AHA Scientific Session Abstract, November 2022
In our previous study, we developed a deep-learning-based novel cardiovascular disease (CVD) risk stratification system based on retinal photographs, Reti-CVD. This study aims to further validate Reti-CVD in the US population of the Age-Related Eye Disease Studies (AREDS).
Our testing cohort comprises of AREDS participants who have undergone retinal photography. We evaluated the ability of Reti-CVD to predict atherosclerotic CVD (ASCVD) events (non-fatal and fatal events, and fatal events) using Cox proportional-hazards models. Reti-CVD scores were then calculated and stratified into three groups based on optimized cut-off values from AREDS. Upon further stratifying Reti-CVD scores, we evaluated whether adding Reti-CVD to traditional risk factors could improve risk prediction.
Among 3,555 participants, 282 (7.9%) had non-fatal and fatal ASCVD events, and 84 (2.4%) had fatal ASCVD during the 13-year follow-up. Reti-CVD was significantly associated with an increased risk of ASCVD, demonstrating an adjusted hazard ratio (HR) trend of 1.13 (95% CI, 1.03-1.24) for non-fatal and fatal ASCVD events, and an adjusted HR trend of 1.27 (1.07-1.52) for fatal ASCVD events. Reti-CVD significantly improved the overall predictive performance of traditional risk models with a continuous net reclassification index (NRI) of 0.247 (0.106-0.364) for non-fatal and fatal ASCVD events, and 0.232 (0.107-0.359) for fatal ASCVD events. In heatmaps, traditional risk features such as arteriovenous nicking and arteriolar narrowing were well-detected by the Reti-CVD algorithm.
A deep-learning-based, retinal photograph-derived new CVD biomarker, Reti-CVD, can be utilized as a risk stratification tool in predicting ASCVD risk in the US population. With the improvements observed on the current risk stratification by traditional risk models, Reti-CVD has considerable potential as an enhanced CVD risk stratification tool applicable in the general US population.