Zhu Wei, Li Wenqiang, Tian Zhongbin, Zhang Mingqi, Zhang Yisen, Wang Kun, Zhang Ying, Yang Xinjian, Liu Jian
Department of Interventional Neuroradiology, Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Front Neurol. 2021 Jan 15;11:598740. doi: 10.3389/fneur.2020.598740. eCollection 2020.
Stability stratification of intracranial aneurysms (IAs) is crucial for individualized clinical management, especially for small IAs. We aim to develop and validate a nomogram based on clinical and morphological risk factors for individualized instability stratification of small IAs. Six hundred fifty-eight patients with unstable ( = 293) and stable ( = 416) IAs <7 mm were randomly divided into derivation and validation cohorts. Twelve clinical risk factors and 18 aneurysm morphological risk factors were extracted. Combined with important risk factors, a clinical-morphological predictive nomogram was developed. The nomogram performance was evaluated in the derivation and the validation cohorts in terms of discrimination, calibration, and clinical usefulness. Five independent instability-related risk factors were included in the nomogram: location, irregularity, side/bifurcation type, flow angle, and height-to-width ratio. In the derivation cohort, the area under the curve (95% CI) of the nomogram was 0.803 (95% CI, 0.764-0.842), and good agreement between predicted instability risk and actual instability status could be detected in the calibration plot. The nomogram also exhibited good discriminations and calibration in the validation cohort: the area under the curve (95% CI) was 0.744 (95% CI, 0.677-0.812). Small IAs with scores <90 were considered to have low risk of instability, and those with scores of 90 or greater were considered to have high risk of instability. The nomogram based on clinical and morphological risk factors can be used as a convenient tool to facilitate individualized decision-making in the management of small IAs.
颅内动脉瘤(IA)的稳定性分层对于个体化临床管理至关重要,尤其是对于小型IA。我们旨在开发并验证一种基于临床和形态学危险因素的列线图,用于小型IA的个体化不稳定分层。658例不稳定(n = 293)和稳定(n = 416)的直径<7 mm的IA患者被随机分为推导队列和验证队列。提取了12个临床危险因素和18个动脉瘤形态学危险因素。结合重要危险因素,开发了一种临床-形态学预测列线图。在推导队列和验证队列中,从区分度、校准度和临床实用性方面对列线图性能进行了评估。列线图纳入了5个与不稳定相关的独立危险因素:位置、不规则性、侧支/分叉类型、血流角度和高宽比。在推导队列中,列线图的曲线下面积(95%CI)为0.803(95%CI,0.764 - 0.842),在校准图中可检测到预测的不稳定风险与实际不稳定状态之间具有良好的一致性。列线图在验证队列中也表现出良好的区分度和校准度:曲线下面积(95%CI)为0.744(95%CI,0.677 - 0.812)。得分<90的小型IA被认为不稳定风险低,得分90及以上的则被认为不稳定风险高。基于临床和形态学危险因素的列线图可作为一种便捷工具,有助于小型IA管理中的个体化决策。