Development and Validation of an Interpretable Machine Learning Model for Predicting In-Hospital Mortality in Diabetic Patients with Sepsis-Associated Acute Kidney Injury - Yanni Wang; Hongjie Shen; Shengze Wu; Suibi Yang; Feng Guo; Min Yang; Zhongheng Zhang (Journal of Intensive Care Medicine, 2026)
Background Sepsis-associated acute kidney injury (SA-AKI) is a common and severe complication in critically ill patients, with poor prognosis. Diabetes may further increase adverse outcomes through infection susceptibility, immune dysfunction, and renal vulnerability. However, mortality prediction models for patients with diabetes complicated by SA-AKI remain limited. This study aimed to develop and validate a machine learning-based model for early in-hospital mortality prediction in this population. Methods A total of 6929 patients with SA-AKI and diabetes were identified from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and randomly divided into training and validation sets at a ratio of 7:3. Ninety-four variables, including demographics, diagnoses, clinical parameters, and medication records within the first 24 h after ICU admission, were extracted. Twelve machine learning algorithms were developed and compared, and the optimal model was selected. Recursive feature elimination was used to identify key predictors, while SHapley Additive exPlanations were applied for model interpretation. The final model was deployed as a web-based tool and externally tested using the eICU Collaborative Research Database. Results Thirty-two key predictors were ultimately selected, including urine output rate, platelet count, lactate, weight, blood glucose, SOFA score, pH, blood urea nitrogen, vital signs, coagulation indices, vasopressor use, and other clinically relevant variables. The categorical boosting algorithm model presented better predictive performance [receiver operating characteristic (AUC): 0.828] than other models [accuracy (ACC): 70.9%, sensitivity: 78.7%, specificity: 69%, F1 score: 0.509, positive predictive value (PPV): 33.7%, and negative predictive value (NPV): 93.1%]. External testing using data from the eICU database was also well validated (AUC: 0.793). Conclusions A CatBoost-based machine learning model incorporating 32 clinically accessible variables showed good predictive performance for in-hospital mortality in patients with diabetes and SA-AKI, supporting early risk stratification and clinical decision-making.