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COVID-19 mortality risk assessment: An international multi-center study

Dimitris BertsimasOperations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of AmericaGalit LukinOperations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of AmericaLuca MingardiOperations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of AmericaOmid NohadaniAgni OrfanoudakiOperations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of AmericaBartolomeo StellatoOperations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of AmericaHolly WibergOperations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of AmericaSara González-GarcíaInstitute of Biomedicine of Seville (IBIS), Virgen del Rocío University Hospital, CSIC, University of Seville, Seville, SpainCarlos Luís Parra-CalderónInstitute of Biomedicine of Seville (IBIS), Virgen del Rocío University Hospital, CSIC, University of Seville, Seville, SpainKenneth J. RobinsonHartford HealthCare, Hartford, Connecticut, United States of AmericaMichelle SchneiderHartford HealthCare, Hartford, Connecticut, United States of AmericaBarry SteinHartford Financial Services (United States)Alberto EstiradoHM Hospitals, Madrid, SpainLia a BeccaraAzienda Socio-Sanitaria Territoriale di Cremona, Cremona, ItalyRosario CaninoAzienda Socio-Sanitaria Territoriale di Cremona, Cremona, ItalyMartina Dal BelloPhysics of Living Systems, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of AmericaFederica PezzettiAzienda Socio-Sanitaria Territoriale di Cremona, Cremona, ItalyAngelo PanAzienda Socio-Sanitaria Territoriale di Cremona, Cremona, ItalyThe Hellenic COVID-19 Study Group

2020en

ABI

Annotatsiya

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. 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. 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. 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.

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DOI: 10.1371/journal.pone.0243262

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