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

被引:23
作者
Tseng, Rachel Marjorie Wei Wen [1 ,2 ]
Rim, Tyler Hyungtaek [1 ,3 ,4 ]
Shantsila, Eduard [5 ]
Yi, Joseph K. [6 ]
Park, Sungha [7 ]
Kim, Sung Soo [8 ]
Lee, Chan Joo [7 ]
Thakur, Sahil [1 ]
Nusinovici, Simon [1 ,3 ]
Peng, Qingsheng [1 ,9 ]
Kim, Hyeonmin [4 ]
Lee, Geunyoung [4 ]
Yu, Marco [1 ,3 ]
Tham, Yih-Chung [1 ,3 ,10 ,11 ]
Bakhai, Ameet [12 ,13 ]
Leeson, Paul [14 ]
Lip, Gregory Y. H. [15 ,16 ,17 ]
Wong, Tien Yin [1 ,18 ]
Cheng, Ching-Yu [1 ,3 ,10 ,11 ]
机构
[1] Singapore Natl Eye Ctr, Singapore Eye Res Inst, Singapore, Singapore
[2] Duke NUS Med Sch, Singapore, Singapore
[3] Duke NUS Med Sch, Ophthalmol & Visual Sci Acad Clin Program Eye ACP, Singapore, Singapore
[4] Mediwhale Inc, Seoul, South Korea
[5] Univ Liverpool, Dept Primary Care & Mental Hlth, Liverpool, Merseyside, England
[6] Albert Einstein Coll Med, New York, NY USA
[7] Yonsei Univ, Coll Med, Severance Cardiovasc Hosp, Div Cardiol, Seoul, South Korea
[8] Yonsei Univ, Coll Med, Severance Eye Hosp, Div Retina, Seoul, South Korea
[9] Duke NUS Med Sch, Clin & Translat Sci Program, Singapore, Singapore
[10] Natl Univ Singapore, Ctr Innovat & Precis Eye Hlth, Yong Loo Lin Sch Med, Singapore, Singapore
[11] Natl Univ Singapore, Yong Loo Lin Sch Med, Dept Ophthalmol, Singapore, Singapore
[12] Royal Free Hosp London NHS Fdn Trust, London, England
[13] Barnet Gen Hosp, Cardiol Dept, Thames House, Enfield, Middx, England
[14] Univ Oxford, RDM Div Cardiovasc Med, Cardiovasc Clin Res Facil, Oxford, England
[15] Univ Liverpool, Liverpool Ctr Cardiovasc Sci, Liverpool, Merseyside, England
[16] Liverpool John Moores Univ, Liverpool, Merseyside, England
[17] Liverpool Heart & Chest Hosp, Liverpool, Merseyside, England
[18] Tsinghua Univ, Tsinghua Med, Beijing, Peoples R China
关键词
Artificial intelligence; Cardiovascular disease; Deep learning; Retinal imaging; Retinal photograph; Risk stratification; Risk stratification system; UK Biobank; RISK; PHOTOGRAPHS;
D O I
10.1186/s12916-022-02684-8
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
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.
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