作者
Jiaming Zhang,Zengli Miao,Mervyn D. I. Vergouwen,Zheng Wen,Lei Chen,Xin Nie,Kaige Zheng,Shaohua Mo,Kaiwen Wang,Yi Yang,Yang Liu,Jiangan Li,Pengjun Jiang,Wenju Jiang,Xun Ye,Hongwei He,Yan Zhang,Mahmud Mossa‐Basha,Michael R. Levitt,Peng Liu
摘要
OBJECTIVE: 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. METHODS: This cross-sectional survey enrolled adults aged 35-75 years from October 2023 to February 2025. Participants were recruited from 3 randomly selected communities across 2 urban and one suburban districts in Beijing, China, and received magnetic resonance angiography (MRA). 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. RESULTS: Of 5120 potentially eligible residents, 4711 (92%) were included after exclusions. The median age was 57 (interquartile range, 50-61) years and 2576 (55%) were women. The observed MRA-based prevalence of UIAs was 9.2% (95% confidence interval [CI], 8.3%-10.0%). A multivariate logistic analysis revealed that older age (adjusted odds ratio [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 family history of aneurysm or subarachnoid hemorrhage, age, sex, hypertension, and diabetes mellitus model based on clinical parameters performed well in determining the probability of having UIAs (area under the curve, 0.86; 95% CI, 0.84-0.89). CONCLUSIONS: The MRA-based prevalence of UIAs was approximately 9%. The family history of aneurysm or subarachnoid hemorrhage, age, sex, hypertension, and diabetes mellitus model may help identify individuals with a higher probability of having a prevalent UIA, but external validation is required.