36.8% \t\t\t\t\t\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">106 \t\t\t\t\t\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">30.2% \t\t\t\t\t\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">284 \t\t\t\t\t\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">34.8% \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">CKD \t\t\t\t\t\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">52 \t\t\t\t\t\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">19.0% \t\t\t\t\t\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">50 \t\t\t\t\t\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">26.3% \t\t\t\t\t\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">68 \t\t\t\t\t\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">19.4% \t\t\t\t\t\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">170 \t\t\t\t\t\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">20.9% \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">HIV \t\t\t\t\t\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">2 \t\t\t\t\t\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">0.7% \t\t\t\t\t\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 \t\t\t\t\t\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">2.6% \t\t\t\t\t\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 \t\t\t\t\t\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">1.4% \t\t\t\t\t\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">12 \t\t\t\t\t\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">1.5% \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 ; 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Rolling updates on coronavirus disease (COVID-19). 2020. <a target="_blank" href="https://www.who.int/emergencies/diseases/novel-coronavirus-2019/events-as-they-happen">https://www.who.int/emergencies/diseases/novel-coronavirus-2019/events-as-they-happen</a>." ] ] ] 1 => array:3 [ "identificador" => "bib0035" "etiqueta" => "2" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Clinical characteristics of covid-19 in New York City" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "P. Goyal" 1 => "J.J. Choi" 2 => "L.C. Pinheiro" 3 => "E.J. Schenck" 4 => "R. Chen" 5 => "A. Jabri" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1056/nejmc2010419" "Revista" => array:3 [ "tituloSerie" => "N Engl J Med" "fecha" => "2020" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/32222134" "web" => "Medline" ] ] ] ] ] ] ] ] 2 => array:3 [ "identificador" => "bib0040" "etiqueta" => "3" "referencia" => array:1 [ 0 => array:1 [ "referenciaCompleta" => "Ferrer R. COVID-10 Pandemic: the greatest challenge in the history of critical care. Medicina Intensiva. 2020, <a target="_blank" href="https://doi.org/10.1016/j.medine.2020.04.005">https://doi.org/10.1016/j.medine.2020.04.005</a>" ] ] ] 3 => array:3 [ "identificador" => "bib0050" "etiqueta" => "4" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Prediction models – development, evaluation, and clinical application" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:3 [ 0 => "M.J. Pencina" 1 => "B.A. Goldstein" 2 => "R.B. D’Agostino" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1056/NEJMp2000589" "Revista" => array:3 [ "tituloSerie" => "N Engl J Med" "fecha" => "2020" "link" => array:1 [ 0 => array:2 [ "url" => "https://www.ncbi.nlm.nih.gov/pubmed/32222134" "web" => "Medline" ] ] ] ] ] ] ] ] 4 => array:3 [ "identificador" => "bib0045" "etiqueta" => "5" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Intensive care during the 2019-coronavirus epidemic" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:6 [ 0 => "A. Gonzalez-Castro" 1 => "P. Escudero-Acha" 2 => "Y. Peñascoa" 3 => "O. Leizaolab" 4 => "V. Martínez de Pinillos Sánchez" 5 => "A. García de Lorenzo" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.1016/j.medine.2020.06.001" "Revista" => array:2 [ "tituloSerie" => "Medicina Intensiva" "fecha" => "2020" ] ] ] ] ] ] ] ] ] ] "agradecimientos" => array:1 [ 0 => array:4 [ "identificador" => "xack564231" "titulo" => "Acknowledgements" "texto" => "We would like to acknowledge the contributions of the following, without whom this work would not have been possible: Dr. David Reich, President and COO of Mount Sinai Hospitals for his leadership, the staff of Mount Sinai Hospitals for their dedication and care in documenting the care of and caring for patients; the Department of Scientific Computing at the Icahn School of Medicine at Mount Sinai for the collation of this data." 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Scientific Letter

Early mortality risk stratification after SARS-CoV-2 infection

Estratificación de riesgo de mortalidad temprana en infección por SARS-CoV-2

D.J. Lundona,

Corresponding author

Dara.Lundon@mountsinai.org

Corresponding authors.

