Prevalence and risk factors of unruptured intracranial aneurysms in Chinese adults.
To measure the prevalence of unruptured intracranial aneurysms (UIAs) in Chinese adults aged 35-75 years and establish a model to identify individuals with UIAs.
This cross-sectional survey enrolled adults aged 35-75 years from October 2023 to February 2025. Participants were recruited from three randomly selected communities across two urban and one suburban districts in Beijing, China, and received MR angiography. All images were initially reviewed by attending neuroradiologists and subsequently confirmed by senior neurointerventionalists. A logistic regression model identified risk factors, and a nomogram was developed and evaluated using area under the curve (AUC).
Of 5,120 potentially eligible residents, 4,711 (92%) were included after exclusions. The median age was 57 (IQR, 50-61) years and 2,576 (55%) were women. The observed MRA-based prevalence of UIAs was 9.2% (95% CI, 8.3-10.0%). Multivariate logistic analysis revealed that older age (adjusted OR, 1.03; 95% CI, 1.02-1.04), female sex (adjusted OR, 1.33; 95% CI, 1.08-1.63), family history of intracranial aneurysm or subarachnoid hemorrhage (adjusted OR, 2.36; 95% CI, 1.77-3.12), hypertension (adjusted OR, 1.65; 95% CI, 1.22-2.12), and diabetes mellitus (adjusted OR, 1.63; 95% CI, 1.33-2.00) were independently associated with an increased risk of UIAs. The FASH-D model based on clinical parameters performed well in determining the probability of having UIAs (AUC, 0.86; 95% CI, 0.84-0.89).
The MRA-based prevalence of UIAs was approximately 9%. The FASH-D model may help identify individuals with a higher probability of having a prevalent UIA, but external validation is required.
This cross-sectional survey enrolled adults aged 35-75 years from October 2023 to February 2025. Participants were recruited from three randomly selected communities across two urban and one suburban districts in Beijing, China, and received MR angiography. All images were initially reviewed by attending neuroradiologists and subsequently confirmed by senior neurointerventionalists. A logistic regression model identified risk factors, and a nomogram was developed and evaluated using area under the curve (AUC).
Of 5,120 potentially eligible residents, 4,711 (92%) were included after exclusions. The median age was 57 (IQR, 50-61) years and 2,576 (55%) were women. The observed MRA-based prevalence of UIAs was 9.2% (95% CI, 8.3-10.0%). Multivariate logistic analysis revealed that older age (adjusted OR, 1.03; 95% CI, 1.02-1.04), female sex (adjusted OR, 1.33; 95% CI, 1.08-1.63), family history of intracranial aneurysm or subarachnoid hemorrhage (adjusted OR, 2.36; 95% CI, 1.77-3.12), hypertension (adjusted OR, 1.65; 95% CI, 1.22-2.12), and diabetes mellitus (adjusted OR, 1.63; 95% CI, 1.33-2.00) were independently associated with an increased risk of UIAs. The FASH-D model based on clinical parameters performed well in determining the probability of having UIAs (AUC, 0.86; 95% CI, 0.84-0.89).
The MRA-based prevalence of UIAs was approximately 9%. The FASH-D model may help identify individuals with a higher probability of having a prevalent UIA, but external validation is required.
Authors
Zhang Zhang, Miao Miao, Vergouwen Vergouwen, Wen Wen, Chen Chen, Nie Nie, Zheng Zheng, Mo Mo, Wang Wang, Yang Yang, Liu Liu, Li Li, Jiang Jiang, Jiang Jiang, Ye Ye, He He, Zhang Zhang, Mossa-Basha Mossa-Basha, Levitt Levitt, Liu Liu, Zhao Zhao, Zhu Zhu, Liu Liu, Wang Wang
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