Research Article

IDENTIFICATION OF RISK FACTORS FOR CEREBRAL ANEURYSM RUPTURE THROUGH STATISTICAL ANALYSIS

Volume: 11 Number: 2 December 30, 2025

IDENTIFICATION OF RISK FACTORS FOR CEREBRAL ANEURYSM RUPTURE THROUGH STATISTICAL ANALYSIS

Abstract

This study examines the relationship between clinical, morphological, and hemodynamic factors and the risk of rupture in individuals with cerebral aneurysms. A logistic regression model was developed to identify variables influencing aneurysm rupture. The results indicate that age (p=0.0074) and comorbidities (p=0.0157) have a statistically significant effect on rupture probability. Variance Inflation Factor (VIF) analysis revealed multicollinearity between the length and width variables (VIF=3.96), while no such relationship was detected for other variables. Chi-square and Mann-Whitney U tests confirmed that age, aneurysm type, and comorbidities differ significantly between patients with and without rupture (p<0.05). Post-hoc analysis further supported statistically significant differences between groups concerning age (p=0.0001), aneurysm type (p=0.0004), and comorbidities (p=0.0039). Fisher’s exact test demonstrated a significant association between rupture risk and variables such as diabetes, ischemic cerebrovascular disease, a history of ischemic stroke, and coronary artery predisposition (p<0.05). This comprehensive analysis highlights the critical role of age, aneurysm type, and specific comorbidities in determining the risk of cerebral aneurysm rupture.

Keywords

Ethical Statement

The author declares that this study required ethics committee approval. The Fırat University Non-Interventional Studies Ethics Committee approved the use of the data in this study with the board decision numbered 2020/12-04, dated September 17, 2020.

Thanks

The authors would like to thank Sait Öztürk for his contributions to the information provided regarding patients with cerebral aneurysms.

References

  1. Cinar C, Oran I., “Intrakraniyal Dissekan ve Travmatik Anevrizmalarda Tedavi”. Türk Radyoloji, 10(1), 115-127, 2022. https://doi.org/10.5152/trs.2022.220754.
  2. Yavuz Çelikdemir M, Akbal A. “An Accurate Aneurysm Detection Model based on Artificial Intelligence”. Muş Alparslan Üniversitesi Fen Bilim Derg, 13(2), 224-237, 2025. https://doi.org/10.18586/msufbd.1686309.
  3. Çelikdemir MY, Akbal A. “Determination of the Rupture Risk of Cerebral Aneurysms via the Application of a Narrow Neural Network Classifier”. Trait Du Signal, 42, 875-885, 2025. https://doi.org/10.18280/ts.420224.
  4. Li MH, Chen SW, Li YD, Chen YC, Cheng YS, Hu DJ, et al. “Prevalence of unruptured cerebral aneurysms in Chinese adults aged 35 to 75 years: A cross-sectional study”. Ann Intern Med, 159(8), 2013. https://doi.org/10.7326/0003-4819-159-8-201310150-00004.
  5. Wei X, Jiang J, Cao W, Yu H, Deng H, Chen J, et al. “Artificial intelligence assistance improves the accuracy and efficiency of intracranial aneurysm detection with CT angiography”. Eur J Radiol, 149, 2022. https://doi.org/10.1016/j.ejrad.2022.110169.
  6. Greving JP, Wermer MJH, Brown RD, Morita A, Juvela S, Yonekura M, et al. “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(1), 59-66, 2014. https://doi.org/10.1016/S1474-4422(13)70263-1.
  7. Mensah E, Pringle C, Roberts G, Gurusinghe N, Golash A, Alalade AF. “Deep Learning in the Management of Intracranial Aneurysms and Cerebrovascular Diseases: A Review of the Current Literature”. World Neurosurg, 161, 39-45, 2022. https://doi.org/10.1016/j.wneu.2022.02.006.
  8. Gu F, Wu X, Wu W, Wang Z, Yang X, Chen Z, et al. “Performance of deep learning in the detection of intracranial aneurysm: A systematic review and meta-analysis”. Eur J Radiol, 155, 2022. https://doi.org/10.1016/j.ejrad.2022.110457.

Details

Primary Language

English

Subjects

Electrical Engineering (Other)

Journal Section

Research Article

Publication Date

December 30, 2025

Submission Date

March 15, 2025

Acceptance Date

July 16, 2025

Published in Issue

Year 2025 Volume: 11 Number: 2

APA
Yavuz Çelikdemir, M., & Akbal, A. (2025). IDENTIFICATION OF RISK FACTORS FOR CEREBRAL ANEURYSM RUPTURE THROUGH STATISTICAL ANALYSIS. Middle East Journal of Science, 11(2), 306-320. https://doi.org/10.51477/mejs.1658742
AMA
1.Yavuz Çelikdemir M, Akbal A. IDENTIFICATION OF RISK FACTORS FOR CEREBRAL ANEURYSM RUPTURE THROUGH STATISTICAL ANALYSIS. MEJS. 2025;11(2):306-320. doi:10.51477/mejs.1658742
Chicago
Yavuz Çelikdemir, Meltem, and Ayhan Akbal. 2025. “IDENTIFICATION OF RISK FACTORS FOR CEREBRAL ANEURYSM RUPTURE THROUGH STATISTICAL ANALYSIS”. Middle East Journal of Science 11 (2): 306-20. https://doi.org/10.51477/mejs.1658742.
EndNote
Yavuz Çelikdemir M, Akbal A (December 1, 2025) IDENTIFICATION OF RISK FACTORS FOR CEREBRAL ANEURYSM RUPTURE THROUGH STATISTICAL ANALYSIS. Middle East Journal of Science 11 2 306–320.
IEEE
[1]M. Yavuz Çelikdemir and A. Akbal, “IDENTIFICATION OF RISK FACTORS FOR CEREBRAL ANEURYSM RUPTURE THROUGH STATISTICAL ANALYSIS”, MEJS, vol. 11, no. 2, pp. 306–320, Dec. 2025, doi: 10.51477/mejs.1658742.
ISNAD
Yavuz Çelikdemir, Meltem - Akbal, Ayhan. “IDENTIFICATION OF RISK FACTORS FOR CEREBRAL ANEURYSM RUPTURE THROUGH STATISTICAL ANALYSIS”. Middle East Journal of Science 11/2 (December 1, 2025): 306-320. https://doi.org/10.51477/mejs.1658742.
JAMA
1.Yavuz Çelikdemir M, Akbal A. IDENTIFICATION OF RISK FACTORS FOR CEREBRAL ANEURYSM RUPTURE THROUGH STATISTICAL ANALYSIS. MEJS. 2025;11:306–320.
MLA
Yavuz Çelikdemir, Meltem, and Ayhan Akbal. “IDENTIFICATION OF RISK FACTORS FOR CEREBRAL ANEURYSM RUPTURE THROUGH STATISTICAL ANALYSIS”. Middle East Journal of Science, vol. 11, no. 2, Dec. 2025, pp. 306-20, doi:10.51477/mejs.1658742.
Vancouver
1.Meltem Yavuz Çelikdemir, Ayhan Akbal. IDENTIFICATION OF RISK FACTORS FOR CEREBRAL ANEURYSM RUPTURE THROUGH STATISTICAL ANALYSIS. MEJS. 2025 Dec. 1;11(2):306-20. doi:10.51477/mejs.1658742

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