June 2022
Volume 63, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2022
Deep-learning measurement of retinal vessel caliber predicts incident myocardial infarction
Author Affiliations & Notes
  • Yiu Lun Wong
    The Chinese University of Hong Kong Faculty of Medicine, Hong Kong, Hong Kong
  • Marco Yu
    Singapore Eye Research Institute, Singapore, Singapore
  • Crystal Chong
    Singapore Eye Research Institute, Singapore, Singapore
  • Tien Yin Wong
    Singapore Eye Research Institute, Singapore, Singapore
    Ophthalmology and Visual Sciences Academic Clinical Programme, Duke-NUS Medical School, Singapore, Singapore
  • Ching Yu Cheng
    Singapore Eye Research Institute, Singapore, Singapore
    Ophthalmology and Visual Sciences Academic Clinical Programme, Duke-NUS Medical School, Singapore, Singapore
  • Carol Yim-lui Cheung
    The Chinese University of Hong Kong Faculty of Medicine, Hong Kong, Hong Kong
    Singapore Eye Research Institute, Singapore, Singapore
  • Footnotes
    Commercial Relationships   Yiu Lun Wong None; Marco Yu None; Crystal Chong None; Tien Yin Wong None; Ching Yu Cheng None; Carol Cheung None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2022, Vol.63, 177 – F0024. doi:
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    • Get Citation

      Yiu Lun Wong, Marco Yu, Crystal Chong, Tien Yin Wong, Ching Yu Cheng, Carol Yim-lui Cheung; Deep-learning measurement of retinal vessel caliber predicts incident myocardial infarction. Invest. Ophthalmol. Vis. Sci. 2022;63(7):177 – F0024.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Retinal arteriolar and venular calibers have been proposed to be surrogate measures of systemic microvascular health. We evaluated the associations of deep-learning based retinal vessel caliber measurements with incidence of fatal and non-fatal myocardial infarction (MI) using data from the UK Biobank.

Methods : Prevalent and incident MI was defined with an algorithm developed by UK Biobank, combining information from self-reported medical conditions, operations and medications, hospital admissions and death registries before and after the code date of baseline assessment respectively. Fully automated measurements of central retinal artery and vein equivalents (CRAE/CRVE) from retinal fundus photographs in the area 0.5-2.0 disc diameters away from the optic disc were estimated with a recently validated deep-learning system (SIVA-DLS). Hazard ratios (HR) and area-under-the-curve (AUC) were calculated for evaluating the association and improvement in discriminative performance between CRAE/CRVE with incident MI.

Results : After excluding individuals with prevalent MI and ungradable retinal photographs, 34,841 subjects at risk of incident MI were included in the final analysis. Baseline assessment was conducted from 2009 to 2013, with subjects at risk of MI followed up to March 2020. A total of 375 subjects developed incident MI. Persons with narrower CRAE were more likely to have incident MI (HR 1.67 [95% CI: 1.19-2.36], comparing 1st to 4th quartile, and HR 0.86 [95% CI: 0.75-0.98], per SD increase in CRAE), adjusting for age and gender. When further adjusted for total cholesterol, high-density lipoprotein cholesterol, systolic blood pressure, and other risk factors, the association between CRAE and incident MI persisted (HR 1.64 [95% CI: 1.16-2.32], 1st vs. 4th quartile). CRVE was associated when adjusted for age and gender (HR 1.51 [95% CI: 1.06-2.13]) but not in multivariable models. AUC increased significantly from 0.718 to 0.724 (adjusted for age and gender, p = 0.030) when CRAE/CRVE is included in the models.

Conclusions : Narrowed retinal arteriolar caliber, reflecting systemic microvascular damage, was associated with incident MI. Deep-learning based retinal vessel caliber measurements from retinal photographs may be a useful biomarker of cardiovascular disease in clinical and epidemiological studies.

This abstract was presented at the 2022 ARVO Annual Meeting, held in Denver, CO, May 1-4, 2022, and virtually.

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