Machine learning versus conventional grading systems for prognostication in aneurysmal subarachnoid hemorrhage: a systematic review and meta-analysis.

BACKGROUND

Accurate prognostication after aneurysmal subarachnoid hemorrhage (aSAH) remains challenging. Conventional clinical and radiological grading systems, including the World Federation of Neurosurgical Societies (WFNS), Hunt-Hess, and Fisher scales, are widely used but have limited discriminative capacity. This study aimed to systematically compare machine learning (ML)-based prognostic models with conventional grading systems for predicting functional outcomes and mortality after aSAH, and to evaluate factors influencing ML performance.

METHODS

A systematic review and meta-analysis were conducted according to PRISMA 2020 guidelines. PubMed, Embase, Scopus, Web of Science, and the Cochrane Library were searched for studies published between 2010 and 2025. Eligible studies evaluated ML-based models for outcome prediction in adult aSAH patients and reported performance of conventional grading systems. Prognostic discrimination was pooled using random-effects meta-analysis of the area under the receiver operating characteristic curve (AUC), with predefined subgroup analyses.

RESULTS

Fourteen studies including 6,247 patients were analyzed. ML models demonstrated good to excellent discrimination, with AUCs ranging from 0.81 to 0.97. The pooled ML AUC for predicting unfavourable neurological outcome was 0.86 (95% CI 0.83-0.89; p < 0.0001), with substantial heterogeneity (I² = 96.1%). ML models outperformed conventional grading systems in most studies and showed comparable performance in the remainder. Subgroup analyses confirmed statistically significant prognostic accuracy across clinical-only, imaging-based, and multimodal ML models.

CONCLUSION

Machine learning-based prognostic models demonstrate statistically significant and clinically meaningful performance for outcome prediction after aSAH, exceeding conventional grading systems and supporting their role as complementary risk stratification tools.