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Acta Neurochirurgica

, Volume 160, Issue 12, pp 2425–2434 | Cite as

External validation of cerebral aneurysm rupture probability model with data from two patient cohorts

  • Felicitas J. DetmerEmail author
  • Daniel Fajardo-Jiménez
  • Fernando Mut
  • Norman Juchler
  • Sven Hirsch
  • Vitor Mendes Pereira
  • Philippe Bijlenga
  • Juan R. Cebral
Original Article - Vascular Neurosurgery - Aneurysm
Part of the following topical collections:
  1. Vascular Neurosurgery – Aneurysm

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.

Keywords

Cerebral aneurysm Risk factors Hemodynamics Shape Rupture Prediction 

Notes

Acknowledgements

The authors would like to thank Rafik Ouared and Olivier Brina for helping with the image segmentation of the AneuX data.

Funding

This study was funded by the National Institutes of Health/National Institute of Neurological Disorders and Stroke (NIH-NINDS, grant no. R21NS094780). NJ, SH, VMP, and PB were supported by SystemsX.ch project AneuX evaluated by the Swiss National Science Foundation.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. For this type of study, formal consent is not required.

Supplementary material

701_2018_3712_MOESM1_ESM.pdf (457 kb)
ESM 1 (PDF 457 kb)

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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Bioengineering Department, Volgenau School of EngineeringGeorge Mason UniversityFairfaxUSA
  2. 2.Institute of Applied SimulationZHAW University of Applied SciencesWaedenswilSwitzerland
  3. 3.Institute of PhysiologyUniversity of ZurichZurichSwitzerland
  4. 4.Interventional Neuroradiology Unit, Service of Neuroradiology, Faculty of MedicineUniversity of GenevaGenevaSwitzerland
  5. 5.Neurosurgery, Clinical Neurosciences Department, Faculty of MedicineUniversity of GenevaGenevaSwitzerland

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