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

Abstract

This study aimed to build a prediction model to early diagnose intracranial atherosclerosis (ICAS)-related large vascular occlusion (LVO) in acute ischemic stroke patients before digital subtractive angiography. Patients enrolled in the DIRECT-MT trial (NCT03469206) were included in our secondary analysis and distributed into ICAS-LVO and non-ICAS-LVO groups. We also retrieved demographic data, medical histories, clinical characteristics, and pre-operative imaging data. Hypothesis testing was used to compare data of the two groups, and univariate logistic regression was used to identify the predictors of ICAS-LVO primarily. Then, we used multivariate logistic regression to determine the independent predictors and formulate the prediction model. Model efficacy was estimated by the area under the receiver operating characteristic (ROC) curve (AUC) and diagnostic parameters generated from internal and external validations. The subgroup analysis included 45 cases in the ICAS-LVO group and 611 cases in the non-ICAS-LVO group. Variates with p < 0.1 in the comparative analysis were used as inputs in the univariate logistic regression. Next, variates with p < 0.1 in the univariate logistic regression were used as inputs in the multivariate logistic regression. The multivariate logistic regression indicated that the atrial fibrillation history, hypertension and smoking, occlusion located at the proximal M1 and M2, hyperdense artery sign, and clot burden score were related to the diagnosis of ICAS-LVO. Then, we constructed a prediction model based on multivariate logistics regression. 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. Our current prediction model based on clinical data of patients from the DIRECT-MT trial might be a promising tool for predicting ICAS-LVO.