Abstract 11822: Pivotal Trial of a Deep-Learning-Based Retinal Biomarker (Reti-CVD) in the Prediction of Cardiovascular Disease: Data From CMERC-HI

Abstract

Introduction: The Korean Ministry of Food and Drug Safety (K-MFDS) has authorized over 110 artificial intelligence (AI)-software as medical devices (AI-SaMDs) for diagnostic purposes. Herein, we clinically validate the efficacy of a three-tier cardiovascular disease (CVD) risk stratification system developed using Reti-CVD, an AI-SaMD that utilizes retinal images to estimate CVD risk. The aim of this study is to evaluate the efficacy of RetiCVD in predicting future CVD risk based on retrospective analysis of a prior prospective cohort study. Methods: This clinical study was a single-center, retrospective, conformity-design, confirmatory analysis of prior prospective cohort study. For primary endpoint, retinal images were evaluated by Reti-CVD to validate its three-tier CVD risk stratification system. For secondary endpoints, cardiac CT-measured coronary artery calcium (CAC, 0, >0-100, and >100), carotid intima-media thickness (CIMT, <90 th and ≥90 th percentile), and brachial-ankle pulse wave velocity (baPWV, <1800 and ≥1800 cm/s) were also measured as independent variables for future CVD risk. The cumulative incidence of non-fatal and/or fatal CVD events was evaluated. Cox proportional hazards models were used to estimate the hazard ratios (HR) trends. Results: In this clinical cohort (n=1106), 33 (3.0%) participants had non-fatal or fatal CVD events over 5 years, and RetiCVD scores were significantly associated with increased CVD risk (HR trend=2.02, 95% CI, 1.26-3.24). In a multivariable Cox model incorporating RetiCVD scores, CAC, CIMT, baPWV, and other traditional risk factors, RetiCVD scores for the high-risk tier were significantly associated with increased CVD risk (low risk as a reference; HR=3.56 [1.34-9.51] in high risk), while other biomarkers showed trends toward association: CAC showed a HR of 2.45 [0.88-6.84] in CAC of >100 compared to zero CAC (reference); CIMT showed a HR of 1.50 (0.64-3.51) in CIMT of ≥90 th percentile compared to <90 th percentile; and baPWV showed a HR of 1.27 (0.53-3.03) in baPWV of ≥1800 compared to <1800 cm/s. Conclusions: This is the first K-MFDS approved AI-software that can potentially stratify future risk as biomarkers, which can have major implications such as identifying high-risk individuals.