임형택 (Singapore National Eye Centre, 메디웨일) | 한빛사논문 > 한빛사

한빛사논문

조회710

Singapore National Eye Centre, 메디웨일

CV File 636 kb

BMC Med., 2023 Jan 24;21(1):28 | https://doi.org/10.1186/s12916-022-02684-8 Validation of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from UK Biobank

Rachel Marjorie Wei Wen Tseng1,2†, Tyler Hyungtaek Rim1,3,4*† , Eduard Shantsila5, Joseph K. Yi6, Sungha Park7, Sung Soo Kim8, Chan Joo Lee7, Sahil Thakur1, Simon Nusinovici1,3, Qingsheng Peng1,9, Hyeonmin Kim4, Geunyoung Lee4, Marco Yu1,3, Yih‑Chung Tham1,3,10,11, Ameet Bakhai12,13, Paul Leeson14, Gregory Y.H. Lip15, Tien Yin Wong1,16 and Ching‑Yu Cheng1,3,10,11

1Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
2Duke-NUS Medical School, Singapore, Singapore.
3Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore.
4Mediwhale Inc., Seoul, South Korea.
5Department of Primary Care and Mental Health, University of Liverpool, Liverpool, UK.
6Albert Einstein College of Medicine, New York, NY, USA.
7Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, South Korea.
8Division of Retina, Severance Eye Hospital, Yonsei University College of Medicine, Seoul, South Korea.
9Clinical and Translational Sciences Program, Duke-NUS Medical School, Singapore, Singapore.
10Center for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
11 Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
12Royal Free Hospital London NHS Foundation Trust, London, UK.
13Cardiology Department, Barnet General Hospital, Thames House, Enfeld, UK.
14Cardiovascular Clinical Research Facility, RDM Division of Cardiovascular Medicine, University of Oxford, Oxford, UK.
15Liverpool 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. 16Tsinghua Medicine, Tsinghua University, Beijing, China.

†Rachel Marjorie Wei Wen Tseng and Tyler Hyungtaek Rim contributed equally to this work.
*Correspondence: Tyler Hyungtaek Rim

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.

논문정보

형식Research article
게재일2023년 01월 (BRIC 등록일 2023-03-10)
연구진국내(교신)+국외 연구진
분야 바이오·의료융합 > 의료기기

댓글 작성 로그인

CV 열람하기

연락처 보기