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Quantification of blood and CSF volume to predict outcome after aneurysmal subarachnoid hemorrhage

Booker, J; Zolnourian, A; Street, J; Arora, M; Pandit, AS; Toma, A; Wu, CH; ... Bulters, D; + view all (2024) Quantification of blood and CSF volume to predict outcome after aneurysmal subarachnoid hemorrhage. Neurosurgical Review , 47 (1) , Article 752. 10.1007/s10143-024-03001-y. Green open access

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Abstract

This study aimed to describe the relationship between blood and CSF volumes in different compartments on baseline CT after aSAH, assess if they independently predict long-term outcome, and explore their interaction with age. CT scans from patients participating in a prospective multicenter randomized controlled trial of patients with aSAH were segmented for blood and CSF volumes. The primary outcomes were the mRS, and the Subarachnoid Hemorrhage Outcome Tool (SAHOT) at day 28 and 180. Univariate regressions were conducted to identify significant predictors of poor outcomes, followed by principal component analysis to explore correlations between imaging variables and WFNS. A multivariate predictive model was then developed and optimized using stepwise regression. CT scans from 97 patients with a median delay from symptom onset of 271 min (131–547) were analyzed. Univariate analysis showed only WFNS, and total blood volume (TBV) were significant predictors of both short and long-term outcome with WFNS more predictive of mRS and TBV more predictive of SAHOT. Principal component analysis showed strong dependencies between the imaging predictors. Multivariate ordinal regression showed models with WFNS alone were most predictive of day 180 mRS and models with TBV alone were most predictive of SAHOT. TBV was the most significant measured imaging predictor of poor long-term outcome after aSAH. All these imaging predictors are correlated, however, and may have multiple complex interactions necessitating larger datasets to detect if they provide any additional predictive value for long-term outcome.

Type: Article
Title: Quantification of blood and CSF volume to predict outcome after aneurysmal subarachnoid hemorrhage
Location: Germany
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/s10143-024-03001-y
Publisher version: https://doi.org/10.1007/s10143-024-03001-y
Language: English
Additional information: © 2025 Springer Nature. This article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Keywords: Subarachnoid hemorrhage, Aneurysm, Machine learning, Image segmentation
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Brain Repair and Rehabilitation
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Clinical and Experimental Epilepsy
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery-pp.ucl.ac.uk/id/eprint/10204304
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