Novel predictors and a predictive model of cerebrovascular atherosclerotic ischemic stroke based on clinical databases.

Background And Purpose: Early identification of cerebrovascular atherosclerotic ischemic stroke is necessary for accurate treatment and clinical research.

Aims: To identify novel predictors and build a predictive model of ischemic strokes due to cerebrovascular atherosclerosis.

Method: MIMIC-IV database was used to search for clinical data of patients with ischemic stroke. Included patients were divided into two groups according to their etiologies. Univariate and multivariate logistic regressions were used to build the predictive model, and the model reliability parameters were calculated. The cut-off value for the model was selected according to the Youden index. Clinical data from the Neurovascular Center of Changhai Hospital were used to verify the predictive model.

Results: Logistical regressions showed a positive correlation between advanced age, peripheral atherosclerosis, history of transient ischemia, and the diagnosis of ischemic strokes due to cerebrovascular atherosclerosis. The history of atrial fibrillation, levels of the National Institutes of Health Stroke Scale, serum potassium, and activated partial thromboplastin time were negatively correlated to the diagnosis of cerebrovascular atherosclerotic ischemic stroke. The predictive model was constructed from logistic regression results, and the area under the curve was 0.764. The cut-off value for the model was set at 0.089 to achieve the highest Youden index, with sensitivity and specificity of 75.9% and 64.1%. Clinical verification of the model revealed that the sensitivity and specificity of the model were 52.5% and 93.0% respectively.

Conclusion: The efficacy of the predictive model was acceptable as an aid in predicting cerebrovascular atherosclerotic ischemic stroke.