Intracranial artery stenosis (ICAS) is a leading cause of ischemic stroke worldwide, contributing to the global burden of stroke, particularly in the Asian population. However, there is no non-invasive, easy to popularize and economic for intracranial artery stenosis in mass population screening. This study aims to evaluate the predictive value of retinal vascular signs for intracranial artery stenosis (ICAS) and explore a new screening method.
Intracranial atherosclerosis stenosis (ICAS) is a leading cause of ischemic stroke worldwide, contributing to the global burden of stroke, particularly in the Asian population. Compared with the other stroke subtypes, patients with ICAS particularly the degree of stenosis≥50% had more severe stroke, stayed longer in the hospital and higher risk of recurrent ischemic events. However, there is no non-invasive, easy to popularize and economic for intracranial artery stenosis in mass population screening. Retinal imaging has great advantages including share the same embryological origin, anatomic features, and physiological properties with brain, non-invasive, easy to popularize, inexpensive and possess good economic benefits. Therefore, investigators design this study to assess the predictive value of retinal vascular signs for intracranial artery stenosis (ICAS) and hope to explore a new screening method.
Study Type
OBSERVATIONAL
Enrollment
1,000
Beijing Tiantan Hospital, Capital Medical University
Beijing, Fengtai District, China
Xuanwu hospital;Capital Medical University
Beijin, XI Cheng District, China
Sensitivity
Number of true positives /(number of true positives + number of false negatives)\*100
Time frame: 6 months
Specificity
Number of true negatives /(number of true negatives + number of false positives)\*100
Time frame: 6 months
Positive predictive value
Positive predictive value = true positive cases /(true positive cases + false positive cases)\*100
Time frame: 6 months
Negative predictive value
Negative predictive value = number of true negatives /(number of true negatives + number of false negatives)\*100
Time frame: 6 months
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