ArticleScientific reports2024
The eSAH score: a simple practical predictive model for SAH mortality and outcomes.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Systemic inflammation response index as an independent predictor of unfavorable prognosis and its application in risk stratification in patients with aneurysmal subarachnoid hemorrhage.Frontiers in neuroscience · 2026Article
- External multicenter validation of the eSAH score for predicting outcomes after subarachnoid hemorrhage.Scientific reports · 2025Article
- A Ruptured Tri-Lobulated ICA-PCom Aneurysm Presenting with Preserved Neurological Function: Case Report and Clinical-Anatomical Analysis.Diagnostics (Basel, Switzerland) · 2025Article
- SAHVAI-3D and 4D: Automated AI Volumetric Measurement of Subarachnoid Hemorrhage on Noncontrast Head CT.Stroke (Hoboken, N.J.) · 2025Article
- Aneurysmal subarachnoid hemorrhage-risk score-impact of pre-existing cardiovascular risk factors on functional patient outcomes.Frontiers in neurologyArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We developed a simple quantifiable scoring system that predicts aneurysmal subarachnoid hemorrhage (aSAH) mortality, delayed cerebral ischemia (DCI), and modified Rankin scale (mRS) outcomes using readily available SAH admission data with SAH volume (SAHV) measured on computed tomography (CT). We retrospectively analyzed a cohort of 277 patients with aSAH admitted at our Comprehensive Stroke Center at Mayo Clinic in Jacksonville, Florida, between January 5, 2012, and February 24, 2022. We developed a mathematical radiographic model SAHV that measures basal cisternal SAH blood volume using a derivation of the ABC/2 ellipsoid formula (A = width/thickness, B = length, C = vertical extension) on noncontrast CT, which we previously demonstrated is comparable to pixel-based manual segmentation on noncontrast CT. Data were analyzed using t test, χ
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