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Validation of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from UK Biobank
DC Field | Value | Language |
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dc.contributor.author | 김성수 | - |
dc.contributor.author | 박성하 | - |
dc.contributor.author | 이찬주 | - |
dc.date.accessioned | 2023-07-25T04:56:52Z | - |
dc.date.available | 2023-07-25T04:56:52Z | - |
dc.date.issued | 2023-01 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/195588 | - |
dc.description.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. | - |
dc.description.statementOfResponsibility | open | - |
dc.language | English | - |
dc.publisher | BioMed Central | - |
dc.relation.isPartOf | BMC MEDICINE | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Adult | - |
dc.subject.MESH | Biological Specimen Banks | - |
dc.subject.MESH | Biomarkers | - |
dc.subject.MESH | Cardiovascular Diseases* / epidemiology | - |
dc.subject.MESH | Deep Learning* | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Hypertension* / complications | - |
dc.subject.MESH | Middle Aged | - |
dc.subject.MESH | Risk Factors | - |
dc.subject.MESH | United Kingdom / epidemiology | - |
dc.title | Validation of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from UK Biobank | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Ophthalmology (안과학교실) | - |
dc.contributor.googleauthor | Rachel Marjorie Wei Wen Tseng | - |
dc.contributor.googleauthor | Tyler Hyungtaek Rim | - |
dc.contributor.googleauthor | Eduard Shantsila | - |
dc.contributor.googleauthor | Joseph K Yi | - |
dc.contributor.googleauthor | Sungha Park | - |
dc.contributor.googleauthor | Sung Soo Kim | - |
dc.contributor.googleauthor | Chan Joo Lee | - |
dc.contributor.googleauthor | Sahil Thakur | - |
dc.contributor.googleauthor | Simon Nusinovici | - |
dc.contributor.googleauthor | Qingsheng Peng | - |
dc.contributor.googleauthor | Hyeonmin Kim | - |
dc.contributor.googleauthor | Geunyoung Lee | - |
dc.contributor.googleauthor | Marco Yu | - |
dc.contributor.googleauthor | Yih-Chung Tham | - |
dc.contributor.googleauthor | Ameet Bakhai | - |
dc.contributor.googleauthor | Paul Leeson | - |
dc.contributor.googleauthor | Gregory Y H Lip | - |
dc.contributor.googleauthor | Tien Yin Wong | - |
dc.contributor.googleauthor | Ching-Yu Cheng | - |
dc.identifier.doi | 10.1186/s12916-022-02684-8 | - |
dc.contributor.localId | A00571 | - |
dc.contributor.localId | A01512 | - |
dc.contributor.localId | A03238 | - |
dc.relation.journalcode | J00364 | - |
dc.identifier.eissn | 1741-7015 | - |
dc.identifier.pmid | 36691041 | - |
dc.subject.keyword | Artificial intelligence | - |
dc.subject.keyword | Cardiovascular disease | - |
dc.subject.keyword | Deep learning | - |
dc.subject.keyword | Retinal imaging | - |
dc.subject.keyword | Retinal photograph | - |
dc.subject.keyword | Risk stratification | - |
dc.subject.keyword | Risk stratification system | - |
dc.subject.keyword | UK Biobank | - |
dc.contributor.alternativeName | Kim, Sung Soo | - |
dc.contributor.affiliatedAuthor | 김성수 | - |
dc.contributor.affiliatedAuthor | 박성하 | - |
dc.contributor.affiliatedAuthor | 이찬주 | - |
dc.citation.volume | 21 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 28 | - |
dc.identifier.bibliographicCitation | BMC MEDICINE, Vol.21(1) : 28, 2023-01 | - |
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