JMIR Publications

ABSTRACT

Background:

Prioritizing patients in need of intensive care is necessary to reduce the mortality rate during the global pandemic of the coronavirus disease 2019 (COVID-19). Although several scoring methods have been introduced, many require laboratory or radiographic findings that may not be easily available in certain situations.

Objective:

The purpose of this study was to develop a machine learning model that predicts the need for intensive care for COVID-19 patients with easily providable characteristics, limited to baseline demographics, comorbidities, and symptoms.

Methods:

A retrospective study was performed using a nationwide cohort in South Korea. Patients admitted to 100 hospitals from January 25th, 2020 to June 3rd, 2020 were included. Patient information was collected retrospectively by the attending physicians in each hospital and uploaded to an online case report form. Variables that could be easily provided were extracted. The variables were age, sex, smoking history, body temperature, comorbidities, activities of daily living, and symptoms. The primary outcome was the need for intensive care, defined as admission to the intensive care unit, use of extracorporeal life support, mechanical ventilation, vasopressors, or death within 30 days of hospitalization. Patients admitted until March 20th were included in the derivation group to develop prediction models using an automated machine learning technique. The models were externally validated in patients admitted after March 21st, 2020. The machine learning model with the best discrimination performance was selected and compared against CURB-65 using the area under the receiver operating characteristic curve (AUROC).

Results:

A total of 4787 patients were included in the analysis, of which 3294 were assigned to the derivation group and 1493 to the validation group. Among the 4787 patients, 460 (9.6%) patients needed intensive care. Of the 55 machine learning models developed, the XGBoost model revealed the highest discrimination performance. The AUROC of the XGBoost model was 0.897 (95% CI 0.825−0.847) for the derivation group and 0.885 (95% CI 0.855−0.915) for the validation group. Both the AUROCs were superior to those of CURB-65, which were 0.836 (95% CI 0.825–0.847) and 0.843 (95% CI 0.829−0.857), respectively).

Conclusions:

We developed a machine learning model comprising simple patient-provided characteristics, which can efficiently predict the need for intensive care among COVID-19 patients.