Development and Validation of an Interpretable Machine Learning–Based Prediction Model of Sepsis: A Retrospective Multicenter Cohort Study

Aims/Background: Sepsis is a leading cause of morbidity and mortality in intensive care units (ICUs). Early identification of patients at high risk of death is essential to guide timely interventions and optimize resource allocation. This study aimed to develop and validate an interpretable machine learning model for predicting 28-day mortality in adult ICU patients. Methods: We retrospectively analyzed data from three independent critical care databases, including 98,233 adult ICU patients. Sixteen routinely collected variables available by the 24-hour prediction landmark—including vital signs and laboratory parameters obtained during the first 24 hours after ICU admission, baseline comorbidities documented at or before ICU admission, and treatments administered during the same 24-hour period—were selected for model development. Nine machine learning algorithms were compared, and model performance was evaluated using both internal and external validation cohorts. Explainable machine learning techniques were applied to identify the most influential predictors. Results: Among the nine algorithms, extreme gradient boosting (XGBoost) demonstrated the highest predictive performance. The model achieved an area under the receiver operating characteristic curve (AUROC) of 0.884 (0.871–0.897) in the internal validation cohort and maintained comparable discrimination in the two external validation cohorts, eICU-Sepsis, derived from the eICU Collaborative Research Database (eICU-CRD), and DRECCv1.0 critical care database (DRECC)-Sepsis, with AUROCs of 0.877 (0.872–0.882) and 0.875 (0.856–0.894), respectively. Mechanical ventilation, comorbidity burden, lactate dehydrogenase, age, respiratory rate, and blood pressure made important contributions to the model’s prediction of 28-day mortality. Conclusion: This interpretable machine learning model may facilitate early risk stratification of ICU patients with sepsis. Although the model shows potential for identifying high-risk individuals at the 24-hour prediction landmark, further prospective validation and clinical impact assessment are required before routine clinical implementation.