AUTHOR=Yang Chenghao , Luo Fei , Chen Xi , Deng Yunliang , Lan Meirui 

TITLE=Association between multiple serum biomarkers and cerebral vasospasm in patients with aneurysmal subarachnoid hemorrhage

JOURNAL=Frontiers in Neurology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1592390

DOI=10.3389/fneur.2025.1592390

ISSN=1664-2295

ABSTRACT=BackgroundCerebral vasospasm (CVS) is a potentially life-threatening complication for aneurysmal subarachnoid hemorrhage (aSAH) patients, raising their risk of disability and mortality substantially. Our study aimed to investigate whether serum biomarkers collected 24 h post-op can predict which aSAH patients would develop CVS within 14 days.MethodsWe conducted a retrospective investigation of aSAH patients at our hospital during January 2020 to December 2023. For all patients, we collected comprehensive baseline information and multiple serum biomarkers. To analyze the correlation of these factors and CVS development, we applied univariate and multivariate logistic regression analysis and created a predictive model.Results175 aSAH patients participated in our study – 84 of whom went on to develop CVS and 91 who did not. Four independent risk factors for CVS were determined through logistic regression analysis: Glasgow Coma Scale (GCS) score (OR = 4.633, 95% CI, 2.267–9.467, p < 0.001), hypoxia-inducible factor-1α (HIF-1α) (OR = 1.064, 95% CI, 1.025–1.104, p = 0.001), vascular endothelial growth factor (VEGF) (OR = 1.054, 95% CI, 1.033–1.076, p < 0.001), and endothelin-1 (ET-1) (OR = 1.151, 95% CI, 1.010–1.312, p = 0.035). When we incorporated these variables into the random forest and logistic regression predictive model, it performed exceptionally well, achieving an area under the receiver operating characteristic curve (AUC) of 0.966 and 0.959, respectively.ConclusionOur findings suggest that GCS score and the biomarkers HIF-1α, VEGF, and ET-1 are independent predictors of CVS development in aSAH patients. Our predictive model based on the factors is remarkably accurate, offering clinicians a potentially valuable tool for early identification of high CVS risk patients.