Development and validation of a prognostic prediction model for elderly patients with sepsis-associated acute kidney injury based on interpretable machine learning

Elderly patients with sepsis-associated acute kidney injury (SA-AKI) have high short-term mortality; early identification of high-risk individuals is critical for personalized intervention. Traditional clinical scoring systems relying on linear assumptions and conventional machine learning models often lack interpretability, hindering translation to clinical practice. This study aimed to develop and externally validate an interpretable prognostic model for 28-day in-hospital mortality in elderly SA-AKI using routinely available clinical variables.

Clinical data of 11,468 elderly SA-AKI patients were retrospectively extracted from the MIMIC-IV database and divided into training and internal test sets at a 7:3 ratio using stratified random sampling. A dual feature selection strategy combining LASSO regression and random forest was applied. Five machine learning models-decision tree, logistic regression, random forest, LightGBM, and XGBoost-were constructed in parallel. Model discrimination and calibration were evaluated by AUC, Brier score, accuracy, sensitivity, specificity, and F1 score. External independent validation was performed using a clinical cohort of 257 patients from the General Hospital of Ningxia Medical University. The SHAP method was used for global and individual-level interpretability of the optimal model.

Dual feature selection identified 10 independent predictors: age, weight, red blood cell distribution width, arterial partial pressure of oxygen, blood lactate, prothrombin time, activated partial thromboplastin time, blood urea nitrogen, serum creatinine, and respiratory rate. Among the five models, XGBoost demonstrated the best performance, with training AUC 0.9898 (accuracy 0.9472) and external validation AUC 0.9156 (accuracy 0.8560), indicating strong generalizability. SHAP global importance ranking showed age as the primary driver, followed by serum creatinine and arterial partial pressure of oxygen. Each core feature exhibited a nonlinear association with 28-day mortality risk, with clear model-derived risk inflection points rather than clinical intervention thresholds.

The XGBoost-based interpretable model exhibits excellent discriminative performance and robust external generalizability for predicting 28-day mortality in elderly SA-AKI. The SHAP attribution framework renders model decisions transparent, providing quantitative evidence-based support for early risk stratification and hypothesis generation for individualized organ-protective strategies in the ICU.