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

BMC Medicine

Research Article

Joseph K. Yi¹  Paul Leeson²  Eduard Shantsila³  Sungha Park⁴  Chan Joo Lee⁴  Sung Soo Kim⁵  Gregory Y.H. Lip⁶  Geunyoung Lee⁷  Hyeonmin Kim⁷  Ameet Bakhai⁸  Sahil Thakur⁹  Qingsheng Peng^1,10  Rachel Marjorie Wei Wen Tseng^1,11  Marco Yu^1,12  Simon Nusinovici^1,12  Ching-Yu Cheng^1,13  Yih-Chung Tham^1,13  Tyler Hyungtaek Rim^1,14  Tien Yin Wong^1,15 

[1] Albert Einstein College of Medicine, New York, NY, USA;Cardiovascular Clinical Research Facility, RDM Division of Cardiovascular Medicine, University of Oxford, Oxford, UK;Department of Primary Care and Mental Health, University of Liverpool, Liverpool, UK;Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, South Korea;Division of Retina, Severance Eye Hospital, Yonsei University College of Medicine, Seoul, South Korea;Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom; and Department of Clinical Medicine, Aalborg University, Aalborg, Denmark;Mediwhale Inc., Seoul, South Korea;Royal Free Hospital London NHS Foundation Trust, London, UK;Cardiology Department, Barnet General Hospital, Thames House, Enfield, UK;Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore;Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore;Clinical and Translational Sciences Program, Duke-NUS Medical School, Singapore, Singapore;Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore;Duke-NUS Medical School, Singapore, Singapore;Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore;Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore;Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore;Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore;Center for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore;Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore;Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore;Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore;Mediwhale Inc., Seoul, South Korea;Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore;Tsinghua Medicine, Tsinghua University, Beijing, China;

关键词: Artificial intelligence;    Cardiovascular disease;    Deep learning;    Retinal imaging;    Retinal photograph;    Risk stratification;    Risk stratification system;    UK Biobank;   

DOI  :  10.1186/s12916-022-02684-8

 received in 2022-07-12, accepted in 2022-11-28,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundCurrently 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.MethodsReti-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.ResultsAmong 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.ConclusionsReti-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.

【 授权许可】

CC BY   
© The Author(s) 2023

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