Deep learning-based retinal risk stratification for 10-year atherosclerotic cardiovascular mortality in patients with hypertension and diabetes: a retrospective cohort study

Background: Cardiovascular risk stratification in patients with hypertension and diabetes remains suboptimal. We evaluated the prognostic and incremental value of Dr. Noon CVD, a deep learning (DL)-based retinal biomarker, for 10-year atherosclerotic cardiovascular (ASCVD) mortality. Methods: This retrospective, single-center, primary prevention cohort study included 7,832 Korean patients with hypertension or diabetes diagnosed on or before retinal fundus photography (2005 to 2022). Patients with prior cardiovascular disease were excluded. The primary outcome of 10-year ASCVD mortality and a sensitivity analysis of 5-year mortality were stratified using 4-tier and 3-tier Dr. Noon CVD systems, respectively. Multivariable Cox proportional hazards models were adjusted for traditional risk factors and chronic kidney disease (CKD), and incremental predictive value was assessed using the change in the C-index (ΔC-index) and net reclassification improvement (NRI). Results: = 0.003) subgroups. The reclassification improvement was greater in the diabetes than the hypertension subgroup (10-year NRI, 0.481 vs. 0.413). The score's prognostic value was independent of diabetic retinopathy status. Conclusions: DL-based retinal imaging provides prognostic value for 10-year ASCVD mortality independent of the available clinical risk factors (age, sex, hypertension, diabetes, and CKD) and consistent under adjustment for available laboratory measures, supporting its role as an accessible opportunistic screening tool for long-term primary prevention in cardiometabolic clinics.