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COVID-19 Mortality Risk Assessment: An International Multi-Center Study

36 Pages Posted: 2 Oct 2020

See all articles by Dimitris Bertsimas

Dimitris Bertsimas

Massachusetts Institute of Technology (MIT) - Sloan School of Management

Galit Lukin

Massachusetts Institute of Technology (MIT) - Operations Research Center

Luca Mingardi

Massachusetts Institute of Technology (MIT) - Sloan School of Management

Omid Nohadani

Benefits Science Technologies

Agni Orfanoudaki

Massachusetts Institute of Technology (MIT) - Operations Research Center

Bartolomeo Stellato

Massachusetts Institute of Technology (MIT) - Sloan School of Management

Holly Wiberg

Massachusetts Institute of Technology (MIT) - Operations Research Center

José Miguel Cisneros-Herreros

University of Seville - Institute of Biomedicine of Seville

Sara Gonzalez-Garcia

University of Seville - Institute of Biomedicine of Seville

Carlos Luis Parra-Calderón

University of Seville - Institute of Biomedicine of Seville

The Hellenic COVID-19 Study Group

Independent

Kenneth Robinson

Hartford HealthCare

Michelle Schneider

Hartford HealthCare

Barry Stein

Hartford HealthCare

Alberto Estirado

HM Hospitals

Lia a Beccara

ASST Ospedale di Cremona

Rosario Canino

ASST Ospedale di Cremona

Martina Dal Bello

Massachusetts Institute of Technology (MIT) - Physics of Living Systems

Federica Pezzetti

ASST Ospedale di Cremona

Angelo Pan

ASST Ospedale di Cremona

More...

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. 

Funding: No funding was provided.

Declaration of Interests: All authors have no competing interests to declare that may be relevant to the submitted work.

Ethics Approval Statement: All independent organizations and the Massachusetts Institute of Technology institutional review boards approved this protocol as minimal-risk research using data collected for standard clinical practice and waived the requirement for informed consent. The survey was anonymous and confidentiality of information was assured.

Suggested Citation

Bertsimas, Dimitris and Lukin, Galit and Mingardi, Luca and Nohadani, Omid and Orfanoudaki, Agni and Stellato, Bartolomeo and Wiberg, Holly and Cisneros-Herreros, José Miguel and Gonzalez-Garcia, Sara and Parra-Calderón, Carlos Luis and Group, The Hellenic COVID-19 Study and Robinson, Kenneth and Schneider, Michelle and Stein, Barry and Estirado, Alberto and Beccara, Lia a and Canino, Rosario and Bello, Martina Dal and Pezzetti, Federica and Pan, Angelo, COVID-19 Mortality Risk Assessment: An International Multi-Center Study (6/24/2020). Available at SSRN: https://ssrn.com/abstract=3638294 or http://dx.doi.org/10.2139/ssrn.3638294

Dimitris Bertsimas (Contact Author)

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

E53-359
Cambridge, MA 02142
United States
617-253-4223 (Phone)
617-258-7579 (Fax)

Galit Lukin

Massachusetts Institute of Technology (MIT) - Operations Research Center

77 Massachusetts Avenue
Bldg. E 40-149
Cambridge, MA 02139
United States

Luca Mingardi

Massachusetts Institute of Technology (MIT) - Sloan School of Management

100 Main Street
E62-416
Cambridge, MA 02142
United States

Omid Nohadani

Benefits Science Technologies

Agni Orfanoudaki

Massachusetts Institute of Technology (MIT) - Operations Research Center

77 Massachusetts Avenue
Bldg. E 40-149
Cambridge, MA 02139
United States

Bartolomeo Stellato

Massachusetts Institute of Technology (MIT) - Sloan School of Management

100 Main Street
E62-416
Cambridge, MA 02142
United States

Holly Wiberg

Massachusetts Institute of Technology (MIT) - Operations Research Center

77 Massachusetts Avenue
Bldg. E 40-149
Cambridge, MA 02139
United States

José Miguel Cisneros-Herreros

University of Seville - Institute of Biomedicine of Seville

Seville, 41013
Spain

Sara Gonzalez-Garcia

University of Seville - Institute of Biomedicine of Seville

Seville, 41013
Spain

Carlos Luis Parra-Calderón

University of Seville - Institute of Biomedicine of Seville

Seville, 41013
Spain

Kenneth Robinson

Hartford HealthCare

Michelle Schneider

Hartford HealthCare

Barry Stein

Hartford HealthCare

Alberto Estirado

HM Hospitals

Lia a Beccara

ASST Ospedale di Cremona

Italy

Rosario Canino

ASST Ospedale di Cremona

Italy

Martina Dal Bello

Massachusetts Institute of Technology (MIT) - Physics of Living Systems

77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
United States

Federica Pezzetti

ASST Ospedale di Cremona

Italy

Angelo Pan

ASST Ospedale di Cremona

Italy

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