国家神经系统疾病临床医学研究中心数据管理与共享系统

Data Management And Sharing System of China National Clinical Research Center for Neurological Diseases

《国家中心数据管理与共享系统操作说明书 —— 研究者部分》

ECAS score: a web-based risk model to predict moderate and severe extracranial carotid artery stenosis

第一作者：Yan Yinglin, Yinglin

通讯作者：Ji Ruijun, Ruijun

Extracranial carotid artery prediction risk model

基本信息

2018-04-01

已发表文章

发表杂志：Neurological Research

发表类型: Article

影响因子(IF): 2.448

PMID: 29392984 Pubmed原文链接 全文下载

主要研究背景及目的

方法

Methods The ECAS score was developed based on the Renqiu Stroke Screening Study (RSSS), in which eligible patients were randomly divided into derivation (60%) and validation (40%) cohorts. ECAS was diagnosed by carotid duplex ultrasound according to the published criteria. Independent predictors of moderate (≥50%) and severe (≥70%) ECAS were obtained using multivariable logistic regression. The area under the receiver operating characteristic curve (AUROC) and the Hosmer-Lemeshow test were used to assess model discrimination and calibration.

结论

Conclusion The ECAS score is a valid model for predicting moderate and severe ECAS. Further validation of the ECAS score in different populations and larger samples is warranted.

主要结果

Results A total of 5010 participants were included and the mean age was 64.3. The proportion of ECAS of < 50%, 50-69%, 70-99% and occlusion was 4.4, 0.5, 0.4, and 0.4%, respectively. The ECAS score was developed from sets of predictors of moderate and severe ECAS. The ECAS score demonstrated good discrimination in the derivation and validation cohorts (AUROC range: 0.785-0.846). The Hosmer-Lemeshow tests of ECAS score for moderate and severe ECAS were not significant in the derivation and validation cohorts (all P > 0.05). When compared to the three existing models, the ECAS score showed significantly better discrimination for both moderate and severe ECAS (all P < 0.001).