Skip to main content
Top
Published in: International Journal of Computer Assisted Radiology and Surgery 11/2018

01-11-2018 | Original Article

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

Authors: Felicitas J. Detmer, Bong Jae Chung, Fernando Mut, Martin Slawski, Farid Hamzei-Sichani, Christopher Putman, Carlos Jiménez, Juan R. Cebral

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 11/2018

Login to get access

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.
Appendix
Available only for authorised users
Literature
1.
go back to reference Rinkel GJ, Djibuti M, van Gijn J (1998) Prevalence and risk of rupture of intracranial aneurysms: a systematic review. Stroke 29:251–259CrossRefPubMed Rinkel GJ, Djibuti M, van Gijn J (1998) Prevalence and risk of rupture of intracranial aneurysms: a systematic review. Stroke 29:251–259CrossRefPubMed
6.
go back to reference Wiebers DO, Whisnant JP, Huston J, Meissner I, Brown RD, Piepgras DG, Forbes GS, Thielen K, Nichols D, O’Fallon WM, Peacock J, Jaeger L, Kassell NF, Kongable-Beckman GL, Torner JC (2003) Unruptured intracranial aneurysms: natural history, clinical outcome, and risks of surgical and endovascular treatment. Lancet 362:103–110CrossRefPubMed Wiebers DO, Whisnant JP, Huston J, Meissner I, Brown RD, Piepgras DG, Forbes GS, Thielen K, Nichols D, O’Fallon WM, Peacock J, Jaeger L, Kassell NF, Kongable-Beckman GL, Torner JC (2003) Unruptured intracranial aneurysms: natural history, clinical outcome, and risks of surgical and endovascular treatment. Lancet 362:103–110CrossRefPubMed
8.
go back to reference Japan Investigators UCAS, 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. https://doi.org/10.1056/nejmoa1113260 CrossRef Japan Investigators UCAS, 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. https://​doi.​org/​10.​1056/​nejmoa1113260 CrossRef
11.
go back to reference Ujiie H, Tamano Y, Sasaki K, Hori T (2001) Is the aspect ratio a reliable index for predicting the rupture of a saccular aneurysm? Neurosurgery 48:495–502 (discussion 502-3) CrossRefPubMed Ujiie H, Tamano Y, Sasaki K, Hori T (2001) Is the aspect ratio a reliable index for predicting the rupture of a saccular aneurysm? Neurosurgery 48:495–502 (discussion 502-3) CrossRefPubMed
14.
17.
go back to reference 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 77:1050–1059. https://doi.org/10.1002/ana.24400 CrossRefPubMed 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 77:1050–1059. https://​doi.​org/​10.​1002/​ana.​24400 CrossRefPubMed
18.
go back to reference Bisbal J, Engelbrecht G, Villa-Uriol M-C, Frangi AF (2011) Prediction of cerebral aneurysm rupture using hemodynamic, morphologic and clinical features: a data mining approach. In: Hameurlain A, Liddle SW, Schewe K-D, Zhou X (eds) Database and expert systems applications. Springer, Berlin, pp 59–73CrossRef Bisbal J, Engelbrecht G, Villa-Uriol M-C, Frangi AF (2011) Prediction of cerebral aneurysm rupture using hemodynamic, morphologic and clinical features: a data mining approach. In: Hameurlain A, Liddle SW, Schewe K-D, Zhou X (eds) Database and expert systems applications. Springer, Berlin, pp 59–73CrossRef
20.
go back to reference 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 Imaging 24:457–467CrossRefPubMed 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 Imaging 24:457–467CrossRefPubMed
21.
go back to reference 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 26:477–488CrossRefPubMed 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 26:477–488CrossRefPubMed
22.
23.
go back to reference Taylor CA, Hughes TJR, Zarins CK (1998) Finite element modeling of blood flow in arteries. Comput Methods App Mech Eng 158:155–196CrossRef Taylor CA, Hughes TJR, Zarins CK (1998) Finite element modeling of blood flow in arteries. Comput Methods App Mech Eng 158:155–196CrossRef
24.