, B.D. Kellyb, S. Naira, D.M. Boltonb, N. Kyprianoua, P. Wiklunda, A. Tewaria,

Corresponding author

Ash.Tewari@mountsinai.org

Corresponding authors.

a Department of Urology, Icahn School of Medicine, Mount Sinai Hospitals, New York, United States

b Department of Urology, University of Melbourne, Austin Health, Melbourne, United States

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        "titulo" => "Estratificaci&#243;n de riesgo de mortalidad temprana en infecci&#243;n por SARS-CoV-2"
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          "en" => "<p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">&#40;a&#41; Correlation matrix demonstrating the statistically significant &#40;<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;05&#41; correlations between risk factors and the &#8216;outcome&#8217; variable of death within 7 days from symptom onset&#58; the larger the size and the stronger the color&#44; the higher the correlation&#46; &#40;b&#41; Receiver operating characteristic curves and AUC values for the model constructed to predict death within 7 days from the listed variables &#40;Age&#44; Sex&#44; Systolic BP&#44; Max Temperature&#44; O<span class="elsevierStyleInf">2</span> saturations and Zip code&#41; in the independent holdout dataset demonstrating the discriminative ability of this model &#40;AUC<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>0&#46;8612&#59; 95&#37; CI 0&#46;804&#8211;0&#46;919&#41;&#46; &#40;c&#41; Calibration curves of the model in the holdout dataset demonstrating the agreement between predicted and observed probabilities of a positive Covid-19 test&#46; &#40;d&#41; Decision curve analysis for positive Covid-19 test prediction in the holdout dataset&#46; &#40;e&#41; Screenshot of the deployed web-app which is freely available for use online at The risk tool is freely available online at <span class="elsevierStyleInterRef" id="intr0005" href="http://www.tiny.cc/covidcalc">www&#46;tiny&#46;cc&#47;covidcalc</span> &#40;f&#41; Kaplan&#8211;Meier Curve of risk strata as defined by the derived risk model derived from 3 demographic and 4 clinical features&#46;&#59; risk strata of low &#40;33&#37; risk of death within 7 days&#41; intermediate &#40;34&#8211;66&#37;&#41; and high risk &#40;&#62;66&#37;&#41; are plotted in an independent hold-out test set from the NY cohort&#46;</p>"
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    "textoCompleto" => "<span class="elsevierStyleSections"><p id="par0005" class="elsevierStylePara elsevierViewall">As of June 3rd there are over 6&#46;4 million confirmed cases in over 188 countries and territories and 382&#44;435 confirmed deaths&#46;<a class="elsevierStyleCrossRef" href="#bib0030"><span class="elsevierStyleSup">1</span></a> New York has reported 30&#44;007 of these deaths&#44; or 7&#46;8&#37; of all COVID-19 deaths in the world to date&#44; and has just passed the inevitable mantle of epicenter of the pandemic to Latin America&#46;<a class="elsevierStyleCrossRef" href="#bib0035"><span class="elsevierStyleSup">2</span></a></p><p id="par0010" class="elsevierStylePara elsevierViewall">Discerning those patients whom are most likely to die due to COVID-19 is a challenge given the novelty of the syndrome&#46; A better understanding of which patient progresses to serious disease with critical need of care&#44; and which patient experiences rapid recovery could help better inform our understanding of the COVID-19 syndrome and better care for all patients&#46;<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">3</span></a> Such information could also assist in the allocation of intensive care beds&#44; ventilators&#44; and planning palliative care and hospice support&#46;</p><p id="par0015" class="elsevierStylePara elsevierViewall">While the majority of those who test positive for SARS-CoV-2 will not require hospital admission&#44; intensive care or mechanical ventilation&#44; some will &#40;data from our institution suggest &#8764;13&#37; of those hospitalized due to COVID-19 underwent mechanical ventilation&#41;&#46; Concerns over a shortage of ventilators have transformed into commentary about ventilators being overused&#44; while it is now shortages of medications that are anticipated&#46;<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">3</span></a></p><p id="par0020" class="elsevierStylePara elsevierViewall">A recent publication outlined the unprecedented opportunities of prediction models to inform&#44; personalize and improve care&#44; as well as reiterating principles for the creation and use of such models&#46;<a class="elsevierStyleCrossRef" href="#bib0050"><span class="elsevierStyleSup">4</span></a> Using