Machine learning-driven risk prediction of delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage using peripheral inflammatory markers.

ABSTRACT:

Background

Delayed cerebral ischemia (DCI) remains a leading cause of secondary neurological deterioration and mortality after aneurysmal subarachnoid hemorrhage (aSAH). Accumulating evidence highlights the pivotal role of systemic inflammation in the pathogenesis of DCI, with peripheral inflammatory markers showing potential as early indicators. However, the predictive performance of individual biomarkers is limited. By leveraging machine learning (ML) techniques, it is possible to integrate heterogeneous inflammatory signals and model complex nonlinear relationships to improve individualized risk prediction.

Methods and materials

We conducted a retrospective analysis of 562 aSAH patients admitted to a single tertiary center. Clinical, radiographic, and laboratory data-incl

SUBMITTER: Liu Y 

PROVIDER: S-EPMC12738297 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature