Machine learning mortality prediction model for cyclosporine therapy in pediatric aplastic anemia

The outcomes of children with aplastic anemia receiving cyclosporine monotherapy vary significantly in terms of mortality risk; therefore, a prognostic model for predicting mortality risk was constructed to optimize risk-stratified treatment strategies. This retrospective cohort study included children with acquired AA receiving cyclosporine-based immunosuppression, stratified by disease severity (vSAA/SAA/NSAA) and randomly split into training (70%) and validation (30%) cohorts. Ten machine learning models were developed; hyperparameters were optimized via grid search with 10-fold cross-validation exclusively within the training cohort to prevent data leakage. Model performance was evaluated using area under the ROC curve (AUC), accuracy, recall, specificity, precision, F1 score, and Brier score. Decision curve analysis (DCA) quantified clinical net benefit. The calibration curve was used to evaluate the reliability of the predicted probabilities. The SHapley Additive exPlanations (SHAP) framework was used to interpret feature contributions and ensure model transparency. Least absolute shrinkage and selection operator (LASSO) regression on the training cohort identified 5 predictors: reticulocyte count (RC), platelet count (PLT), disease subtype (vSAA/SAA/NSAA), total bilirubin (TB), and bone marrow myeloid proportion. The CatBoost model achieved the highest performance: AUC 0.834 (95% CI: 0.774-0.895) in training and 0.826 (95% CI: 0.743-0.910) in validation, with acceptable calibration (Brier score: 0.206 in training cohort, 0.207 in validation cohort). SHAP analysis confirmed RC as the top contributor, with lower RC values associated with higher predicted mortality risk. The CatBoost model demonstrates robust performance and transparency for predicting mortality risk in children with AA after cyclosporine treatment. Adherence to TRIPOD + AI guidelines ensures methodological rigor, supporting its potential as a clinical decision tool to stratify patients into distinct mortality risk groups and optimize individualized treatment strategies.