• Wessel E van der Steen, Henk A Marquering, Boers Anna M M AMM Department of Biomedical Engineering and Physics, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands; Department of Robotics and Mechat, Lucas A Ramos, René van den Berg, Vergouwen Mervyn D I MDI Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center, Utrecht University, Utrecht, The Netherlands., Majoie Charles B L M CBLM Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands., Bert A Coert, William P Vandertop, Dagmar Verbaan, and Roos Yvo B W E M YBWEM Department of Neurology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands..
• Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands; Department of Neurology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands; Neurosurgical Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands; Department of Biomedical Engineering and Physics, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands. Electronic address: w.e.vandersteen@amc.uva.nl.
• World Neurosurg. 2019 Oct 1; 130: e613-e619.

BackgroundThe amount of blood detected on brain computed tomography scan is frequently used in prediction models for delayed cerebral ischemia (DCI) in patients with aneurysmal subarachnoid hemorrhage (aSAH). These models, which include coarse grading scales to assess the amount of blood, have only moderate predictive value. Therefore, we aimed to develop a predictive model for DCI including automatically quantified total blood volume (TBV).MethodsWe included patients from a prospective aSAH registry. TBV was assessed with an automatic hemorrhage quantification algorithm. The outcome measure was clinical deterioration due to DCI. Clinical and radiologic variables were included in a logistic regression model. The final model was selected by bootstrapped backward selection and internally validated by assessing the optimism-corrected R2 value, c-statistic, and calibration plot. The c-statistic of the TBV model was compared with models that used the (modified) Fisher scale instead.ResultsWe included 369 patients. After backward selection, only TBV was included in the final model. The internally validated R2 value was 6%, and the c-statistic was 0.64. The c-statistic of the TBV model was higher than both the Fisher scale model (0.56; P < 0.001) and the modified Fisher scale model (0.58; P < 0.05).ConclusionsIn our registry, only TBV independently predicted DCI. TBV discriminated better than the (modified) Fisher scale, but still had only moderate value for predicting DCI. Our findings suggest that other factors need to be identified to achieve better accuracy for predicting DCI.Copyright © 2019 Elsevier Inc. All rights reserved.

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