PT - JOURNAL ARTICLE AU - Alicja Regina Rudnicka AU - Roshan Welikala AU - Sarah Barman AU - Paul J Foster AU - Robert Luben AU - Shabina Hayat AU - Kay-Tee Khaw AU - Peter Whincup AU - David Strachan AU - Christopher G Owen TI - Artificial intelligence-enabled retinal vasculometry for prediction of circulatory mortality, myocardial infarction and stroke AID - 10.1136/bjo-2022-321842 DP - 2022 Dec 01 TA - British Journal of Ophthalmology PG - 1722--1729 VI - 106 IP - 12 4099 - http://bjo.bmj.com/content/106/12/1722.short 4100 - http://bjo.bmj.com/content/106/12/1722.full SO - Br J Ophthalmol2022 Dec 01; 106 AB - 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 7411 European Prospective Investigation into Cancer (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-statistics and R2 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 5862 EPIC-Norfolk participants (67.6, 9.1 years, respectively). Prediction models for circulatory mortality in men and women had optimism adjusted C-statistics and R2 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.Data may be obtained from a third party and are not publicly available. The data supporting the results reported here are available through the UK Biobank https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access.