Tseng, Rachel Marjorie Wei Wen, Rim, Tyler Hyungtaek, Shantsila, Eduard 
ORCID: 0000-0002-2429-6980, Yi, Joseph K, Park, Sungha, Kim, Sung Soo, Lee, Chan Joo, Thakur, Sahil, Nusinovici, Simon, Peng, Qingsheng et al (show 9 more authors)
(2023)
Validation of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from UK Biobank.
, England.
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Validation of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease data from UK Bi.pdf - Published version Download (860kB) | Preview |
Abstract
<h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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.
| Item Type: | Conference or Workshop Item (Unspecified) |
|---|---|
| Uncontrolled Keywords: | Artificial intelligence, Cardiovascular disease, Deep learning, Retinal imaging, Retinal photograph, Risk stratification, Risk stratification system, UK Biobank |
| Divisions: | Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Population Health |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 04 Apr 2023 14:39 |
| Last Modified: | 04 Apr 2023 14:40 |
| DOI: | 10.1186/s12916-022-02684-8 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3169427 |
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