COVID-19 Mortality Risk Assessment: An International Multi-Center Study | medRxiv preprints (not peer reviewed)

Type Journal Article Author Dimitris Bertsimas Author Galit Lukin Author Luca Mingardi Author Omid Nohadani Author Agni Orfanoudaki Author Bartolomeo Stellato Author Holly Wiberg Author Sara Gonzalez-Garcia Author Carlos Luis Parra-Calderon Author The Hellenic COVID-19 Study Group Author Kenneth Robinson Author Michelle Schneider Author Barry Stein Author Alberto Estirado Author Lia a Beccara Author Rosario Canino Author Martina Dal Bello Author Federica Pezzetti Author Angelo Pan URL https://www.medrxiv.org/content/10.1101/2020.07.07.20148304v3 Rights © 2020, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution 4.0 International), CC BY 4.0, as described at http://creativecommons.org/licenses/by/4.0/ Pages 2020.07.07.20148304 Publication medRxiv Date 19/07/2020 Extra Publisher: Cold Spring Harbor Laboratory Press DOI 10.1101/2020.07.07.20148304 Library Catalog www.medrxiv.org Language en Abstract Background: Timely identification of COVID-19 patients at high risk of mortality can significantly improve patient management and resource allocation within hospitals. This study seeks to develop and validate a data-driven personalized mortality risk calculator for hospitalized COVID-19 patients. Methods: De-identified data was obtained for 3,927 COVID-19 positive patients from six independent centers, comprising 33 different hospitals. Demographic, clinical, and laboratory variables were collected at hospital admission. The COVID-19 Mortality Risk (CMR) tool was developed using the XGBoost algorithm to predict mortality. Its discrimination performance was subsequently evaluated on three validation cohorts. Findings: The derivation cohort of 3,062 patients has an observed mortality rate of 26.84%. Increased age, decreased oxygen saturation (≤ 93%), elevated levels of C-reactive protein (≥ 130 mg/L), blood urea nitrogen (≥ 18 mg/dL), and blood creatinine (≥ 1.2 mg/dL) were identified as primary risk factors, validating clinical findings. The model obtains out-of-sample AUCs of 0.90 (95% CI, 0.87-0.94) on the derivation cohort. In the validation cohorts, the model obtains AUCs of 0.92 (95% CI, 0.88-0.95) on Seville patients, 0.87 (95% CI, 0.84-0.91) on Hellenic COVID-19 Study Group patients, and 0.81 (95% CI, 0.76-0.85) on Hartford Hospital patients. The CMR tool is available as an online application at covidanalytics.io/mortality_calculator and is currently in clinical use. Interpretation: The CMR model leverages machine learning to generate accurate mortality predictions using commonly available clinical features. This is the first risk score trained and validated on a cohort of COVID-19 patients from Europe and the United States. Short Title COVID-19 Mortality Risk Assessment