Utilization of machine-learning models to accurately predict the risk for critical COVID-19

Dan Assaf, Ya’ara Gutman, Yair Neuman, Gad Segal, Sharon Amit, Shiraz Gefen-Halevi, Noya Shilo, Avi Epstein, Ronit Mor-Cohen, Asaf Biber, Galia Rahav, Itzchak Levy, Amit Tirosh

Research output: Contribution to journalArticlepeer-review

46 Scopus citations

Abstract

Among patients with Coronavirus disease (COVID-19), the ability to identify patients at risk for deterioration during their hospital stay is essential for effective patient allocation and management. To predict patient risk for critical COVID-19 based on status at admission using machine-learning models. Retrospective study based on a database of tertiary medical center with designated departments for patients with COVID-19. Patients with severe COVID-19 at admission, based on low oxygen saturation, low partial arterial oxygen pressure, were excluded. The primary outcome was risk for critical disease, defined as mechanical ventilation, multi-organ failure, admission to the ICU, and/or death. Three different machine-learning models were used to predict patient deterioration and compared to currently suggested predictors and to the APACHEII risk-prediction score. Among 6995 patients evaluated, 162 were hospitalized with non-severe COVID-19, of them, 25 (15.4%) patients deteriorated to critical COVID-19. Machine-learning models outperformed the all other parameters, including the APACHE II score (ROC AUC of 0.92 vs. 0.79, respectively), reaching 88.0% sensitivity, 92.7% specificity and 92.0% accuracy in predicting critical COVID-19. The most contributory variables to the models were APACHE II score, white blood cell count, time from symptoms to admission, oxygen saturation and blood lymphocytes count. Machine-learning models demonstrated high efficacy in predicting critical COVID-19 compared to the most efficacious tools available. Hence, artificial intelligence may be applied for accurate risk prediction of patients with COVID-19, to optimize patients triage and in-hospital allocation, better prioritization of medical resources and improved overall management of the COVID-19 pandemic.

Original languageEnglish
Pages (from-to)1435-1443
Number of pages9
JournalInternal and Emergency Medicine
Volume15
Issue number8
DOIs
StatePublished - 1 Nov 2020

Keywords

  • COVID-19
  • Disease severity
  • Machine learning
  • Prediction
  • Risk stratification

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