Comparison of Prediction Models based on Risk Factors and Retinal Characteristics Associated with Recurrence One Year after Ischemic Stroke.

OBJECTIVES:To develop risk estimation models for 1-year ischemic stroke recurrence using clinical risk factors and retinal characteristics. METHODS:From June 2017 to January 2019, 332 patients with first-ever ischemic stroke were enrolled and followed up in the Shenzhen Traditional Chinese Medicine hospital in China. The primary endpoint was defined as fatal or recurrent stroke after 1 year of the index stroke. Clinical risk factors and retinal characteristics were identified by multivariate logistic models. RESULTS:The multivariate logistic model with only clinical risk factors showed that Cerebral Atherosclerosis (OR 1.68, 95%CI: 1.000-2.81), white matter lesions (OR 3.61, 95%CI: 2.18-5.98), and Cardiac disease (OR 1.88, 95%CI: 1.02-3.46) were statistically significantly associated with higher stroke recurrence risk. The sensitivity and specificity of this model were 69.1% and 68.4% respectively. The multivariate logistic model with only retinal characteristics showed that central retinal venule equivalent (OR .34, 95%CI: .14-.83), hemorrhage (OR .6, 95%CI: .41-.88), exudate (OR 1.64, 95%CI: 1.16-2.32), central retinal artery equivalent (OR 2.95, 95%CI: 1.23-7.08), and Aangle (OR 0.8, 95%CI: .61-1.004) were statistically significantly associated with stroke recurrence. The sensitivity and specificity of the model were 62.0% and 64.4% respectively. The multivariate logistic model with both clinical risk factors and retinal characteristics showed that cerebral atherosclerosis (OR 1.74, 95%CI: 1.020-2.981), white matter lesions (OR 3.65, 95%CI: 2.17-6.13), cardiac disease (OR 1.99, 95%CI: 1.06-3.74), hemorrhage (OR .68, 95%CI: .49-.96), exudate (OR 1.65, 95%CI: 1.16-2.36) were independent risk factors of stroke recurrence. The sensitivity and specificity of the model were 72.5% and 70.7% respectively. CONCLUSIONS:Combining the traditional risk factors of stroke with the retinal vessels characteristics to establish the recurrent cerebral infarction prediction model may improve the accuracy of the prediction.