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Development and internal validation of an aneurysm rupture probability model based on patient characteristics and aneurysm location, morphology, and hemodynamics

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Abstract

Purpose

Unruptured cerebral aneurysms pose a dilemma for physicians who need to weigh the risk of a devastating subarachnoid hemorrhage against the risk of surgery or endovascular treatment and their complications when deciding on a treatment strategy. A prediction model could potentially support such treatment decisions. The aim of this study was to develop and internally validate a model for aneurysm rupture based on hemodynamic and geometric parameters, aneurysm location, and patient gender and age.

Methods

Cross-sectional data from 1061 patients were used for image-based computational fluid dynamics and shape characterization of 1631 aneurysms for training an aneurysm rupture probability model using logistic group Lasso regression. The model’s discrimination and calibration were internally validated based on the area under the curve (AUC) of the receiver operating characteristic and calibration plots.

Results

The final model retained 11 hemodynamic and 12 morphological variables, aneurysm location, as well as patient age and gender. An adverse hemodynamic environment characterized by a higher maximum oscillatory shear index, higher kinetic energy and smaller low shear area as well as a more complex aneurysm shape, male gender and younger age were associated with an increased rupture risk. The corresponding AUC of the model was 0.86 (95% CI [0.85, 0.86], after correction for optimism 0.84).

Conclusion

The model combining variables from various domains was able to discriminate between ruptured and unruptured aneurysms with an AUC of 86%. Internal validation indicated potential for the application of this model in clinical practice after evaluation with longitudinal data.

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Funding

This study was funded by the National Institutes of Health/National Institute of Neurological Disorders and Stroke (NIH-NINDS, Grant #R21NS094780).

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Authors and Affiliations

  1. Bioengineering Department, Volgenau School of Engineering, George Mason University, 4400 University Drive, Fairfax, VA, 22030, USA

    Felicitas J. Detmer, Bong Jae Chung, Fernando Mut & Juan R. Cebral

  2. Statistics Department, George Mason University, Fairfax, VA, USA

    Martin Slawski

  3. Department of Neurological Surgery, University of Massachusetts, Worcester, MA, USA

    Farid Hamzei-Sichani

  4. Interventional Neuroradiology Unit, Inova Fairfax Hospital, Falls Church, VA, USA

    Christopher Putman

  5. Neurosurgery Department, University of Antioquia, Medellín, Colombia

    Carlos Jiménez

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  1. Felicitas J. Detmer
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  3. Fernando Mut
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Correspondence to Felicitas J. Detmer.

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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 Declaration of Helsinki and its later amendments or comparable ethical standards. For this type of study, formal consent is not required.

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Detmer, F.J., Chung, B.J., Mut, F. et al. Development and internal validation of an aneurysm rupture probability model based on patient characteristics and aneurysm location, morphology, and hemodynamics. Int J CARS 13, 1767–1779 (2018). https://doi.org/10.1007/s11548-018-1837-0

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  • DOI: https://doi.org/10.1007/s11548-018-1837-0

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