Prediction of patients requiring intensive care for COVID-19: development and validation of an integer-based score using data from Centers for Disease Control and Prevention of South Korea

• Published in: Journal of intensive care 2021-01, Vol.9 (1), p.16-16, Article 16
• Main Authors: Heo, JoonNyung, Han, Deokjae, Kim, Hyung-Jun, Kim, Daehyun, Lee, Yeon-Kyeng, Lim, Dosang, Hong, Sung Ok, Park, Mi-Jin, Ha, Beomman, Seog, Woong
• Format: Article
• Language: English
• Subjects: Accuracy; Activities of daily living; Age; Analysis; Body temperature; Chronic kidney failure; Coronaviruses; COVID-19; Critical care; Datasets; Disease control; Disease transmission; Dyspnea; Epidemics; Hemoptysis; Intensive care; Kidney diseases; Laboratories; Medical prognosis; Medical research; Medicine, Experimental; Mortality; Patients; Prognosis; Quarantine; Statistical analysis; Variables; Ventilators
• ISSN: 2052-0492; 2052-0492
• DOI: 10.1186/s40560-021-00527-x

Unavailability or saturation of the intensive care unit may be associated with the fatality of COVID-19. Prioritizing the patients for hospitalization and intensive care may be critical for reducing the fatality of COVID-19. This study aimed to develop and validate a new integer-based scoring system for predicting patients with COVID-19 requiring intensive care, using only the predictors available upon triage. This is a retrospective study using cohort data from the Korean Centers for Disease Control and Prevention that included all admitted patients with COVID-19 between January 19 and June 3, 2020, in South Korea. The primary outcome was patients requiring intensive care defined as actual admission to the intensive care unit; at any time use of an extracorporeal life support device, mechanical ventilation, or vasopressors; and death. Patients admitted until March 20 were included for the training dataset to develop the prediction models and externally validated for the patients admitted afterward. Two logistic regression models were developed with different predictors and the predictive performance was compared: one with patient-provided variables and the other with added radiologic and laboratory variables. An integer-based scoring system was developed based on the developed logistic regression model. A total of 5193 patients were considered, with 4663 patients included after excluding patients with age under 18 or insufficient data. For the training dataset, 3238 patients were included. Of the included patients, 444 (9.5%) patients required intensive care. The model developed with only the clinical variables showed an area under the curve of 0.884 for the validation set. The performance did not differ when radiologic and laboratory variables were added. Seven variables were selected for developing an integer-based scoring system: age, sex, initial body temperature, dyspnea, hemoptysis, history of chronic kidney disease, and activities of daily living. The area under the curve of the scoring system was 0.880. An integer-based scoring system was developed for predicting patients with COVID-19 requiring intensive care, with high performance. This system may aid decision support for prioritizing the patient for hospitalization and intensive care, particularly in a situation with limited medical resources.