Utilization of machine-learning models to accurately predict the risk for critical COVID-19.
Intern Emerg Med
; 15(8): 1435-1443, 2020 Nov.
Article
in English
| MEDLINE | ID: covidwho-718479
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.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pneumonia, Viral
/
Coronavirus Infections
/
Risk Assessment
/
Machine Learning
Type of study:
Diagnostic study
/
Experimental Studies
/
Observational study
/
Prognostic study
Topics:
Long Covid
Limits:
Adult
/
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Language:
English
Journal:
Intern Emerg Med
Journal subject:
Emergency Medicine
/
Internal Medicine
Year:
2020
Document Type:
Article
Affiliation country:
S11739-020-02475-0
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