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Incorporating variability of patient inflow conditions into statistical models for aneurysm rupture assessment

  • Original Article - Vascular Neurosurgery - Aneurysm
  • Published:
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Abstract

Background

Hemodynamic patterns have been associated with cerebral aneurysm instability. For patient-specific computational fluid dynamics (CFD) simulations, the inflow rates of a patient are typically not known. The aim of this study was to analyze the influence of inter- and intra-patient variations of cerebral blood flow on the computed hemodynamics through CFD simulations and to incorporate these variations into statistical models for aneurysm rupture prediction.

Methods

Image data of 1820 aneurysms were used for patient-specific steady CFD simulations with nine different inflow rates per case, capturing inter- and intra-patient flow variations. Based on the computed flow fields, 17 hemodynamic parameters were calculated and compared for the different flow conditions. Next, statistical models for aneurysm rupture were trained in 1571 of the aneurysms including hemodynamic parameters capturing the flow variations either by defining hemodynamic “response variables” (model A) or repeatedly randomly selecting flow conditions by patients (model B) as well as morphological and patient-specific variables. Both models were evaluated in the remaining 249 cases.

Results

All hemodynamic parameters were significantly different for the varying flow conditions (p < 0.001). Both the flow-independent “response” model A and the flow-dependent model B performed well with areas under the receiver operating characteristic curve of 0.8182 and 0.8174 ± 0.0045, respectively.

Conclusions

The influence of inter- and intra-patient flow variations on computed hemodynamics can be taken into account in multivariate aneurysm rupture prediction models achieving a good predictive performance. Such models can be applied to CFD data independent of the specific inflow boundary conditions.

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Notes

  1. The flow range for the VA was defined as \( {\Delta }_Q={Q}_{\mathrm{mean}\_\mathrm{fitted}}\pm {Q}_{\mathrm{mean}\_\mathrm{fitted}}\frac{Q_{\mathrm{sd}}}{Q_{\mathrm{mean}}} \), with Qsd and Qmean as reported in [11] and Qmean _ fitted the flow fitted based on the relationship Q = 31.96A1.64

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Acknowledgments

Part of the data used for evaluation of the models (AneuX data) was collected starting in 2006 in the context of the EU project @neurIST and SystemsX.ch initiative AneuX evaluated by the Swiss National Science Foundation. The data is hosted by the Swiss Bioinformatics Institute in the context of the Aneurysm Data Bank. The authors would like to thank Sandrine Morel, Vitor Mendes Pereira, Daniel Rüfenacht, Karl Schaller, and Norman Juchler for helping with data collection, cleansing, harmonization, and processing. The authors would further like to acknowledge Michael J. Durka and Anne M. Robertson for providing flow data from ultrasound measurements in patient populations for this study.

Funding

This research was supported, in part, by a grant from the National Institutes of Neurological Disorders and Stroke (NIH-NINDS), Grant No. R01NS097457. SH and PB were supported by SystemsX.ch project AneuX evaluated by the Swiss National Science Foundation.

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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, Fernando Mut & Juan R. Cebral

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

    Martin Slawski

  3. Institute of Applied Simulation, ZHAW University of Applied Sciences, Wädenswil, Switzerland

    Sven Hirsch

  4. Neurosurgery, Clinical Neurosciences Department, Geneva University Hospital and Faculty of Medicine, Geneva University, Geneva, Switzerland

    Philippe Bijlenga

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  1. Felicitas J. Detmer
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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 Helsinki declaration 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., Mut, F., Slawski, M. et al. Incorporating variability of patient inflow conditions into statistical models for aneurysm rupture assessment. Acta Neurochir 162, 553–566 (2020). https://doi.org/10.1007/s00701-020-04234-8

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  • DOI: https://doi.org/10.1007/s00701-020-04234-8

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