Skip to main content
SpringerLink
Log in

Development of a statistical model for discrimination of rupture status in posterior communicating artery aneurysms

  • Original Article - Vascular Neurosurgery - Aneurysm
  • Published:
Acta Neurochirurgica Aims and scope Submit manuscript

Abstract

Background

Intracranial aneurysms at the posterior communicating artery (PCOM) are known to have high rupture rates compared to other locations. We developed and internally validated a statistical model discriminating between ruptured and unruptured PCOM aneurysms based on hemodynamic and geometric parameters, angio-architectures, and patient age with the objective of its future use for aneurysm risk assessment.

Methods

A total of 289 PCOM aneurysms in 272 patients modeled with image-based computational fluid dynamics (CFD) were used to construct statistical models using logistic group lasso regression. These models were evaluated with respect to discrimination power and goodness of fit using tenfold nested cross-validation and a split-sample approach to mimic external validation.

Results

The final model retained maximum and minimum wall shear stress (WSS), mean parent artery WSS, maximum and minimum oscillatory shear index, shear concentration index, and aneurysm peak flow velocity, along with aneurysm height and width, bulge location, non-sphericity index, mean Gaussian curvature, angio-architecture type, and patient age. The corresponding area under the curve (AUC) was 0.8359. When omitting data from each of the three largest contributing hospitals in turn, and applying the corresponding model on the left-out data, the AUCs were 0.7507, 0.7081, and 0.5842, respectively.

Conclusions

Statistical models based on a combination of patient age, angio-architecture, hemodynamics, and geometric characteristics can discriminate between ruptured and unruptured PCOM aneurysms with an AUC of 84%. It is important to include data from different hospitals to create models of aneurysm rupture that are valid across hospital populations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Golshani K, Ferrell A, Zomorodi A, Smith TP, Britz GW (2010) A review of the management of posterior communicating artery aneurysms in the modern era. Surg Neurol Int 1:88

    Article  PubMed  PubMed Central  Google Scholar 

  2. Morita A, Kirino T, Hashi K, Aoki N, Fukuhara S, Hashimoto N, Nakayama T, Sakai M, Teramoto A, Tominari S, Yoshimoto T (2012) The natural course of unruptured cerebral aneurysms in a Japanese cohort. N Engl J Med 366:2474–2482

    Article  PubMed  Google Scholar 

  3. Cebral JR, Raschi M (2013) Suggested connections between risk factors of intracranial aneurysms: a review. Ann Biomed Eng 41:1366–1383

    Article  PubMed  Google Scholar 

  4. Weir B, Disney L, Karrison T (2002) Sizes of ruptured and unruptured aneurysms in relation to their sites and the ages of patients. J Neurosurg 96:64–70

    Article  PubMed  Google Scholar 

  5. Greving JP, Wermer MJ, Brown RD, Morita A, Juvela S, Yonekura M, Ishibashi T, Torner JC, Nakayama T, Rinkel GJ, Algra A (2014) Development of the PHASES score for prediction of risk of rupture of intracranial aneurysms: a pooled analysis of six prospective cohort studies. Lancet Neurol 13:59–66

    Article  PubMed  Google Scholar 

  6. Tominari S, Morita A, Ishibashi T, Yamazaki T, Takao H, Murayama Y, Sonobe M, Yonekura M, Saito N, Shiokawa Y, Date I, Tominaga T, Nozaki K, Houkin K, Miyamoto S, Kirino T, Hashi K, Nakayama T, for the Unruptured Cerebral Aneurysm Study Japan Investigators (2015) Prediction model for 3-year rupture risk of unruptured cerebral aneurysms in Japanese patients. Ann Neurol 77(6):1050–1059

  7. Xiang J, Natarajan SK, Tremmel M, Ma D, Mocco J, Hopkins LN, Siddiqui AH, Levy EI, Meng H (2011) Hemodynamic-morphologic discriminants for intracranial aneurysm rupture. Stroke 42:144–152

    Article  PubMed  Google Scholar 

  8. Chung BJ, Doddasomayajula R, Mut F, Detmer F, Pritz MB, Hamzei-Sichani F, Brinjikji W, Kallmes DF, Jimenez C, Putman CM, Cebral JR (2017) Angio-architectures and hemodynamics characteristics of posterior communicating artery aneurysms and their association with rupture status. AJNR Am J Neuroradiol in press:

  9. Cebral JR, Castro MA, Appanaboyina S, Putman CM, Millan D, Frangi AF (2005) Efficient pipeline for image-based patient-specific analysis of cerebral aneurysm hemodynamics: technique and sensitivity. IEEE Trans Med Imag 24:457–467

