Artificial intelligence enabled retinal vasculometry for prediction of circulatory mortality, myocardial infarction and stroke

Aims We examine whether inclusion of Artificial Intelligence (AI)-enabled retinal vasculometry (RV) improves existing risk algorithms for incident stroke, myocardial infarction (MI) and circulatory mortality. Methods AI-enabled retinal vessel image analysis processed images from 88,052 UK Biobank (UKB) participants (aged 40-69 years at image capture) and 7,411 EPIC-Norfolk participants (aged 48-92). Retinal arteriolar and venular width, tortuosity and area were extracted. Prediction models were developed in UKB using multivariable Cox proportional hazards regression for circulatory mortality, incident stroke and MI, and externally validated in EPIC-Norfolk. Model performance was assessed using optimism adjusted calibration, C- and R 2 statistics. Performance of Framingham risk scores (FRS) for incident stroke and incident MI, with addition of RV to FRS, were compared with a simpler model based on RV, age, smoking status and medical history (antihypertensive/cholesterol lowering medication, diabetes, prevalent stroke/MI). Results UKB prognostic models were developed on 65,144 participants (mean age 56.8; median follow-up 7.7 years) and validated in 5,862 EPIC-Norfolk participants (67.6, 9.1 years respectively). Prediction models for circulatory mortality in men and women had optimism adjusted C- and R 2 statistics between 0.75-0.77 and 0.33-0.44 respectively. For incident stroke and MI, addition of RV to FRS did not improve model performance in either cohort. However, the simpler RV model performed equally or better than FRS. Conclusion RV offers an alternative predictive biomarker to traditional risk-scores for vascular health, without the need for blood sampling or blood pressure measurement. Further work is needed to examine RV in population screening to triage individuals at high-risk. (250 words) HIGHLIGHTS What is already known on this topic Population screening for MI and stroke using risk prediction tools exist but have limited uptake; risk scores for circulator mortality do not exist. What this study adds Risk models developed in UK Biobank (validated in EPIC-Norfolk) using Artificial Intelligence (AI)-enabled retinal vasculometry (RV), age, history of cardiovascular disease, use of hypertensive medication and smoking yielded high predictive test performance for circulatory mortality. Risk scores for MI and stroke performed similarly to established risk scores. How this study might affect research, practice or policy AI-enabled RV extraction offers a non-invasive prognostic biomarker of vascular health that does not require blood sampling or blood pressure measurement, and potentially has greater community reach to identify individuals at medium-high risk requiring further clinical assessment. SYNOPSIS/PRECIS Risk models developed in UK Biobank (validated in EPIC-Norfolk) using Artificial Intelligence enabled retinal vasculometry indices, age, history of cardiovascular disease, use of hypertensive medication and smoking yielded high predictive test performance for circulatory mortality. Risk scores for MI and stroke performed similarly to established risk scores.