{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Tseng RMWW"],"funding":["Medical Research Council","Agency for Science, Technology and Research"],"pagination":["28"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9872417"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["21(1)"],"pubmed_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.<"],"journal":["BMC medicine"],"pubmed_title":["Validation of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from UK Biobank."],"pmcid":["PMC9872417"],"funding_grant_id":["MC_PC_17228","Grant No. H20c6a0031","MR/W003686/1"],"pubmed_authors":["Bakhai A","Yu M","Kim SS","Thakur S","Kim H","Tham YC","Leeson P","Yi JK","Lip GYH","Park S","Lee G","Cheng CY","Wong TY","Lee CJ","Nusinovici S","Shantsila E","Rim TH","Peng Q","Tseng RMWW"],"additional_accession":[]},"is_claimable":false,"name":"Validation of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from UK Biobank.","description":"<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.<","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Jan","modification":"2026-03-31T11:00:13.074Z","creation":"2024-11-14T06:13:01.486Z"},"accession":"S-EPMC9872417","cross_references":{"pubmed":["36691041"],"doi":["10.1186/s12916-022-02684-8"]}}