    Article  Google Scholar 

  10. Ford MD, Alperin N, Lee SH, Holdsworth DW, Steinman DA (2005) Characterization of volumetric flow rate waveforms in the normal internal carotid and vertebral arteries. Physiol Meas 26:477–488

    Article  PubMed  Google Scholar 

  11. Cebral JR, Castro MA, Putman CM, Alperin N (2008) Flow-area relationship in internal carotid and vertebral arteries. Physiol Meas 29:585–594

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Taylor CA, Hughes TJR, Zarins CK (1998) Finite element modeling of blood flow in arteries. Comp Meth App Mech Eng 158:155–196

    Article  Google Scholar 

  13. Mut F, Aubry R, Löhner R, Cebral JR (2010) Fast numerical solutions of patient-specific blood flows in 3D arterial systems. Int J Num Meth Biomed Eng 26:73–85

    Article  Google Scholar 

  14. Byrne G, Mut F, Cebral JR (2014) Quantifying the large-scale hemodynamics of intracranial aneurysms. AJNR Am J Neuroradiol 35:333–338

    Article  PubMed  CAS  Google Scholar 

  15. Ma B, Harbaugh RE, Raghavan ML (2004) Three-dimensional geometrical characterization of cerebral aneurysms. Ann Biomed Eng 32:264–273

    Article  PubMed  Google Scholar 

  16. Mut F, Löhner R, Chien A, Tateshima S, Viñuela F, Putman CM, Cebral JR (2011) Computational hemodynamics framework for the analysis of cerebral aneurysms. Int J Num Meth Biomed Eng 27:822–839

    Article  Google Scholar 

  17. Raghavan ML, Ma B, Harbaugh RE (2005) Quantified aneurysm shape and rupture risk. J Neurosurg 102:355–362

    Article  PubMed  Google Scholar 

  18. Meier L, van de Geer S, Buhlmann P (2008) The group lasso for logistic regression. J R Stat Soc Ser B Stat Methodol 70(1):53–71

    Article  Google Scholar 

  19. Bühlmann P, van de Geer S (2011) Statistics for high-dimensional data: methods, theory and applications. Springer, New York

    Book  Google Scholar 

  20. Simon N, Tibshirani R (2012) Standardization and the Group Lasso Penalty. Stat Sin. https://doi.org/10.5705/ss.2011.075

  21. Detmer FJ, Chung BJ, Mut F, Slawski M, Hamzei-Sichani F, Putman CM, Jimenez CM, Cebral JR Development and internal validation of an aneurysm rupture probability model based on patient characteristics and aneurysm location, morphology, and hemodynamics. Submitted Manuscript

  22. Steyerberg EW (2009) Clinical prediction models. Springer, New York

    Book  Google Scholar 

  23. Austin PC, Steyerberg EW (2014) Graphical assessment of internal and external calibration of logistic regression models by using loess smoothers. Stat Med 33(3):517–535

    Article  PubMed  Google Scholar 

  24. Varma S, Simon R (2006) Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics 7(91)

  25. Zhang Y, Jing L, Liu J, Li C, Fan J, Wang S, Li H, Yang X (2016) Clinical, morphological, and hemodynamic independent characteristic factors for rupture of posterior communicating artery aneurysms. J Neurointerv Surg 8:808–812

    Article  PubMed  Google Scholar 

  26. Backes D, Rinkel GJE, Greving JP, Velthuis BK, Murayama Y, Takao H, Ishibashi T, Igase M, terBrugge KG, Agid R, Jääskeläinen JE, Lindgren AE, Koivisto T, von Und Zu Fraunberg M, Matsubara S, Moroi J, Wong GKC, Abrigo JM, Igase K, Matsumoto K, Wermer MJH, van Walderveen MAA, Algra A, Vergouwen MDI (2017) ELAPSS score for prediction of risk of growth of unruptured intracranial aneurysms. Neurology 88(17):1600–1606

  27. Matsukawa H, Uemura A, Fujii M, Kamo M, Takahashi O, Sumiyoshi S (2013) Morphological and clinical risk factors for the rupture of anterior communicating artery aneurysms. J Neurosurg 118(5):978–983

    Article  PubMed  Google Scholar 

  28. Wang G-X, Yu J-Y, Wen L, Zhang L, Mou K-J, Zhang D (2016) Risk factors for the rupture of middle cerebral artery bifurcation aneurysms using CT angiography. PLoS One 11(12):e0166654