the Mount Sinai Health Systems&#8217; database of all SARS-Co-V-2 positive patients with an encounter in New York City&#44; we developed a prediction model to identify those patients most likely to succumb to the disease thin 7 days &#40;the median time to death of those who died&#41; from the onset of COVID-19 symptoms&#46; We did this by first identifying all those whom died following a positive SARS-CoV-2 test&#44; and all of those whom were positive for SARS-CoV-2&#44; but surviving after at least 7 days post-diagnosis &#40;<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>&#41;&#46; The data was randomly divided into a training and independent holdout dataset &#40;80&#37;&#58;20&#37;&#41;&#46; Feature selection included least absolute shrinkage and selection operator &#40;LASSO&#41; regression method&#46; Prediction models were built using machine learning methods including SVM&#44; k-NN and random forest&#46; The performance of the constructed model was assessed by area under the receiver operating characteristic curve &#40;AUC&#41;&#44; calibration &#40;calibration plots and Hosmer&#8211;Lemeshow calibration test&#41; and clinical utility assessed by decision curve analysis&#46;</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><p id="par0025" class="elsevierStylePara elsevierViewall">During the study period&#44; there were 14&#44;651 patients who tested positive for SARS-CoV-2&#59; complete data was available for 5152&#46; Of these 464 &#40;9&#37;&#41; died&#59; the median time to death from the onset of symptoms was 7 days in this cohort&#46;<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">5</span></a> Patients who had been in hospital for at least 7 days and had not deceased accounted for 6&#46;8&#37; of this cohort &#40;<span class="elsevierStyleItalic">N</span><span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>351&#41; &#40;<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>&#41;&#46; Significant predictors of mortality within 7 days of symptom onset &#40;as identified by the feature selection method described above&#41; include age&#44; gender&#44; respiratory rate&#44; oxygen saturation level&#44; systolic blood pressure&#44; maximum temperature and zip code &#40;<a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>A&#41;&#46;</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><p id="par0030" class="elsevierStylePara elsevierViewall">The risk probability of death within 7 days for patients was calculated from a model developed in 80&#37; of this cohort&#44; using the 3 demographic and 4 clinical variables listed above&#46; The model was assessed in the independent hold-out group using discriminative ability &#40;<a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>B&#58; AUC 0&#46;861&#41;&#44; calibration curves&#59; which demonstrate that the magnitude of the assigned probabilities track the relative frequency of the observed outcomes &#40;<a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>C&#41; and decision curve analysis &#40;<a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>D&#41; which demonstrates the model offers a superior net benefit across a greater range of probabilities than other common strategies&#46; Patients were stratified according to their calculated risk probability as follows&#58; low risk &#60;33&#37;&#44; intermediate&#44; 34&#8211;66&#37;&#44; and high risk &#62;66&#37;&#46; Patients classified as low risk by this model have a statistically significant longer survival than those in the intermediate and high risk groups &#40;<span class="elsevierStyleItalic">p</span><span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#46;0001&#41; &#40;<a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a>F&#41;&#46;</p><p id="par0035" class="elsevierStylePara elsevierViewall">This model demonstrates the significant role that both clinical and social determinants play in predicting the clinical outcome for patients infected with SARS-CoV-2 and tested positive for COVID-19&#46; The methods used to develop this risk stratification tool can be easily employed to reproduce a time and location specific model&#44; for other cities and other countries as this pandemic continues to evolve and unfold globally&#46; Such tools can aide clinicians in decision making&#44; and provide health systems with insights to allow them better allocate resources in response to this pandemic&#46;</p></span>"
