Title: External validation of cerebral aneurysm rupture probability model with data from two patient cohorts
Authors : Detmer, Felicitas J.
Fajardo-Jiménez, Daniel
Mut, Fernando
Juchler, Norman
Hirsch, Sven
Pereira, Vitor Mendes
Bijlenga, Philippe
Cebral, Juan R.
Published in : Acta Neurochirurgica
Issue Date: 30-Oct-2018
License (according to publishing contract) : Licence according to publishing contract
Type of review: Peer review (Publication)
Language : English
Subjects : Cerebral aneurysm; Hemodynamics; Prediction; Risk factors; Rupture; Shape
Subject (DDC) : 616.8: Neurology, diseases of nervous system
Abstract: Background: For a treatment decision of unruptured cerebral aneurysms, physicians and patients need to weigh the risk of treatment against the risk of hemorrhagic stroke caused by aneurysm rupture. The aim of this study was to externally evaluate a recently developed statistical aneurysm rupture probability model, which could potentially support such treatment decisions. Methods: Segmented image data and patient information obtained from two patient cohorts including 203 patients with 249 aneurysms were used for patient-specific computational fluid dynamics simulations and subsequent evaluation of the statistical model in terms of accuracy, discrimination, and goodness of fit. The model’s performance was further compared to a similarity-based approach for rupture assessment by identifying aneurysms in the training cohort that were similar in terms of hemodynamics and shape compared to a given aneurysm from the external cohorts. Results: When applied to the external data, the model achieved a good discrimination and goodness of fit (area under the receiver operating characteristic curve AUC = 0.82), which was only slightly reduced compared to the optimism-corrected AUC in the training population (AUC = 0.84). The accuracy metrics indicated a small decrease in accuracy compared to the training data (misclassification error of 0.24 vs. 0.21). The model’s prediction accuracy was improved when combined with the similarity approach (misclassification error of 0.14). Conclusions: The model’s performance measures indicated a good generalizability for data acquired at different clinical institutions. Combining the model-based and similarity-based approach could further improve the assessment and interpretation of new cases, demonstrating its potential use for clinical risk assessment.
Departement: Life Sciences and Facility Management
Organisational Unit: Institute of Applied Simulation (IAS)
Publication type: Article in scientific Journal
DOI : 10.1007/s00701-018-3712-8
ISSN: 0001-6268
0942-0940
URI: https://digitalcollection.zhaw.ch/handle/11475/12682
Appears in Collections:Publikationen Life Sciences und Facility Management

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