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
. 2020 Jun;41(6):1015-1021.
doi: 10.3174/ajnr.A6575. Epub 2020 May 14.

Prediction of Outcome Using Quantified Blood Volume in Aneurysmal SAH

Affiliations

Prediction of Outcome Using Quantified Blood Volume in Aneurysmal SAH

W E van der Steen et al. AJNR Am J Neuroradiol. 2020 Jun.

Abstract

Background and purpose: In patients with SAH, the amount of blood is strongly associated with clinical outcome. However, it is commonly estimated with a coarse grading scale, potentially limiting its predictive value. Therefore, we aimed to develop and externally validate prediction models for clinical outcome, including quantified blood volumes, as candidate predictors.

Materials and methods: Clinical and radiologic candidate predictors were included in a logistic regression model. Unfavorable outcome was defined as a modified Rankin Scale score of 4-6. An automatic hemorrhage-quantification algorithm calculated the total blood volume. Blood was manually classified as cisternal, intraventricular, or intraparenchymal. The model was selected with bootstrapped backward selection and validated with the R 2, C-statistic, and calibration plots. If total blood volume remained in the final model, its performance was compared with models including location-specific blood volumes or the modified Fisher scale.

Results: The total blood volume, neurologic condition, age, aneurysm size, and history of cardiovascular disease remained in the final models after selection. The externally validated predictive accuracy and discriminative power were high (R 2 = 56% ± 1.8%; mean C-statistic = 0.89 ± 0.01). The location-specific volume models showed a similar performance (R 2 = 56% ± 1%, P = .8; mean C-statistic = 0.89 ± 0.00, P = .4). The modified Fisher models were significantly less accurate (R 2 = 45% ± 3%, P < .001; mean C-statistic = 0.85 ± 0.01, P = .03).

Conclusions: The total blood volume-based prediction model for clinical outcome in patients with SAH showed a high predictive accuracy, higher than a prediction model including the commonly used modified Fisher scale.

PubMed Disclaimer

Figures

Fig 1.
Fig 1.
Calibration plots of the TBV model for poor outcome (A), the TBV model for death (B), the location-specific model for poor outcome (C), the location-specific model for death (D), the modified Fisher model for poor outcome (E), and the modified Fisher model for death (F).

References

    1. van Gijn J, Kerr RS, Rinkel GJ. Subarachnoid haemorrhage. Lancet 2007;369:306–18 10.1016/S0140-6736(07)60153-6 - DOI - PubMed
    1. Vergouwen MD, Jong-Tjien-Fa AV, Algra A, et al. . Time trends in causes of death after aneurysmal subarachnoid hemorrhage: a hospital-based study. Neurology 2016;86:59–63 10.1212/WNL.0000000000002239 - DOI - PubMed
    1. Jaja BN, Cusimano MD, Etminan N, et al. . Clinical prediction models for aneurysmal subarachnoid hemorrhage: a systematic review. Neurocrit Care 2013;18:143–53 10.1007/s12028-012-9792-z - DOI - PubMed
    1. Jaja BN, Saposnik G, Lingsma HF, et al. ; SAHIT oration. Development and validation of outcome prediction models for aneurysmal subarachnoid haemorrhage: the SAHIT multinational cohort study. BMJ 2018;360:j5745 10.1136/bmj.j5745 - DOI - PubMed
    1. Kramer AH, Hehir M, Nathan B, et al. . A comparison of 3 radiographic scales for the prediction of delayed ischemia and prognosis following subarachnoid hemorrhage. J Neurosurg 2008;109:199–207 10.3171/JNS/2008/109/8/0199 - DOI - PubMed

Publication types

LinkOut - more resources