Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Randomized Controlled Trial
. 2024 Oct 8;47(1):752.
doi: 10.1007/s10143-024-03001-y.

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

James Booker  1   2 , Ardalan Zolnourian  3 , James Street  4   5   6 , Mukul Arora  3 , Anand S Pandit  4 , Ahmed Toma  4 , Chieh-Hsi Wu  7 , Ian Galea  8 , Diederik Bulters  3   8
Affiliations
Randomized Controlled Trial

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

James Booker et al. Neurosurg Rev. .

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.

Keywords: Aneurysm; Image segmentation; Machine learning; Subarachnoid hemorrhage.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflicts of interest related to the content of this article.

Figures

Fig. 1
Fig. 1
Receiver operator characteristic (ROC) curve of conventional predictors of outcome after subarachnoid hemorrrhage. mRS, modified Rankin Score; WFNS, World Federation of Neurological Societies scale; SSV, Selective Sulcal Volume
Fig. 2
Fig. 2
Proportional and cumulative proportion of variance from the principal component analysis
Fig. 3
Fig. 3
Interaction between age at 40, 60, and 80 years, and (a) Total Blood Volume, (b) SSV CSF and (c) Ventricular CSF Volume for prediction of mRS at day 180. SSV, Selective Sulcal Volume
Fig. 4
Fig. 4
Three-way interaction between age, total blood volume at 6.03 ml, 24.12 ml and 42.2 ml, and (a) SSV CSF. (b) Ventricular CSF. Blood volumes were selected by the model as they represent lower, middle and upper thirds in the cohort. Shaded areas represent the confidence intervals of the regressions. SSV, Selective Sulcal Volume; TBV, Total Blood Volume
See this image and copyright information in PMC

References

    1. Schatlo B, Fung C, Stienen MN, Fathi AR, Fandino J, Smoll NR et al (2020) Incidence and Outcome of Aneurysmal Subarachnoid Hemorrhage: The Swiss Study on Subarachnoid Hemorrhage (Swiss SOS). Stroke [Internet]. [cited 2022 Feb 25];344–7. https://www.ahajournals.org/doi/abs/10.1161/STROKEAHA.120.029538 - PubMed
    1. Kramer AH, Hehir M, Nathan B, Gress D, Dumont AS, Kassell NF et al (2008) A comparison of 3 radiographic scales for the prediction of delayed ischemia and prognosis following subarachnoid hemorrhage. J Neurosurg 109:199–207 - PubMed
    1. Boers AM, Zijlstra IA, Gathier CS, Van Den Berg R, Slump CH, Marquering HA et al (2014) Automatic quantification of subarachnoid hemorrhage on noncontrast CT. Am J Neuroradiol 35:2279–2286 - PMC - PubMed
    1. van der Barros RS, Boers AMM, van den Zijlstra IJ, El Youssoufi W et al (2020) Automated segmentation of subarachnoid hemorrhages with convolutional neural networks. Inform Med Unlocked [Internet]. ;19:100321. 10.1016/j.imu.2020.100321
    1. Jiménez-Roldán L, Alén JF, Gómez PA, Lobato RD, Ramos A, Munarriz PM et al (2013) Volumetric analysis of subarachnoid hemorrhage: Assessment of the reliability of two computerized methods and their comparison with other radiographic scales - clinical article. J Neurosurg 118:84–93 - PubMed

Publication types

LinkOut - more resources