A Decision Tree Model to Help Treatment Decision-Making for Unruptured Intracranial Aneurysms: A Multi-center, Long-Term Follow-up Study in a Large Chinese Cohort.

IF 3.8 2区 医学 Q1 CLINICAL NEUROLOGY
Zheng Wen, Xin Nie, Lei Chen, Peng Liu, Chuanjin Lan, Mahmud Mossa-Basha, Michael R Levitt, Hongwei He, Shuo Wang, Jiangan Li, Chengcheng Zhu, Qingyuan Liu
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引用次数: 0

Abstract

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.

Abstract Image

帮助未破裂颅内动脉瘤治疗决策的决策树模型:一项大型中国队列的多中心长期随访研究。
中国人是未破裂颅内动脉瘤(UIA)的高发人群。由于缺乏大规模的纵向研究,中国人群对预测UIA生长或破裂的临床和影像学风险因素了解甚少,UIA患者的治疗决策面临挑战。开发UIA不稳定性决策树(DT)模型,并在多中心研究中验证其性能。分析了两项前瞻性纵向多中心队列研究中的单例 UIA 患者,并将其设定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)。在发展队列中建立了 DT,并在验证队列中进行了验证。在使用 DT 之前和之后,临床医生在识别不稳定 UIA 方面的表现采用曲线下面积 (AUC) 进行比较。开发队列包括 1270 名患者和 1270 个 UIA,随访时间为 47.2 ± 15.5 个月。动脉瘤不稳定发生在 187 例(14.7%)患者中。多变量 Cox 分析显示,高血压(危险比 [HR],1.54;95%CI,1.14-2.09)、长宽比(HR,1.22;95%CI,1.17-1.28)、大小比(HR,1.31;95%CI,1.23-1.41)、分叉结构(HR,2.05;95%CI,1.52-2.78)和不规则形状(HR,4.30;95%CI,3.19-5.80)是导致不稳定的因素。在验证队列(n = 106,12 个不稳定)中,包含这些因素的 DT 模型对 UIA 不稳定具有高度预测性(AUC,0.88 [95%CI,0.79-0.97]),优于 PHASES 和 ELAPSS 等现有的 UIA 风险量表(AUC,0.77 [95%CI,0.67-0.86] 和 0.76 [95%CI,0.66-0.86],P<0.05)。
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来源期刊
Translational Stroke Research
Translational Stroke Research CLINICAL NEUROLOGY-NEUROSCIENCES
CiteScore
13.80
自引率
4.30%
发文量
130
审稿时长
6-12 weeks
期刊介绍: Translational Stroke Research covers basic, translational, and clinical studies. The Journal emphasizes novel approaches to help both to understand clinical phenomenon through basic science tools, and to translate basic science discoveries into the development of new strategies for the prevention, assessment, treatment, and enhancement of central nervous system repair after stroke and other forms of neurotrauma. Translational Stroke Research focuses on translational research and is relevant to both basic scientists and physicians, including but not restricted to neuroscientists, vascular biologists, neurologists, neuroimagers, and neurosurgeons.
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