go back to reference 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 Numer Method Biomed Eng 27:822–839CrossRefPubMedPubMedCentral 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 Numer Method Biomed Eng 27:822–839CrossRefPubMedPubMedCentral
26.
go back to reference Ma B, Harbaugh RE, Raghavan ML (2004) Three-dimensional geometrical characterization of cerebral aneurysms. Ann Biomed Eng 32:264–273CrossRefPubMed Ma B, Harbaugh RE, Raghavan ML (2004) Three-dimensional geometrical characterization of cerebral aneurysms. Ann Biomed Eng 32:264–273CrossRefPubMed
27.
go back to reference Raghavan ML, Ma B, Harbaugh RE (2005) Quantified aneurysm shape and rupture risk. J Neurosurg 102:355–362CrossRefPubMed Raghavan ML, Ma B, Harbaugh RE (2005) Quantified aneurysm shape and rupture risk. J Neurosurg 102:355–362CrossRefPubMed
32.
go back to reference Steyerberg EW (2009) Clinical prediction models: a practical approach to development, validation, and updating. Springer, New YorkCrossRef Steyerberg EW (2009) Clinical prediction models: a practical approach to development, validation, and updating. Springer, New YorkCrossRef
34.
go back to reference Kumar R, Indrayan A (2011) Receiver operating characteristic (ROC) curve for medical researchers. Indian Pediatr 48:277–287CrossRefPubMed Kumar R, Indrayan A (2011) Receiver operating characteristic (ROC) curve for medical researchers. Indian Pediatr 48:277–287CrossRefPubMed
36.
go back to reference Weston J, Elisseeff A, Schölkopf B, Tipping M (2003) Use of the zero-norm with linear models and kernel methods. J Mach Learn Res 3:1439–1461 Weston J, Elisseeff A, Schölkopf B, Tipping M (2003) Use of the zero-norm with linear models and kernel methods. J Mach Learn Res 3:1439–1461
40.
go back to reference R Core Team (2017) R: A language and environment for statistical computing. Version 3.3.3, R Foundation for Statistical Computing, Vienna, Austria. R Core Team (2017) R: A language and environment for statistical computing. Version 3.3.3, R Foundation for Statistical Computing, Vienna, Austria.
41.
go back to reference 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 63:185–197CrossRefPubMedPubMedCentral 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 63:185–197CrossRefPubMedPubMedCentral
42.
go back to reference 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 42:144–152CrossRefPubMed 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 42:144–152CrossRefPubMed
49.
go back to reference Xiang J, Varble N, Davies JM, Rai AT, Kono K, Sugiyama S, Binning MJ, Tawk RG, Choi H, Ringer AJ, Snyder KV, Levy EI, Hopkins LN, Siddiqui AH, Meng H (2017) Initial clinical experience with aview—a clinical computational platform for intracranial aneurysm morphology, hemodynamics, and treatment management. World Neurosurg 108:534–542. https://doi.org/10.1016/j.wneu.2017.09.030 CrossRefPubMed Xiang J, Varble N, Davies JM, Rai AT, Kono K, Sugiyama S, Binning MJ, Tawk RG, Choi H, Ringer AJ, Snyder KV, Levy EI, Hopkins LN, Siddiqui AH, Meng H (2017) Initial clinical experience with aview—a clinical computational platform for intracranial aneurysm morphology, hemodynamics, and treatment management. World Neurosurg 108:534–542. https://​doi.​org/​10.​1016/​j.​wneu.​2017.​09.​030 CrossRefPubMed
Metadata
Title
Development and internal validation of an aneurysm rupture probability model based on patient characteristics and aneurysm location, morphology, and hemodynamics
Authors
Felicitas J. Detmer
Bong Jae Chung
Fernando Mut
Martin Slawski
Farid Hamzei-Sichani
Christopher Putman
Carlos Jiménez
Juan R. Cebral
Publication date
01-11-2018
Publisher
Springer International Publishing
Published in
International Journal of Computer Assisted Radiology and Surgery / Issue 11/2018
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-018-1837-0

Other articles of this Issue 11/2018

International Journal of Computer Assisted Radiology and Surgery 11/2018 Go to the issue