Supplementary file 1_Explainable machine learning for 2-year functional outcomes in older adults with aneurysmal subarachnoid hemorrhage in North China: external validation and health-system context.docx

Discrimination, calibration, decision-curve analysis, and SHapley Additive exPlanations (SHAP) were evaluated.</p>Results<p>Among 1,413 patients, 583 (41.3%) had a poor 2-year outcome. Gradient boosting machine (GBM) AUCs were 0.863 (95% CI, 0.835–0.891), 0.834 (0.785–0.883), and 0.791 (0.749–0.833) in the training, internal-validation, and external-validation cohorts, respectively. External-validation accuracy was 0.752, sensitivity 0.661, specificity 0.808, precision 0.683, and F1 score 0.672.

GBM had the highest external AUC point estimate but did not lead every classification metric. SHAP ranked Hunt–Hess grade, age, World Federation of Neurosurgical Societies grade, and treatment as the most influential features. Unadjusted survival differed across treatment and hospital-capacity strata (both log-rank p < 0.0001).</p>Conclusion<p>An explainable GBM retained moderate discrimination during external validation and may support admission-stage risk stratification in older adults with aSAH.

The health-system findings are associative, and prospective recalibration, fairness assessment, and impact evaluation are required before clinical or public-health deployment.</p>