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

Rudnicka, Alicja R; Welikala, Roshan A; Barman, Sarah A; Foster, Paul J; Luben, Robert; Hayat, Shabina A; Khaw, Kay-Tee; ... Owen, Christopher G; + view all (2022) Artificial intelligence-enabled retinal vasculometry for prediction of circulatory mortality, myocardial infarction and stroke. British Journal of Ophthalmology 10.1136/bjo-2022-321842. (In press). Green open access

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Abstract

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.

Type: Article
Title: Artificial intelligence-enabled retinal vasculometry for prediction of circulatory mortality, myocardial infarction and stroke
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1136/bjo-2022-321842
Publisher version: https://doi.org/10.1136/bjo-2022-321842
Language: English
Additional information: © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.
Keywords: Science & Technology, Life Sciences & Biomedicine, Ophthalmology, Imaging, Retina, Diagnostic tests, Investigation, Epidemiology, Public health, CORONARY-HEART-DISEASE, CARDIOVASCULAR-DISEASE, ATHEROSCLEROSIS RISK, VESSEL CALIBER, WOMEN, MODELS, ASSOCIATION, TORTUOSITY, DIAMETER, SMOKING
UCL classification: UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Ophthalmology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10157393
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