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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
dc.contributor.author김성수-
dc.contributor.author박성하-
dc.contributor.author이찬주-
dc.date.accessioned2023-07-25T04:56:52Z-
dc.date.available2023-07-25T04:56:52Z-
dc.date.issued2023-01-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/195588-
dc.description.abstractBackground: 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.statementOfResponsibilityopen-
dc.languageEnglish-
dc.publisherBioMed Central-
dc.relation.isPartOfBMC MEDICINE-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.subject.MESHAdult-
dc.subject.MESHBiological Specimen Banks-
dc.subject.MESHBiomarkers-
dc.subject.MESHCardiovascular Diseases* / epidemiology-
dc.subject.MESHDeep Learning*-
dc.subject.MESHHumans-
dc.subject.MESHHypertension* / complications-
dc.subject.MESHMiddle Aged-
dc.subject.MESHRisk Factors-
dc.subject.MESHUnited Kingdom / epidemiology-
dc.titleValidation of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from UK Biobank-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Ophthalmology (안과학교실)-
dc.contributor.googleauthorRachel Marjorie Wei Wen Tseng-
dc.contributor.googleauthorTyler Hyungtaek Rim-
dc.contributor.googleauthorEduard Shantsila-
dc.contributor.googleauthorJoseph K Yi-
dc.contributor.googleauthorSungha Park-
dc.contributor.googleauthorSung Soo Kim-
dc.contributor.googleauthorChan Joo Lee-
dc.contributor.googleauthorSahil Thakur-
dc.contributor.googleauthorSimon Nusinovici-
dc.contributor.googleauthorQingsheng Peng-
dc.contributor.googleauthorHyeonmin Kim-
dc.contributor.googleauthorGeunyoung Lee-
dc.contributor.googleauthorMarco Yu-
dc.contributor.googleauthorYih-Chung Tham-
dc.contributor.googleauthorAmeet Bakhai-
dc.contributor.googleauthorPaul Leeson-
dc.contributor.googleauthorGregory Y H Lip-
dc.contributor.googleauthorTien Yin Wong-
dc.contributor.googleauthorChing-Yu Cheng-
dc.identifier.doi10.1186/s12916-022-02684-8-
dc.contributor.localIdA00571-
dc.contributor.localIdA01512-
dc.contributor.localIdA03238-
dc.relation.journalcodeJ00364-
dc.identifier.eissn1741-7015-
dc.identifier.pmid36691041-
dc.subject.keywordArtificial intelligence-
dc.subject.keywordCardiovascular disease-
dc.subject.keywordDeep learning-
dc.subject.keywordRetinal imaging-
dc.subject.keywordRetinal photograph-
dc.subject.keywordRisk stratification-
dc.subject.keywordRisk stratification system-
dc.subject.keywordUK Biobank-
dc.contributor.alternativeNameKim, Sung Soo-
dc.contributor.affiliatedAuthor김성수-
dc.contributor.affiliatedAuthor박성하-
dc.contributor.affiliatedAuthor이찬주-
dc.citation.volume21-
dc.citation.number1-
dc.citation.startPage28-
dc.identifier.bibliographicCitationBMC MEDICINE, Vol.21(1) : 28, 2023-01-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Ophthalmology (안과학교실) > 1. Journal Papers

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