Machine learning models to evaluate mortality in pediatric patients with pneumonia in the intensive care unit

Abstract Objectives This study aimed to predict mortality in children with pneumonia who were admitted to the intensive care unit (ICU) to aid decision‐making. Study Design Retrospective cohort study conducted at a single tertiary hospital. Patients This study included children who were admitted to the pediatric ICU at the National Taiwan University Hospital between 2010 and 2019 due to pneumonia. Methodology Two prediction models were developed using tree‐structured machine learning algorithms. The primary outcomes were ICU mortality and 24‐h ICU mortality. A total of 33 features, including demographics, underlying diseases, vital signs, and laboratory data, were collected from the electronic health records. The machine learning models were constructed using the development data set, and performance matrices were computed using the holdout test data set. Results A total of 1231 ICU admissions of children with pneumonia were included in the final cohort. The area under the receiver operating characteristic curves (AUROCs) of the ICU mortality model and 24‐h ICU mortality models was 0.80 (95% confidence interval [CI], 0.69–0.91) and 0.92 (95% CI, 0.86–0.92), respectively. Based on feature importance, the model developed in this study tended to predict increased mortality for the subsequent 24 h if a reduction in the blood pressure, peripheral capillary oxygen saturation (SpO2), or higher partial pressure of carbon dioxide (PCO2) were observed. Conclusions This study demonstrated that the machine learning models for predicting ICU mortality and 24‐h ICU mortality in children with pneumonia have the potential to support decision‐making, especially in resource‐limited settings.