Machine learning model predicting new-onset acute kidney injury in fulminant myocarditis

Aim: To develop and validate a SHapley additive exPlanations (SHAP)-interpretable machine learning (ML) model for predicting new-onset Acute kidney injury (AKI) after coronary care unit (CCU) admission in patients with Fulminant myocarditis (FM). Methods: This retrospective cohort study included 157 consecutive patients with FM admitted to the CCU of Fuwai Central China Cardiovascular Hospital between January 2018 and January 2026. Admission clinical data were used for model development and internal validation. ML models were constructed, with feature selection and model optimization performed within the mlr3 framework. Results: New-onset AKI occurred in 36.9% of patients after CCU admission. Among the evaluated models, logistic regression showed the best overall performance, with an area under the receiver operating characteristic curve of 0.87 and good calibration. Internal validation using 1,000 bootstrap resampling iterations confirmed the robustness of model discrimination, calibration, and clinical utility. Sensitivity analyses further supported the stability of the findings. SHAP analysis identified mechanical ventilation as having the greatest relative impact on model predictions, followed by anion gap, chloride, total bilirubin, pulmonary infection, and urea. Based on these six predictors, a nomogram was developed for individualized estimation of AKI risk. Conclusion: A six-variable bedside prediction model demonstrated good discrimination and calibration for predicting new-onset AKI after CCU admission in patients with FM. The model may assist early risk stratification and support renal-protective management. Given the retrospective observational design, the selected predictors should be interpreted as predictive markers rather than causal determinants of AKI.