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

AimsWe examine whether inclusion of artificial intelligence (AI)-enabled retinal vasculometry (RV) improves existing risk algorithms for incident stroke, myocardial infarction (MI) and circulatory mortality.MethodsAI-enabled retinal vessel image analysis processed images from 88 052 UK Biobank (UKB)...

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Published in	British journal of ophthalmology Vol. 106; no. 12; pp. 1722 - 1729
Main Authors	Rudnicka, Alicja Regina, Welikala, Roshan, Barman, Sarah, Foster, Paul J, Luben, Robert, Hayat, Shabina, Khaw, Kay-Tee, Whincup, Peter, Strachan, David, Owen, Christopher G
Format	Journal Article
Language	English
Published	BMA House, Tavistock Square, London, WC1H 9JR BMJ Publishing Group Ltd 01.12.2022 BMJ Publishing Group LTD BMJ Publishing Group
Subjects	Accuracy Artificial Intelligence Cardiovascular disease Clinical Science Diagnostic tests/Investigation Epidemiology Female Health risks Heart attacks Humans Imaging Incidence Male Medical diagnosis Middle Aged Mortality Myocardial Infarction - diagnosis Ophthalmology Proportional Hazards Models Prospective Studies Public health Retina Risk Factors Stroke Stroke - diagnosis Stroke - epidemiology Veins & arteries Retina Epidemiology Imaging Public health Diagnostic tests/Investigation
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ISSN	0007-1161 1468-2079 1468-2079
DOI	10.1136/bjo-2022-321842

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Abstract	AimsWe examine whether inclusion of artificial intelligence (AI)-enabled retinal vasculometry (RV) improves existing risk algorithms for incident stroke, myocardial infarction (MI) and circulatory mortality.MethodsAI-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).ResultsUKB 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.ConclusionRV 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.
AbstractList	AimsWe examine whether inclusion of artificial intelligence (AI)-enabled retinal vasculometry (RV) improves existing risk algorithms for incident stroke, myocardial infarction (MI) and circulatory mortality.MethodsAI-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).ResultsUKB 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.ConclusionRV 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. 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.AIMSWe examine whether inclusion of artificial intelligence (AI)-enabled retinal vasculometry (RV) improves existing risk algorithms for incident stroke, myocardial infarction (MI) and circulatory mortality.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).METHODSAI-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).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.RESULTSUKB 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.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.CONCLUSIONRV 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. 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. 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 R 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). 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 R 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. 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.
Author	Barman, Sarah Whincup, Peter Strachan, David Welikala, Roshan Rudnicka, Alicja Regina Hayat, Shabina Khaw, Kay-Tee Owen, Christopher G Foster, Paul J Luben, Robert
AuthorAffiliation	5 Department of Psychiatry, Cambridge Public Health , University of Cambridge School of Clinical Medicine , Cambridge , UK 2 Faculty of Science, Engineering and Computing , Kingston University , Kingston-Upon-Thames , UK 3 NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology , University College London , London , UK 4 MRC Epidemiology Unit , Cambridge University , Cambridge , UK 1 Population Health Research Institute , St George's University of London , London , UK
AuthorAffiliation_xml	– name: 4 MRC Epidemiology Unit , Cambridge University , Cambridge , UK – name: 2 Faculty of Science, Engineering and Computing , Kingston University , Kingston-Upon-Thames , UK – name: 3 NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology , University College London , London , UK – name: 5 Department of Psychiatry, Cambridge Public Health , University of Cambridge School of Clinical Medicine , Cambridge , UK – name: 1 Population Health Research Institute , St George's University of London , London , UK
Author_xml	– sequence: 1 givenname: Alicja Regina orcidid: 0000-0003-0369-8574 surname: Rudnicka fullname: Rudnicka, Alicja Regina email: arudnick@sgul.ac.uk organization: Population Health Research Institute, St George's University of London, London, UK – sequence: 2 givenname: Roshan surname: Welikala fullname: Welikala, Roshan organization: Faculty of Science, Engineering and Computing, Kingston University, Kingston-Upon-Thames, UK – sequence: 3 givenname: Sarah surname: Barman fullname: Barman, Sarah organization: Faculty of Science, Engineering and Computing, Kingston University, Kingston-Upon-Thames, UK – sequence: 4 givenname: Paul J orcidid: 0000-0002-4755-177X surname: Foster fullname: Foster, Paul J organization: NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, University College London, London, UK – sequence: 5 givenname: Robert orcidid: 0000-0002-5088-6343 surname: Luben fullname: Luben, Robert organization: MRC Epidemiology Unit, Cambridge University, Cambridge, UK – sequence: 6 givenname: Shabina orcidid: 0000-0001-9068-8723 surname: Hayat fullname: Hayat, Shabina organization: Department of Psychiatry, Cambridge Public Health, University of Cambridge School of Clinical Medicine, Cambridge, UK – sequence: 7 givenname: Kay-Tee surname: Khaw fullname: Khaw, Kay-Tee organization: MRC Epidemiology Unit, Cambridge University, Cambridge, UK – sequence: 8 givenname: Peter surname: Whincup fullname: Whincup, Peter organization: Population Health Research Institute, St George's University of London, London, UK – sequence: 9 givenname: David surname: Strachan fullname: Strachan, David organization: Population Health Research Institute, St George's University of London, London, UK – sequence: 10 givenname: Christopher G orcidid: 0000-0003-1135-5977 surname: Owen fullname: Owen, Christopher G organization: Population Health Research Institute, St George's University of London, London, UK
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Copyright	Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ. 2022 Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/ . Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ. 2022
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Snippet	AimsWe examine whether inclusion of artificial intelligence (AI)-enabled retinal vasculometry (RV) improves existing risk algorithms for incident stroke,... We examine whether inclusion of artificial intelligence (AI)-enabled retinal vasculometry (RV) improves existing risk algorithms for incident stroke,...
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StartPage	1722
SubjectTerms	Accuracy Artificial Intelligence Cardiovascular disease Clinical Science Diagnostic tests/Investigation Epidemiology Female Health risks Heart attacks Humans Imaging Incidence Male Medical diagnosis Middle Aged Mortality Myocardial Infarction - diagnosis Ophthalmology Proportional Hazards Models Prospective Studies Public health Retina Risk Factors Stroke Stroke - diagnosis Stroke - epidemiology Veins & arteries
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Title	Artificial intelligence-enabled retinal vasculometry for prediction of circulatory mortality, myocardial infarction and stroke
URI	https://bjo.bmj.com/content/106/12/1722.full https://www.ncbi.nlm.nih.gov/pubmed/36195457 https://www.proquest.com/docview/2723921640 https://www.proquest.com/docview/2739546305 https://www.proquest.com/docview/2721637726 https://pubmed.ncbi.nlm.nih.gov/PMC9685715
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