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Clinical predictors of COVID-19 mortality by Arjun S. Yadaw; Yan-chak Li; Sonali Bose; Ravi Iyengar; Supinda Bunyavanich; Gaurav Pandey is a scholarly article available to read on EtoBox.

What is Clinical predictors of COVID-19 mortality about?

## Background: The coronavirus disease 2019 (COVID-19) pandemic has affected over millions of individuals and caused hundreds of thousands of deaths worldwide. It can be difficult to accurately predict mortality among COVID-19 patients presenting with a spectrum of complications, hindering the prognostication and management of the disease. ## Methods: We applied machine learning techniques to clinical data from a large cohort of 5,051 COVID-19 patients treated at the Mount Sinai Health System in New York City, the global COVID-19 epicenter, to predict mortality. Predictors were designed to classify patients into Deceased or Alive mortality classes and were evaluated in terms of the area under the receiver operating characteristic (ROC) curve (AUC score). ## Findings: Using a development cohort (n=3,841) and a systematic machine learning framework, we identified a COVID-19 mortality predictor that demonstrated high accuracy (AUC=0·91) when applied to test sets of retrospective (n= 961) and prospective (n=249) patients. This mortality predictor was based on five clinical features: age, minimum O~2~ saturation during encounter, type of patient encounter (inpatient vs. various types of

Author

Arjun S. Yadaw; Yan-chak Li; Sonali Bose; Ravi Iyengar; Supinda Bunyavanich; Gaurav Pandey

Publisher

Cold Spring Harbor Laboratory

Published

2020

Language

EN