Mortality prediction of patients with sepsis in the emergency department using machine learning models: a retrospective cohort study according to the Sepsis-3 definitions

Although clinical scoring systems and biomarkers have been used to predict outcomes in sepsis, their prognostic value is limited. Therefore, machine learning (ML) models have been proposed to predict the outcomes of sepsis. This study aims to propose ML algorithms that create robust models for predicting mortality in patients with sepsis diagnosed using the Sepsis-3 definitions in the emergency department. This study was performed using a prospectively collected registry of adult patients with sepsis between January 2016 and February 2020. Among the 810 patients, 607 (75%) and 203 (25%) patients were assigned to the training and test sets, respectively. The primary outcome was 30-day mortality. Using the values of the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC), balanced accuracy, and Brier score, we compared the performances of different ML algorithms with that of the logistic regression models and clinical scoring systems. The ML models' performance was superior to that of the clinical scoring systems. A light gradient boosting machine achieved the highest AUROC among the ML models in predicting 30-day mortality. Most of the ML models had significantly higher AUROC and balanced accuracy than the logistic regression models. All the ML models exhibited higher AUPRC and lower Brier scores compared to the scoring systems and logistic regression model. The ML models can be used as supportive tools for predicting mortality in sepsis patients. In future studies, the performance of the proposed models will be validated using more data from different hospitals or departments.