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Morphological risk model assessing anterior communicating artery aneurysm rupture: Development and validation.
Yongchun Chen1, Haixia Xing2, Boli Lin3
1Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China; Department of Neurosurgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China.
Clinical Neurology and Neurosurgery
|August 25, 2020
Summary
Predicting anterior communicating artery (ACoA) aneurysm rupture is difficult. A new morphological risk score, considering factors like aneurysm size and shape, shows promise in assessing rupture risk.
Area of Science:
- Neurosurgery
- Radiology
- Medical Imaging
Background:
- Anterior communicating artery (ACoA) aneurysms pose a significant rupture risk, making accurate prediction crucial for patient management.
- Existing methods for predicting ACoA aneurysm rupture often lack precision, highlighting the need for improved risk stratification tools.
Purpose of the Study:
- To investigate the association between detailed aneurysm morphologies and the rupture risk of ACoA aneurysms.
- To develop and validate a morphological risk model for predicting ACoA aneurysm rupture.
Main Methods:
- Retrospective analysis of 759 patients with ACoA aneurysms, with a separate validation cohort.
- Detailed morphological parameters were measured using CT angiography.
- Univariable, multivariable analyses, and ROC curve analysis were employed to assess risk factors and model performance.
Main Results:
- Key morphological risk factors identified include larger aneurysm size, anterior projection, dominant A1 segment, and irregular aneurysm shape.
- Larger vessel size was found to be inversely associated with rupture risk.
- The developed morphological risk score demonstrated good predictive discrimination (AUC 0.73-0.80) and calibration in both derivation and validation cohorts.
Conclusions:
- A morphological risk model incorporating specific aneurysm features can aid in evaluating the rupture risk of ACoA aneurysms.
- This model offers a potential tool for clinicians to better stratify patients and guide treatment decisions for ACoA aneurysms.
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