Abstract
Background
Coronavirus disease-19 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and is currently a major cause of intensive care unit (ICU) admissions globally. The role of machine learning in the ICU is evolving but currently limited to diagnostic and prognostic values. A decision tree (DT) algorithm is a simple and intuitive machine learning method that provides sequential nonlinear analysis of variables. It is simple and might be a valuable tool for bedside physicians during COVID-19 to predict ICU outcomes and help in critical decision-making like end-of-life decisions and bed allocation in the event of limited ICU bed capacities. Herein, we utilized a machine learning DT algorithm to describe the association of a predefined set of variables and 28-day ICU outcome in adult COVID-19 patients admitted to the ICU. We highlight the value of utilizing a machine learning DT algorithm in the ICU at the time of a COVID-19 pandemic.
Methods
This was a prospective and multicenter cohort study involving 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 predictors of 28-day ICU mortality were identified using two predictive models: conventional logistic regression and DT analyses.
Results
There were 1468 critically ill COVID-19 patients included in the study. The 28-day ICU mortality was 540 (36.8 %), and the 90-day mortality was 600 (40.9 %). The DT algorithm identified five variables that were integrated into the algorithm to predict 28-day ICU outcomes: need for intubation, need for vasopressors, age, gender, and PaO2/FiO2 ratio.
Conclusion
DT is a simple tool that might be utilized in the ICU to identify critically ill COVID-19 patients who are at high risk of 28-day ICU mortality. However, further studies and external validation are still required.
【저자키워드】 COVID-19, SARS-CoV2, predictors, ICU, Decision tree, 【초록키워드】 SARS-CoV-2, coronavirus, Mortality, intensive care, COVID-19 pandemic, hospital, diagnostic, intubation, Gender, outcome, Saudi Arabia, Critically ill, Algorithm, Logistic regression, age, predictor, prognostic, Admission, Critical, predict, association, Analysis, COVID-19 patient, external validation, high risk, Predictive, acute respiratory syndrome, help, PaO2/FiO2 ratio, vasopressors, physician, variable, FIVE, highlight, multicenter cohort study, Result, identify, caused, required, provide, analyses, adult COVID-19 patient, variables, 【제목키워드】 Mortality, Critically ill, Algorithm, machine, adult COVID-19 patient,