Cardiovascular disease risk assessment using a deep-learning-based retinal biomarker: a comparison with existing risk scores

AimsThis study aims to evaluate the ability of a deep-learning-based cardiovascular disease (CVD) retinal biomarker, Reti-CVD, to identify individuals with intermediate- and high-risk for CVD.Methods and resultsWe defined the intermediate- and high-risk groups according to Pooled Cohort Equation (PCE), QRISK3, and modified Framingham Risk Score (FRS). Reti-CVD's prediction was compared to the number of individuals identified as intermediate- and high-risk according to standard CVD risk assessment tools, and sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated to assess the results. In the UK Biobank, among 48 260 participants, 20 643 (42.8%) and 7192 (14.9%) were classified into the intermediate- and high-risk groups according to PCE, and QRISK3, respectively. In the Singapore Epidemiology of Eye Diseases study, among 6810 participants, 3799 (55.8%) were classified as intermediate- and high-risk group according to modified FRS. Reti-CVD identified PCE-based intermediate- and high-risk groups with a sensitivity, specificity, PPV, and NPV of 82.7%, 87.6%, 86.5%, and 84.0%, respectively. Reti-CVD identified QRISK3-based intermediate- and high-risk groups with a sensitivity, specificity, PPV, and NPV of 82.6%, 85.5%, 49.9%, and 96.6%, respectively. Reti-CVD identified intermediate- and high-risk groups according to the modified FRS with a sensitivity, specificity, PPV, and NPV of 82.1%, 80.6%, 76.4%, and 85.5%, respectively.ConclusionThe retinal photograph biomarker (Reti-CVD) was able to identify individuals with intermediate and high-risk for CVD, in accordance with existing risk assessment tools. Graphical AbstractThe Pooled Cohort Equation (PCE), QRISK3, and modified Framingham Risk Score (FRS) are formal risk assessment tools to guide primary prevention of cardiovascular disease in the USA, UK, and Singapore, respectively. However, these risk assessment tools require a lipid profiling via blood tests. Currently, a non-invasive cardiovascular disease (CVD) risk assessment tool is not available, particularly in stratifying those at intermediate- and high-risk. Reti-CVD is a retinal imaging biomarker developed from a deep-learning algorithm that can predict future CVD risk. In this study, we aim to evaluate the performance of Reti-CVD as a non-invasive triage tool to identify individuals with intermediate- and high-risk for CVD who can potentially benefit from early intervention. In this cross-sectional validation study, we confirmed Reti-CVD's ability to identify individuals with intermediate- and high-risk for CVD based on their 10-year CVD risk using three different standard risk assessment tools (PCE, QRISK3, and modified FRS) with more than 80% sensitivity and specificity. Our findings demonstrate Reti-CVD's ability to function as a non-invasive screening tool that can help triage patients according to CVD risk.