Department of Neurology, Toulouse University Hospital, Toulouse, France.
Toulouse NeuroImaging Center, Toulouse University, Toulouse, France.
Ann Neurol. 2023 Jan;93(1):16-28. doi: 10.1002/ana.26519. Epub 2022 Nov 16.
OBJECTIVE: Determining the underlying causes of intracerebral hemorrhage (ICH) is of major importance, because risk factors, prognosis, and management differ by ICH subtype. We developed a new causal CLASsification system for ICH Subtypes, termed CLAS-ICH, based on recent advances in neuroimaging. METHODS: CLAS-ICH defines 5 ICH subtypes: arteriolosclerosis, cerebral amyloid angiopathy, mixed small vessel disease (SVD), other rare forms of SVD (genetic SVD and others), and secondary causes (macrovascular causes, tumor, and other rare causes). Every patient is scored in each category according to the level of diagnostic evidence: (1) well-defined ICH subtype; (2) possible underlying disease; and (0) no evidence of the disease. We evaluated CLAS-ICH in a derivation cohort of 113 patients with ICH from Massachusetts General Hospital, Boston, USA, and in a derivation cohort of 203 patients from Inselspital, Bern, Switzerland. RESULTS: In the derivation cohort, a well-defined ICH subtype could be identified in 74 (65.5%) patients, including 24 (21.2%) with arteriolosclerosis, 23 (20.4%) with cerebral amyloid angiopathy, 18 (15.9%) with mixed SVD, and 9 (8.0%) with a secondary cause. One or more possible causes were identified in 42 (37.2%) patients. Interobserver agreement was excellent for each category (kappa value ranging from 0.86 to 1.00). Despite substantial differences in imaging modalities, we obtained similar results in the validation cohort. INTERPRETATION: CLAS-ICH is a simple and reliable classification system for ICH subtyping, that captures overlap between causes and the level of diagnostic evidence. CLAS-ICH may guide clinicians to identify ICH causes, and improve ICH classification in multicenter studies. ANN NEUROL 2023;93:16-28.
目的:确定颅内出血 (ICH) 的根本原因非常重要,因为风险因素、预后和治疗方法因 ICH 亚型而异。我们基于神经影像学的最新进展,开发了一种新的 ICH 亚型因果分类系统,称为 CLAS-ICH。
方法:CLAS-ICH 将 ICH 分为 5 个亚型:动脉硬化、脑淀粉样血管病、混合小血管病 (SVD)、其他少见的 SVD 形式(遗传性 SVD 和其他)和继发性原因(大血管原因、肿瘤和其他少见原因)。根据诊断证据水平,每位患者在每个类别中均有评分:(1)明确的 ICH 亚型;(2)可能存在的基础疾病;和(0)无该疾病的证据。我们在美国波士顿马萨诸塞州总医院的 113 例 ICH 患者的推导队列和瑞士伯尔尼因斯泰尔医院的 203 例患者的推导队列中评估了 CLAS-ICH。
结果:在推导队列中,74 例(65.5%)患者可明确确定 ICH 亚型,其中 24 例(21.2%)为动脉硬化,23 例(20.4%)为脑淀粉样血管病,18 例(15.9%)为混合 SVD,9 例(8.0%)为继发性病因。42 例(37.2%)患者确定了一个或多个可能的病因。每个类别的观察者间一致性均非常好(kappa 值范围为 0.86 至 1.00)。尽管影像学方式存在很大差异,但在验证队列中我们获得了相似的结果。
结论:CLAS-ICH 是一种用于 ICH 亚型分类的简单而可靠的分类系统,可捕捉病因之间的重叠和诊断证据的水平。CLAS-ICH 可以帮助临床医生确定 ICH 病因,并提高多中心研究中的 ICH 分类。神经病学年鉴 2023;93:16-28.
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