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

PLoS One. 2020 Dec 9;15(12):e0243262. doi: 10.1371/journal.pone.0243262. eCollection 2020.

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 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.

Publication types

  • Clinical Trial
  • Multicenter Study
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Aged
  • Aged, 80 and over
  • Algorithms*
  • COVID-19 / blood
  • COVID-19 / diagnosis
  • COVID-19 / mortality*
  • COVID-19 / therapy
  • Europe / epidemiology
  • Female
  • Hospital Mortality*
  • Humans
  • Male
  • Middle Aged
  • Models, Biological*
  • Risk Assessment
  • Risk Factors
  • SARS-CoV-2*
  • United States / epidemiology

Grants and funding

HW is supported by the National Science Foundation Graduate Research Fellowship under Grant No. 174530. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors(s) and do not necessarily reflect the views of the National Science Foundation. This work was partially supported by a grant from c3.ai for COVID-19 related research; this organization played no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Benefits Science Technologies provided support in the form of salaries for ON, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Hartford HealthCare provided support in the form of salaries for KN, MS, and BS, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section. No further funding was provided for the study.