ArticleFrontiers in neurology2026
Explainable machine learning for 2-year functional outcomes in older adults with aneurysmal subarachnoid hemorrhage in North China: external validation and health-system context.
Article in Frontiers in neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Older adults with aneurysmal subarachnoid hemorrhage (aSAH) have heterogeneous long-term outcomes, and differences in treatment pathways and hospital capacity may complicate early risk stratification. Methods: This multicenter cohort study included adults aged ≥60 years with aSAH in North China. Of 1,132 admissions at four hospitals from January 2017 to September 2023, 914 patients with 2-year follow-up were divided 7:3 into training ( Results: 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 Conclusion: 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.
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