TY - JOUR
T1 - Utilization of machine-learning models to accurately predict the risk for critical COVID-19
AU - Assaf, Dan
AU - Gutman, Ya’ara
AU - Neuman, Yair
AU - Segal, Gad
AU - Amit, Sharon
AU - Gefen-Halevi, Shiraz
AU - Shilo, Noya
AU - Epstein, Avi
AU - Mor-Cohen, Ronit
AU - Biber, Asaf
AU - Rahav, Galia
AU - Levy, Itzchak
AU - Tirosh, Amit
N1 - Publisher Copyright:
© 2020, Società Italiana di Medicina Interna (SIMI).
PY - 2020/11/1
Y1 - 2020/11/1
N2 - 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.
AB - 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.
KW - COVID-19
KW - Disease severity
KW - Machine learning
KW - Prediction
KW - Risk stratification
UR - http://www.scopus.com/inward/record.url?scp=85089550529&partnerID=8YFLogxK
U2 - 10.1007/s11739-020-02475-0
DO - 10.1007/s11739-020-02475-0
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C2 - 32812204
AN - SCOPUS:85089550529
SN - 1828-0447
VL - 15
SP - 1435
EP - 1443
JO - Internal and Emergency Medicine
JF - Internal and Emergency Medicine
IS - 8
ER -