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Title: COVID-19 mortality risk assessment: An international multi-center study
Authors: Bertsimas, Dimitris
Lukin, Galit
Mingardi, Luca
Nohadani, Omid
Orfanoudaki, Agni
Stellato, Bartolomeo
Wiberg, Holly
Gonzalez-Garcia, Sara
Parra-Calderón, Carlos Luis
Robinson, Kenneth
Schneider, Michelle
Stein, Barry
Estirado, Alberto
A Beccara, Lia
Canino, Rosario
Dal Bello, Martina
Pezzetti, Federica
Pan, Angelo
metadata.dc.contributor.authoraffiliation: [Bertsimas,D; Mingardi,L; Stellato,B] Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America. [Bertsimas,D; Lukin,G; Mingardi,L; Orfanoudaki,A; Stellato,B; Wiberg,H] Operations Research Center, Massachusetts Institute of Technology, Cambridge,Massachusetts, United States of America. [Nohadani,O] Benefits Science Technologies, Boston, Massachusetts, UnitedStates of America. [Gonzalez-Garcia,S; Parra-Calderón,CL] Institute of Biomedicine of Seville (IBIS), Virgen del Rocío University Hospital, CSIC, University of Seville, Seville, Spain. [Robinson,K; Schneider,M; Stein,B] Hartford HealthCare, Hartford, Connecticut, United States of America. [Estirado,A] HM Hospitals, Madrid, Spain. [A Beccara,L; Canino,R; Pezzetti,F; Pan,A] Azienda Socio-Sanitaria Territoriale di Cremona, Cremona, Italy. [Dall Bello,M] Physics of Living Systems, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America. The Hellenic COVID-19 Study Group
Keywords: COVID-19;Hospital mortality;Aged, 80 and over;Aged;Europe;United States;Mortalidad hospitalaria;Anciano de 80 o más años;Ancianos;Factores de riesgo;Estados Unidos;Europa
metadata.dc.subject.mesh: Medical Subject Headings::Persons::Persons::Age Groups::Adult::Aged
Medical Subject Headings::Persons::Persons::Age Groups::Adult::Aged::Aged, 80 and over
Medical Subject Headings::Diseases::Virus Diseases::RNA Virus Infections::Nidovirales Infections::Coronaviridae Infections::Coronavirus Infections
Medical Subject Headings::Geographical Locations::Geographic Locations::Europe
Medical Subject Headings::Check Tags::Male
Medical Subject Headings::Persons::Persons::Age Groups::Adult::Middle Aged
Medical Subject Headings::Health Care::Environment and Public Health::Public Health::Epidemiologic Measurements::Risk Assessment
Medical Subject Headings::Health Care::Environment and Public Health::Public Health::Epidemiologic Factors::Causality::Risk Factors
Medical Subject Headings::Geographical Locations::Geographic Locations::Americas::North America::United States
Medical Subject Headings::Phenomena and Processes::Mathematical Concepts::Algorithms
Medical Subject Headings::Health Care::Population Characteristics::Demography::Vital Statistics::Mortality::Hospital Mortality
Medical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Models, Theoretical::Models, Biological
Issue Date: 9-Dec-2020
Citation: Bertsimas D, Lukin G, Mingardi L, Nohadani O, Orfanoudaki A, Stellato B, et al. COVID-19 mortality risk assessment: An international multi-center study. PLoS One. 2020 Dec 9;15(12):e0243262.
Abstract: 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 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.
metadata.dc.identifier.doi: 10.1371/journal.pone.0243262
ISSN: 1932-6203 (Online)
Appears in Collections:01- Artículos - IBIS. Instituto de Biomedicina de Sevilla

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