Development of an interpretable machine learning model for predicting new-onset atrial fibrillation in patients with sepsis-associated acute kidney injury: A retrospective cohort study

ObjectiveSepsis-associated acute kidney injury (SA-AKI) is a major contributor to multi-organ failure and often leads to complications such as new-onset atrial fibrillation (NOAF). NOAF is associated with poorer outcomes, including increased mortality. However, current methods for predicting NOAF in patients with SA-AKI remain limited.MethodsThis retrospective cohort study used data from the MIMIC-IV database to identify 12,956 adult patients with SA-AKI, among whom 2,708 developed NOAF. Machine learning (ML) techniques, including Boruta feature selection and nine predictive algorithms, were applied to identify key predictors and develop forecasting models for NOAF. Model performance was evaluated using metrics such as area under the curve (AUC), accuracy, sensitivity, specificity, and F1 score. SHapley Additive exPlanations (SHAP) values were used to enhance model interpretability and identify the most influential predictors.ResultsXGBoost demonstrated the best predictive performance, achieving an AUC of 0.83. The top predictors included age, creatinine, mean blood pressure, congestive heart failure, temperature, and anion gap. SHAP analysis confirmed the significant impact of these factors on NOAF risk. The model was further optimized by retaining eight key variables, ensuring strong predictive performance while enhancing practical applicability. A web-based platform was developed for real-time risk assessment.ConclusionsThis study presents a robust and interpretable ML model for predicting NOAF in patients with SA-AKI. By identifying critical risk factors, the model may assist clinicians in implementing timely interventions to improve patient outcomes. Further multicenter validation is required to confirm these findings and refine risk prediction across diverse patient populations.