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Juvela S, Poussa K, Lehto H, Porras M (2013) Natural history of unruptured intracranial aneurysms: a long-term follow-up study. Stroke 44:2414–2421

    Article  PubMed  Google Scholar 

  30. Hamdan A, Barnes J, Mitchell P (2014) Subarachnoid hemorrhage and the female sex: analysis of risk factors, aneurysm characteristics, and outcomes: clinical article. J Neurosurg 121(6):1367–1373

    Article  PubMed  Google Scholar 

  31. Wermer MJH, van der Schaaf IC, Algra A, Rinkel GJE (2007) Risk of rupture of unruptured intracranial aneurysms in relation to patient and aneurysm characteristics: an updated meta-analysis. Stroke 38(4):1404–1410

    Article  PubMed  Google Scholar 

  32. Dhar S, Tremmel M, Mocco J, Kim M, Yamamoto J, Siddiqui AH, Hopkins LN, Meng H (2008) Morphology parameters for intracranial aneurysm rupture risk assessment. Neurosurgery 63:185–197

    Article  PubMed  PubMed Central  Google Scholar 

  33. Lv N, Wang C, Karmonik C, Fang Y, Xu J, Yu Y, Cao W, Liu J, Huang Q (2016) Morphological and hemodynamic discriminators for rupture status in posterior communicating artery aneurysms. PLoS One 11:e0149906

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Duan G, Lv N, Yin J, Xu J, Hong B, Xu Y, Liu J, Huang Q (2016) Morphological and hemodynamic analysis of posterior communicating artery aneurysms prone to rupture: a matched case-control study. J Neurointerventional Surg 8(1):47–51

    Article  Google Scholar 

  35. Huhtakangas J, Lehecka M, Lehto H, Jahromi BR, Niemelä M, Kivisaari R (2017) CTA analysis and assessment of morphological factors related to rupture in 413 posterior communicating artery aneurysms. Acta Neurochir. https://doi.org/10.1007/s00701-017-3263-4

  36. Xu J, Yu Y, Wu X, Wu Y, Jiang C, Wang S, Huang Q, Liu J (2013) Morphological and hemodynamic analysis of mirror posterior communicating artery aneurysms. PLoS One 8:se55413

    Article  CAS  Google Scholar 

  37. Collins GS, Reitsma JB, Altman DG, Moons KG (2015) Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMC Med 13(1):1

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Funding

This study was funded by the National Institutes of Health/National Institute of Neurological Disorders and Stroke (NIH-NINDS, grant no. R21NS094780).

Author information

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, Michael Pritz & Juan R. Cebral

  2. Department of Bioengineering, University of Utah, Salt Lake City, UT, USA

    Michael Pritz

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

    Martin Slawski

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

    Farid Hamzei-Sichani

  5. Department of Radiology, Mayo Clinic, Rochester, MN, USA

    David Kallmes

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

    Christopher Putman

  7. Neurosurgery Department, University of Antioquia, Medellin, Colombia

    Carlos Jimenez

Authors
  1. Felicitas J. Detmer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bong Jae Chung
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fernando Mut
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Michael Pritz
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Martin Slawski
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Farid Hamzei-Sichani
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. David Kallmes
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Christopher Putman
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Carlos Jimenez
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Juan R. Cebral
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Felicitas J. Detmer.

Ethics declarations

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.

Additional information

Comments

This manuscript investigates whether a geometry and flow-based risk assessment could improve our ability to identify aneurysms at risk of rupture from the currently available risk factor-based scoring and practice. The major limitation of this study is the case-control type study design comparing ruptured and unruptured aneurysms, which setting is prone to bias because the geometry and flow conditions of an aneurysm that has ruptured may have been very different before the rupture. In spite of this limitation, the results of this study are highly interesting and contribute significantly to our knowledge on risk factor for aneurysm rupture and demonstrate that a detailed analysis of geometry and flow conditions may help to identify those aneurysms in which aberrant flow conditions will trigger pathological wall remodelling that eventually leads to rupture.

Juhana Frosen

Finland

This article is part of the Topical Collection on Vascular Neurosurgery - Aneurysm

Electronic supplementary material

ESM 1

(PDF 514 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Detmer, F.J., Chung, B.J., Mut, F. et al. Development of a statistical model for discrimination of rupture status in posterior communicating artery aneurysms. Acta Neurochir 160, 1643–1652 (2018). https://doi.org/10.1007/s00701-018-3595-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00701-018-3595-8

Keywords

Search

Navigation