Identification of Key Risk Factors for Rupture in Small Intracranial Aneurysms: A Multicenter Study.

BACKGROUND

Predicting 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.

METHODS

A 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 curve, calibration plots, and the Hosmer-Lemeshow goodness-of-fit test. Clinical utility was assessed via decision curve analysis.

RESULTS

The 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, daughter sacs, aspect ratio, and size ratio. The model demonstrated strong predictive ability with area under the curves 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 decision curve analysis highlighted the model's clinical relevance.

CONCLUSION

This 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.