Development and validation of a machine learning model predicting illness trajectory and hospital utilization of COVID-19 patients: A nationwide study

Michael Roimi, Rom Gutman, Jonathan Somer, Asaf Ben Arie, Ido Calman, Yaron Bar-Lavie, Udi Gelbshtein, Sigal Liverant-Taub, Arnona Ziv, Danny Eytan, Malka Gorfine, Uri Shalit

Research output: Contribution to journalArticlepeer-review

Abstract

Objective: The spread of coronavirus disease 2019 (COVID-19) has led to severe strain on hospital capacity in many countries. We aim to develop a model helping planners assess expected COVID-19 hospital resource utilization based on individual patient characteristics. Materials and Methods: We develop a model of patient clinical course based on an advanced multistate survival model. The model predicts the patient's disease course in terms of clinical states-critical, severe, or moderate. The model also predicts hospital utilization on the level of entire hospitals or healthcare systems. We cross-validated the model using a nationwide registry following the day-by-day clinical status of all hospitalized COVID-19 patients in Israel from March 1 to May 2, 2020 (n = 2703). Results: Per-day mean absolute errors for predicted total and critical care hospital bed utilization were 4.72 ± 1.07 and 1.68 ± 0.40, respectively, over cohorts of 330 hospitalized patients; areas under the curve for prediction of critical illness and in-hospital mortality were 0.88 ± 0.04 and 0.96 ± 0.04, respectively. We further present the impact of patient influx scenarios on day-by-day healthcare system utilization. We provide an accompanying R software package. Discussion: The proposed model accurately predicts total and critical care hospital utilization. The model enables evaluating impacts of patient influx scenarios on utilization, accounting for the state of currently hospitalized patients and characteristics of incoming patients. We show that accurate hospital load predictions were possible using only a patient's age, sex, and day-by-day clinical state (critical, severe, or moderate). Conclusions: The multistate model we develop is a powerful tool for predicting individual-level patient outcomes and hospital-level utilization.

Original languageEnglish
Pages (from-to)1188-1196
Number of pages9
JournalJournal of the American Medical Informatics Association : JAMIA
Volume28
Issue number6
DOIs
StatePublished - 12 Jun 2021

Keywords

  • Adult
  • Aged
  • Aged, 80 and over
  • COVID-19
  • Female
  • Hospitalization/statistics & numerical data
  • Hospitals/statistics & numerical data
  • Humans
  • Israel
  • Length of Stay/statistics & numerical data
  • Machine Learning
  • Male
  • Middle Aged
  • Models, Statistical
  • Prognosis
  • Proportional Hazards Models
  • Registries
  • healthcare facilities
  • hospital utilization
  • multistate model
  • survival analysis

All Science Journal Classification (ASJC) codes

  • Health Informatics

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