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

Abstract

To develop risk estimation models for 1-year ischemic stroke recurrence using clinical risk factors and retinal characteristics. 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. 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. 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.