• Haoxian Long, Wenqiang Che, Chenyou Yang, Yonglong Liao, Jiamin Wu, Chuan Chen, Xiangyu Wang, and Jun Wen.
• Department of Neurosurgery, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China.
• World Neurosurg. 2024 Dec 7.

Background And PurposePredicting rupture risk in small intracranial aneurysms (IAs) < 5 mm is crucial for guiding clinical decisions. This study aims to identify key clinical and morphological risk factors associated with rupture in small IAs, providing better insight for decision-making.MethodsA retrospective analysis was performed on patients with small IAs from one center, with external validation data from another center. Logistic regression identified significant risk factors for aneurysm rupture, which were incorporated into a predictive model. The model's performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), calibration plots, and the Hosmer-Lemeshow (H-L) goodness-of-fit test. Clinical utility was assessed via decision curve analysis (DCA).ResultsThe training cohort consisted of 226 patients (ruptured, n = 181; unruptured, n = 92), while 136 patients (ruptured, n = 100; unruptured, n = 59) were used for external validation. Significant risk factors included hypertension, smoking, anterior communicating artery aneurysms (AcomA), daughter sacs, aspect ratio (AR), and size ratio (SR). The model demonstrated strong predictive ability with AUCs of 0.969 and 0.967 in the training and validation cohorts, respectively. Calibration plots indicated a good agreement between predicted and observed rupture risks, while DCA highlighted the model's clinical relevance.ConclusionThis study identifies and validates critical risk factors associated with small IA rupture and presents a clinically useful, high-accuracy predictive model to aid in individualized patient management.Copyright © 2024. Published by Elsevier Inc.

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