AUTHOR=Li He , Ma Hong-Yu , Zhang Lei , Liu Pei , Zhang Yong-Xin , Zhang Xiao-Xi , Li Zi-Fu , Xing Peng-Fei , Zhang Yong-Wei , Li Qiang , Yang Peng-Fei , Liu Jian-Min 

TITLE=Early diagnosis of intracranial atherosclerotic large vascular occlusion: A prediction model based on DIRECT-MT data

JOURNAL=Frontiers in Neurology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2022.1026815

DOI=10.3389/fneur.2022.1026815

ISSN=1664-2295

ABSTRACT=Aims: To build a prediction model to early diagnose intracranial atherosclerosis (ICAS) related large vascular occlusion (LVO) in patients with acute ischemic stroke before digital subtractive angiography. 
Methods: The patients enrolled in the DIRECT-MT trial (NCT03469206) were included in our secondary analysis and distributed into the ICAS-LVO and non-ICAS-LVO groups. Demographic data, medical histories, clinical characteristics, and pre-operative imaging data were investigated. Hypothesis testing was used to compare the data of two groups, and univariate logistic regression was used to identify the predictors of ICAS-LVO primarily. Multivariate logistic regression was performed to determine the independent predictors and formulate the prediction model. The efficacy of the models was estimated by the area under the receiver operating characteristic curve and diagnostic parameters generated from internal and external validations. 
Results: The subgroup analysis included 45 cases in the ICAS-LVO group and 611 cases in the non-ICAS-LVO group. The variates with p<0.1 in the comparative analysis were input into the univariate logistic regression. The variates with p<0.1 in the univariate logistic regression were further input into the multivariate logistic regression. Multivariate logistic regression indicated that the histories of atrial fibrillation, hypertension and smoking, occlusion located at proximal M1 and M2, hyperdense artery sign, and clot burden score were independent predictors of ICAS-LVO. The prediction model based on the multivariate logistics regression were constructed, and the sensitivity and specificity of the model were 84.09% and 74.54% in internal validation and 73.11% and 71.53% in external validation. 
Conclusion: The prediction model based on clinical data of patients from the DIRECT-MT trial was a promising tool for predicting ICAS-LVO.