Development and validation of predictive models for COVID-19 outcomes in a safety-net hospital population

Author:

Hao Boran12,Hu Yang12,Sotudian Shahabeddin13,Zad Zahra13,Adams William G4,Assoumou Sabrina A5,Hsu Heather4,Mishuris Rebecca G5,Paschalidis Ioannis C1236

Affiliation:

1. Center for Information and Systems Engineering, Boston University , Boston, Massachusetts, USA

2. Department of Electrical and Computer Engineering, Boston University , Boston, Massachusetts, USA

3. Division of Systems Engineering, Boston University , Boston, Massachusetts, USA

4. Department of Pediatrics, Boston Medical Center and Boston University School of Medicine , Boston, Massachusetts, USA

5. Department of Medicine, Boston Medical Center and Boston University School of Medicine , Boston, Massachusetts, USA

6. Department of Biomedical Engineering, Boston University , Boston, Massachusetts, USA

Abstract

Abstract Objective To develop predictive models of coronavirus disease 2019 (COVID-19) outcomes, elucidate the influence of socioeconomic factors, and assess algorithmic racial fairness using a racially diverse patient population with high social needs. Materials and Methods Data included 7,102 patients with positive (RT-PCR) severe acute respiratory syndrome coronavirus 2 test at a safety-net system in Massachusetts. Linear and nonlinear classification methods were applied. A score based on a recurrent neural network and a transformer architecture was developed to capture the dynamic evolution of vital signs. Combined with patient characteristics, clinical variables, and hospital occupancy measures, this dynamic vital score was used to train predictive models. Results Hospitalizations can be predicted with an area under the receiver-operating characteristic curve (AUC) of 92% using symptoms, hospital occupancy, and patient characteristics, including social determinants of health. Parsimonious models to predict intensive care, mechanical ventilation, and mortality that used the most recent labs and vitals exhibited AUCs of 92.7%, 91.2%, and 94%, respectively. Early predictive models, using labs and vital signs closer to admission had AUCs of 81.1%, 84.9%, and 92%, respectively. Discussion The most accurate models exhibit racial bias, being more likely to falsely predict that Black patients will be hospitalized. Models that are only based on the dynamic vital score exhibited accuracies close to the best parsimonious models, although the latter also used laboratories. Conclusions This large study demonstrates that COVID-19 severity may accurately be predicted using a score that accounts for the dynamic evolution of vital signs. Further, race, social determinants of health, and hospital occupancy play an important role.

Funder

National Science Foundation

Office of Naval Research

National Institutes of Health

Boston University Clinical and Translational Science Award

Boston University Rafik B. Hariri Institute for Computing and Computational Science and Engineering

Publisher

Oxford University Press (OUP)

Subject

Health Informatics

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