Abstract 17817: Deep Learning-Based Retinal Imaging for Predicting Cardiovascular Disease Events in Prediabetic and Diabetic Patients: A Study Using the UK Biobank

Abstract

Introduction: Our previous work led to developing a deep learning algorithm for retinal images, Reti-CVD, which effectively predicted cardiovascular disease (CVD) events in individuals without CVD history, leveraging coronary artery calcium (CAC) scores for algorithm training. Hypothesis: This study aims to assess the capability of deep learning-assisted retinal imaging to predict CVD events among prediabetic and diabetic patients using the data from the UK Biobank. Methods: Our study included prediabetic and diabetic patients from the UK Biobank. Reti-CVD scores were calculated and categorized into three risk groups - low (n=550), moderate (n=276), and high (n=275), based on the 50th and 75th percentiles, following a 2:1:1 ratio. To assess the Reti-CVD's ability in predicting fatal and non-fatal CVD events, we performed a survival analysis on the longitudinal data from the UK Biobank using Cox proportional-hazards models and hazard ratios (HRs). Results: Among the 1101 prediabetic or diabetic patients at the onset, 138 (12.5%) experienced CVD events. According to Reti-CVD scores, these events were found as 8.2% (45/550), 15.2% (42/276), and 18.5% (51/275) in the low, moderate, and high-risk groups over a median follow-up period of 11 years, respectively. After adjusting for factors such as age, gender, hypertensive medication use, statin use, and smoking history, a significant association was observed between the Reti-CVD and the incidence of CVD events (HR=1.57, 95% CI, 1.00-2.47 for the moderate-risk group; HR=1.88, 95% CI, 1.19-2.98 for the high-risk group compared to the low-risk group). An increasing HR trend of 1.36 (95% CI, 1.09-1.70) was observed across risk groups in the prediction of CVD events. Conclusions: The Reti-CVD offers a valuable tool for risk stratification among prediabetic and diabetic patients, indicating its potential in managing these high-risk groups.