The Coronavirus Disease-19 (COVID-19) caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a major cause of intensive care unit (ICU) admissions globally. Robust data of epidemiology, characteristics, and disease outcomes from different regions and populations showed considerable variations. However, limited number of reports addressed predictors of mortality utilizing machine learning methods. Herein, we aimed to describe the association and relationship of a predefined set of variables found to be predictive of 28–day ICU outcome among adults COVID-19 patients admitted to the ICU using a machine learning decision tree (DT) algorithm.
This was a prospective/retrospective, multicenter cohort study from 14 hospitals in Saudi Arabia. We included critically ill COVID-19 patients admitted to the ICU between March 1, 2020, and October 31, 2020. The primary outcome was 28-day ICU mortality. Secondary outcomes were 90-day mortality and ICU length of stay. The predictors of mortality were identified using two predictive models, the conventional logistic regression and DT analysis.
A total of 1468 critically ill COVID-19 patients were included. The mean age was 55.9 (SD±15.1) years, with 74% of the patients were males. The 28-day ICU mortality was 540 (36.8%), while 90-day mortality was 600 (40.9%). The multivariable logistic regression model demonstrated that the PaO2/FiO2 ratio on ICU admission and the need for intubation or vasopressors could strongly predict 28-day ICU mortality. The DT algorithm identified five variables [need for intubation, need for vasopressors, age, gender, and PaO2/FiO2 ratio] provided in an algorithmic fashion to predict 28-day ICU outcome.
Five clinical predictors of 28-day ICU outcome were identified using DT algorithmic analysis of COVID-19 patients admitted to ICU. The findings of this DT analysis may be used in ICU for early identification of critically ill COVID-19 patients who are at high risk of 28-day mortality.