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Validation of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from UK Biobank

Authors
 Rachel Marjorie Wei Wen Tseng  ;  Tyler Hyungtaek Rim  ;  Eduard Shantsila  ;  Joseph K Yi  ;  Sungha Park  ;  Sung Soo Kim  ;  Chan Joo Lee  ;  Sahil Thakur  ;  Simon Nusinovici  ;  Qingsheng Peng  ;  Hyeonmin Kim  ;  Geunyoung Lee  ;  Marco Yu  ;  Yih-Chung Tham  ;  Ameet Bakhai  ;  Paul Leeson  ;  Gregory Y H Lip  ;  Tien Yin Wong  ;  Ching-Yu Cheng 
Citation
 BMC MEDICINE, Vol.21(1) : 28, 2023-01 
Journal Title
BMC MEDICINE
Issue Date
2023-01
MeSH
Adult ; Biological Specimen Banks ; Biomarkers ; Cardiovascular Diseases* / epidemiology ; Deep Learning* ; Humans ; Hypertension* / complications ; Middle Aged ; Risk Factors ; United Kingdom / epidemiology
Keywords
Artificial intelligence ; Cardiovascular disease ; Deep learning ; Retinal imaging ; Retinal photograph ; Risk stratification ; Risk stratification system ; UK Biobank
Abstract
Background: Currently in the United Kingdom, cardiovascular disease (CVD) risk assessment is based on the QRISK3 score, in which 10% 10-year CVD risk indicates clinical intervention. However, this benchmark has limited efficacy in clinical practice and the need for a more simple, non-invasive risk stratification tool is necessary. Retinal photography is becoming increasingly acceptable as a non-invasive imaging tool for CVD. Previously, we developed a novel CVD risk stratification system based on retinal photographs predicting future CVD risk. This study aims to further validate our biomarker, Reti-CVD, (1) to detect risk group of ≥ 10% in 10-year CVD risk and (2) enhance risk assessment in individuals with QRISK3 of 7.5-10% (termed as borderline-QRISK3 group) using the UK Biobank.

Methods: Reti-CVD scores were calculated and stratified into three risk groups based on optimized cut-off values from the UK Biobank. We used Cox proportional-hazards models to evaluate the ability of Reti-CVD to predict CVD events in the general population. C-statistics was used to assess the prognostic value of adding Reti-CVD to QRISK3 in borderline-QRISK3 group and three vulnerable subgroups.

Results: Among 48,260 participants with no history of CVD, 6.3% had CVD events during the 11-year follow-up. Reti-CVD was associated with an increased risk of CVD (adjusted hazard ratio [HR] 1.41; 95% confidence interval [CI], 1.30-1.52) with a 13.1% (95% CI, 11.7-14.6%) 10-year CVD risk in Reti-CVD-high-risk group. The 10-year CVD risk of the borderline-QRISK3 group was greater than 10% in Reti-CVD-high-risk group (11.5% in non-statin cohort [n = 45,473], 11.5% in stage 1 hypertension cohort [n = 11,966], and 14.2% in middle-aged cohort [n = 38,941]). C statistics increased by 0.014 (0.010-0.017) in non-statin cohort, 0.013 (0.007-0.019) in stage 1 hypertension cohort, and 0.023 (0.018-0.029) in middle-aged cohort for CVD event prediction after adding Reti-CVD to QRISK3.

Conclusions: Reti-CVD has the potential to identify individuals with ≥ 10% 10-year CVD risk who are likely to benefit from earlier preventative CVD interventions. For borderline-QRISK3 individuals with 10-year CVD risk between 7.5 and 10%, Reti-CVD could be used as a risk enhancer tool to help improve discernment accuracy, especially in adult groups that may be pre-disposed to CVD.
Files in This Item:
T202304035.pdf Download
DOI
10.1186/s12916-022-02684-8
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Ophthalmology (안과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Sung Soo(김성수) ORCID logo https://orcid.org/0000-0002-0574-7993
Park, Sung Ha(박성하) ORCID logo https://orcid.org/0000-0001-5362-478X
Lee, Chan Joo(이찬주) ORCID logo https://orcid.org/0000-0002-8756-409X
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/195588
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