Wen Zheng, Nie Xin, Chen Lei, Liu Peng, Lan Chuanjin, Mossa-Basha Mahmud, Levitt Michael R, He Hongwei, Wang Shuo, Li Jiangan, Zhu Chengcheng, Liu Qingyuan
Department of Neurosurgery, Beijing Tiantan Hospital, China National Clinical Research Center for Neurological Diseases, Capital Medical University, Beijing, China.
Department of Neurosurgery, the First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan, Guangdong, China.
Transl Stroke Res. 2024 Jul 22. doi: 10.1007/s12975-024-01280-7.
Chinese population have a high prevalence of unruptured intracranial aneurysm (UIA). Clinical and imaging risk factors predicting UIA growth or rupture are poorly understood in the Chinese population due to the lack of large-scale longitudinal studies, and the treatment decision for UIA patients was challenging. Develop a decision tree (DT) model for UIA instability, and validate its performance in multi-center studies. Single-UIA patients from two prospective, longitudinal multicenter cohort studies were analyzed, and set as the development cohort and validation cohort. The primary endpoint was UIA instability (rupture, growth, or morphological change). A DT was established within the development cohort and validated within the validation cohort. The performance of clinicians in identifying unstable UIAs before and after the help of the DT was compared using the area under curve (AUC). The development cohort included 1270 patients with 1270 UIAs and a follow-up duration of 47.2 ± 15.5 months. Aneurysm instability occurred in 187 (14.7%) patients. Multivariate Cox analysis revealed hypertension (hazard ratio [HR], 1.54; 95%CI, 1.14-2.09), aspect ratio (HR, 1.22; 95%CI, 1.17-1.28), size ratio (HR, 1.31; 95%CI, 1.23-1.41), bifurcation configuration (HR, 2.05; 95%CI, 1.52-2.78) and irregular shape (HR, 4.30; 95%CI, 3.19-5.80) as factors of instability. In the validation cohort (n = 106, 12 was unstable), the DT model incorporating these factors was highly predictive of UIA instability (AUC, 0.88 [95%CI, 0.79-0.97]), and superior to existing UIA risk scales such as PHASES and ELAPSS (AUC, 0.77 [95%CI, 0.67-0.86] and 0.76 [95%CI, 0.66-0.86], P < 0.001). Within all 1376 single-UIA patients, the use of the DT significantly improved the accuracy of junior neurosurgical clinicians to identify unstable UIAs (AUC from 0.63 to 0.82, P < 0.001). The DT incorporating hypertension, aspect ratio, size ratio, bifurcation configuration and irregular shape was able to predict UIA instability better than existing clinical scales in Chinese cohorts. CLINICAL TRIAL REGISTRATION: IARP-CP cohort were included (unique identifier: ChiCTR1900024547. Published July 15, 2019. Completed December 30, 2020), with 100-Project phase-I cohort (unique identifier: NCT04872842, Published May 5, 2021. Completed November 8, 2022) as the development cohort. The 100-Project phase-II cohort (unique identifier: NCT05608122. Published November 8, 2022) as the validation cohort.
中国人群中未破裂颅内动脉瘤（UIA）的患病率较高。由于缺乏大规模纵向研究，中国人群中预测UIA生长或破裂的临床和影像危险因素尚不清楚，UIA患者的治疗决策具有挑战性。开发一种用于UIA不稳定性的决策树（DT）模型，并在多中心研究中验证其性能。对来自两项前瞻性、纵向多中心队列研究的单发UIA患者进行分析，并将其作为开发队列和验证队列。主要终点是UIA不稳定性（破裂、生长或形态改变）。在开发队列中建立DT，并在验证队列中进行验证。使用曲线下面积（AUC）比较临床医生在借助DT前后识别不稳定UIA的表现。开发队列包括1270例患者的1270个UIA，随访时间为47.2±15.5个月。187例（14.7%）患者发生动脉瘤不稳定性。多变量Cox分析显示高血压（风险比[HR]，1.54；95%CI，1.14-2.09）、纵横比（HR，1.22；95%CI，1.17-1.28）、大小比（HR，1.31；95%CI，1.23-1.41）、分叉形态（HR，2.05；95%CI，1.52-2.78）和不规则形状（HR，4.30；95%CI，3.19-5.80）是不稳定性因素。在验证队列（n=106，12例不稳定）中，纳入这些因素的DT模型对UIA不稳定性具有高度预测性（AUC，0.88[95%CI，0.79-0.97]），优于现有的UIA风险量表，如PHASES和ELAPSS（AUC，0.77[95%CI，0.67-0.86]和0.76[95%CI，0.66-0.86]，P<0.001）。在所有1376例单发UIA患者中，使用DT显著提高了初级神经外科临床医生识别不稳定UIA的准确性（AUC从0.63提高到0.82，P<0.001）。纳入高血压、纵横比、大小比、分叉形态和不规则形状的DT在中国人队列中比现有临床量表能更好地预测UIA不稳定性。临床试验注册：纳入IARP-CP队列（唯一标识符：ChiCTR1900024547。2019年7月15日发布。2020年12月30日完成），以100-项目I期队列（唯一标识符：NCT04872842，2021年5月5日发布。2022年11月8日完成）作为开发队列。100-项目II期队列（唯一标识符：NCT05608122。2022年11月8日发布）作为验证队列。
