What question did this study set out to answer?

This review aims to evaluate the diagnostic accuracy of cerebral vasospasm monitoring tools and the prediction of delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage.

April 22, 2026Open Access

Cerebral Vasospasm Detection and Delayed Cerebral Ischemia Prediction after Aneurysmal Subarachnoid Hemorrhage: A Scoping Review

Key Points

This review aims to evaluate the diagnostic accuracy of cerebral vasospasm monitoring tools and the prediction of delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage.
Conducted a scoping review following PRISMA-ScR guidelines across multiple databases.
Evaluated studies based on diagnostic accuracy and prediction model risk of bias.
Categorized studies by tools used for vasospasm detection and DCI prediction.
Transcranial Doppler showed sensitivity of 66% and specificity of 97% for vasospasm; CTA had sensitivity of 76% and specificity of 93%.
CT perfusion achieved sensitivity of 86% and specificity of 80% for DCI, while cEEG showed sensitivity of 88% and specificity of 89%.
Machine-learning models for DCI prediction outperformed traditional methods, but most lacked independent validation and were at high risk of bias.

Abstract

Abstract Background/Objective Aneurysmal subarachnoid hemorrhage (aSAH) is complicated by angiographic cerebral vasospasm and delayed cerebral ischemia (DCI), distinct entities with different reference standards and surveillance targets. We mapped the evidence on (1) diagnostic accuracy of vasospasm monitoring tools using digital subtraction angiography (DSA) as the reference standard and (2) detection and prediction of DCI using the 2010 consensus definition. Methods We performed a Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR)-guided scoping review of PubMed/MEDLINE, Google Scholar, and PubMed Central (1 January 2005–31 January 2025). Vasospasm studies required DSA as comparator; DCI studies were categorized by outcome definition (2010 consensus vs. alternative definitions). Methodological quality was assessed with QUADAS-2 (diagnostic accuracy) and Prediction Model Risk of Bias Assessment Tool (PROBAST; prediction models). Results From 762 records, 136 studies met inclusion criteria. For vasospasm detection (DSA reference), transcranial Doppler (18 studies; n = 3256) demonstrated sensitivity 66% (60–72) and specificity 97% (95–99), while computed tomography angiography (CTA) (15 studies; n = 2145) showed sensitivity 76% (72–80) and specificity 93% (91–95). For DCI detection (2010 consensus), CT perfusion (30 studies; n = 1786) yielded sensitivity 86% (78–86) and specificity 80% (75–85) with substantial protocol heterogeneity; continuous electroencephalography (cEEG) (8 studies; n = 523) achieved sensitivity 88% (80–94) and specificity 89% (83–93). Across 84 DCI predictor studies, machine-learning models (23 models) outperformed traditional risk factors (area under the curve AUC 0.68–0.88 vs. ~0.63), but only 44% underwent independent validation, and 67% were at high risk of bias. Conclusions The evidence supports a two-track framework: DSA-referenced tools for vasospasm detection and consensus-defined markers for DCI detection/prediction. Multimodal strategies integrating physiologic and imaging data are most promising, whereas machine-learning approaches require prospective, externally validated evaluations before routine clinical deployment.

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