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Analysis of Cerebral Vasospasm in a Murine Model of Subarachnoid Hemorrhage with High Frequency Transcranial Duplex Ultrasound
Published on: June 3, 2021
Key Predictors of Cerebral Vasospasm Following Subarachnoid Hemorrhage: A Systematic Review and Meta-Analysis
Jung Min Lee1, Wonkyung Jung2, Sun-Mi Lee3
1Department of Nursing, Samsung Medical Center, College of Nursing, The Catholic University of Korea, Seoul, Republic of Korea.
Background:
Subarachnoid hemorrhage (SAH) is a life-threatening condition often leading to complications such as cerebral vasospasm, which causes delayed cerebral ischemia and increases mortality. In intensive care, early identification of vasospasm risk factors by nurses is essential for timely intervention and improved outcomes.
Objectives:
We sought to review and synthesize evidence on risk factors associated with cerebral vasospasm after SAH and inform early risk assessment and nursing care strategies in intensive care.
Methods:
We conducted a systematic review and meta-analysis of observational studies. A comprehensive search was conducted on August 30 to 31, 2024, across 8 databases. Quality was assessed using the Joanna Briggs Institute checklist and the Risk of Bias (RoB 2.0) tool.
Results:
Thirty-eight studies were included in the systematic review, of which 27 were included in the meta-analysis, involving 19 033 patients. Clinical severity measures were most strongly associated with cerebral vasospasm, including Modified Fisher grade (odds ratio (OR) = 2.88; 95% confidence interval (CI), 2.04-4.07), Fisher grade (OR = 2.27; 95% CI, 1.91-2.69), and hemodynamic status (OR = 2.56; 95% CI, 1.17-5.59). Aneurysm-related factors and comorbid conditions were associated with increased risk of cerebral vasospasm, whereas clinical biomarkers and inflammatory markers showed relatively smaller effect sizes.
Conclusions:
Clinical severity scales and hemodynamic status are the strongest predictors of cerebral vasospasm following SAH. These findings support early risk stratification and the implementation of structured monitoring strategies to improve patient outcomes.PROSPERO trial registration number:CRD42024605605 (https://www.crd.york.ac.uk/PROSPERO/view/CRD42024605605).
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