ArticleFrontiers in neurology2025
Association between multiple serum biomarkers and cerebral vasospasm in patients with aneurysmal subarachnoid hemorrhage.
Article in Frontiers in neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning models for predicting vasospasm following ruptured intracranial aneurysms: a systematic review and meta-analysis.Acta neurochirurgica · 2025Pooled it
- Data-Driven Analysis of Systemic Indicators Linking Stroke-Associated Pneumonia, Delayed Cerebral Ischemia, and Outcome After Aneurysmal Subarachnoid Hemorrhage.Journal of clinical medicine · 2026Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Cerebral 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. Methods: We 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. Results: 175 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, Conclusion: Our 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.
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