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          "leyenda" => "<p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">Percentages may not total 100&#44; due to rounding&#46; RIP in &#8805;7 days was chosen as a cut point as the median time to death from the onset of symptoms was 7 days&#46; AA&#58; refers to African American&#59; COPD&#58; chronic obstructive pulmonary disease&#59; HTN&#58; hypertension&#59; CKD&#58; chronic kidney disease&#59; HIV&#58; positive for human immunodeficiency virus&#46;</p>"
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                  "referenciaCompleta" => "World Health Organization&#46; Rolling updates on coronavirus disease &#40;COVID-19&#41;&#46; 2020&#46; <a target="_blank" href="https://www.who.int/emergencies/diseases/novel-coronavirus-2019/events-as-they-happen">https&#58;&#47;&#47;www&#46;who&#46;int&#47;emergencies&#47;diseases&#47;novel-coronavirus-2019&#47;events-as-they-happen</a>&#46;"
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                      "titulo" => "Clinical characteristics of covid-19 in New York City"
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                          "autores" => array:6 [
                            0 => "P&#46; Goyal"
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                            2 => "L&#46;C&#46; Pinheiro"
                            3 => "E&#46;J&#46; Schenck"
                            4 => "R&#46; Chen"
                            5 => "A&#46; Jabri"
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                    0 => array:2 [
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                        "tituloSerie" => "N Engl J Med"
                        "fecha" => "2020"
                        "link" => array:1 [
                          0 => array:2 [
                            "url" => "https://www.ncbi.nlm.nih.gov/pubmed/32222134"
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              "etiqueta" => "3"
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                  "referenciaCompleta" => "Ferrer R&#46; COVID-10 Pandemic&#58; the greatest challenge in the history of critical care&#46; Medicina Intensiva&#46; 2020&#44; <a target="_blank" href="https://doi.org/10.1016/j.medine.2020.04.005">https&#58;&#47;&#47;doi&#46;org&#47;10&#46;1016&#47;j&#46;medine&#46;2020&#46;04&#46;005</a>"
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                            0 => "M&#46;J&#46; Pencina"
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                            2 => "Y&#46; Pe&#241;ascoa"
                            3 => "O&#46; Leizaolab"
                            4 => "V&#46; Mart&#237;nez de Pinillos S&#225;nchez"
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        "identificador" => "xack564231"
        "titulo" => "Acknowledgements"
        "texto" => "<p id="par0045" class="elsevierStylePara elsevierViewall">We would like to acknowledge the contributions of the following&#44; without whom this work would not have been possible&#58; Dr&#46; David Reich&#44; President and COO of Mount Sinai Hospitals for his leadership&#44; the staff of Mount Sinai Hospitals for their dedication and care in documenting the care of and caring for patients&#59; the Department of Scientific Computing at the Icahn School of Medicine at Mount Sinai for the collation of this data&#46;</p>"
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    "en" => array:2 [
      "titulo" => "Scientific letters"
      "idiomaDefecto" => true
    ]
    "idiomaDefecto" => "en"
  ]
  "PDF" => "https://static.elsevier.es/multimedia/21735727/0000004500000008/v1_202110280705/S2173572721001235/v1_202110280705/en/main.pdf?idApp=WMIE&text.app=https://medintensiva.org/"
  "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S2173572721001235?idApp=WMIE"
]

Article information

ISSN: 21735727
Original language: English

DOI:10.1016/j.medine.2020.06.014

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2024 November	4	2	6
2024 October	58	53	111
2024 September	50	32	82
2024 August	51	37	88
2024 July	52	31	83
2024 June	50	42	92
2024 May	47	29	76
2024 April	58	30	88
2024 March	43	26	69
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2023 December	55	43	98
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2023 October	45	27	72
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2023 June	37	13	50
2023 May	57	38	95
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2023 March	50	34	84
2023 February	41	31	72
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2022 December	63	36	99
2022 November	57	31	88
2022 October	71	59	130
2022 September	46	36	82
2022 August	47	43	90
2022 July	41	45	86
2022 June	31	32	63
2022 May	38	37	75
2022 April	41	31	72
2022 March	49	63	112
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2021 December	18	0	18
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