Machine learning-based prediction of short-term outcomes in aneurysmal subarachnoid hemorrhage: a multicenter study integrating clinical and inflammatory indicators.

ABSTRACT:

Background

Aneurysmal subarachnoid hemorrhage (aSAH) is a life-threatening cerebrovascular emergency. We built and validated a machine learning model integrating clinical and inflammatory indicators for early risk prediction.

Methods

This multicenter retrospective cohort study included 1,120 aSAH patients admitted between January 2022 and December 2024 across four tertiary hospitals for model development and 326 independent patients from the Second Xiangya Hospital for quasi-external validation. Twenty-eight candidate predictors were evaluated, encompassing clinical grading scales and inflammation- and nutrition-related biomarkers. Continuous variables were discretized into quartile-based categories to enhance interpretability and mitigate outlier effects. Synthetic minority

SUBMITTER: Zhou W 

PROVIDER: S-EPMC12772089 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature