About: Estimating Survival of Hospitalized COVID-19 Patients from Admission Information

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Estimating Survival of Hospitalized COVID-19 Patients from Admission Information
Creator	Coppa, Kevin Debnath, Shubham Douglas, ; Levy, Todd Makhnevich, Alexander Paradis, Marc Tóth, Viktor Zucker, Barbara Barnaby Bc, P Becker Ab, Lance Cohen Bc, Stuart Dominello, Andrew Davidson Bc, Karina Falzon, Louise Gitman, Michael Hirsch Bcd, Jamie Kim, Eun-Ji Mcginn, Thomas Mogavero, Jazmin Molmenti, Ernesto Richardson, Safiya Zanos Ab, Theodoros
source	MedRxiv; Medline
abstract	Background While clinical characteristics and a range of mortality risk factors of COVID-19 patients have been reported, a practical early clinical survival calculator specialized for the unique cohort of patients has not yet been introduced. Such a tool would provide timely and valuable guidance in clinical care decision-making during this global pandemic. Methods Demographic, laboratory, clinical, and treatment data (from 13 acute care facilities at Northwell Health) were extracted from electronic medical records and used to build and test the predictive accuracy of a survival probability calculator-the Northwell COVID-19 Survival (NOCOS) calculator-for hospitalized COVID-19 patients. The NOCOS calculator was constructed using multivariate regression with L1 regularization (LASSO). Model predictive performance was measured using Receiver Operating Characteristic (ROC) curves and the Area Under the Curve (AUC) of the calculators tested. Results A total of 5,233 inpatients were included in the study. Patient age, serum blood urea nitrogen (BUN), Emergency Severity Index (ESI), red cell distribution width (RCDW), absolute neutrophil count, serum bicarbonate, and glucose were identified as the optimal early predictors of survival by multivariate LASSO regression. The predictive performance of the Northwell COVID-19 Survival (NOCOS) calculator was assessed for 14 consecutive days. Conclusions We present a rapidly developed and deployed estimate of survival probability that outperforms other general risk models. The 7 early predictors of in-hospital survival can help clinicians identify patients with increased probabilities of survival and provide critical decision support.
has issue date	2020-04-27(xsd:dateTime)
bibo:doi	10.1101/2020.04.22.20075416
bibo:pmid	32511640
has license	medrxiv
sha1sum (hex)	d166c3ec40170c1ac88df8e2471934e679b296d2
schema:url	https://doi.org/10.1101/2020.04.22.20075416
resource representing a document's title	Estimating Survival of Hospitalized COVID-19 Patients from Admission Information
has PubMed identifier	32511640
schema:publication	medRxiv : the preprint server for health sciences
resource representing a document's body	covid:d166c3ec40170c1ac88df8e2471934e679b296d2#body_text
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