Our previous work led to developing a deep learning algorithm for retinal images, Reti-CVD, which effectively predicted cardiovascular disease (CVD) events in individuals without CVD history, leveraging coronary artery calcium (CAC) scores for algorithm training.\u00a0<\/p>\n\n\n\n
This study aims to assess the capability of deep learning-assisted retinal imaging to predict CVD events among prediabetic and diabetic patients using the data from the UK Biobank.\u00a0<\/p>\n\n\n\n
Our study included prediabetic and diabetic patients from the UK Biobank. Reti-CVD scores were calculated and categorized into three risk groups \u2013 low (n=550), moderate (n=276), and high (n=275), based on the 50th and 75th percentiles, following a 2:1:1 ratio. To assess the Reti-CVD\u2019s ability in predicting fatal and non-fatal CVD events, we performed a survival analysis on the longitudinal data from the UK Biobank using Cox proportional-hazards models and hazard ratios (HRs).\u00a0<\/p>\n\n\n\n
Among the 1101 prediabetic or diabetic patients at the onset, 138 (12.5%) experienced CVD events. According to Reti-CVD scores, these events were found as 8.2% (45\/550), 15.2% (42\/276), and 18.5% (51\/275) in the low, moderate, and high-risk groups over a median follow-up period of 11 years, respectively. After adjusting for factors such as age, gender, hypertensive medication use, statin use, and smoking history, a significant association was observed between the Reti-CVD and the incidence of CVD events (HR=1.57, 95% CI, 1.00-2.47 for the moderate-risk group; HR=1.88, 95% CI, 1.19-2.98 for the high-risk group compared to the low-risk group). An increasing HR trend of 1.36 (95% CI, 1.09-1.70) was observed across risk groups in the prediction of CVD events.\u00a0<\/p>\n\n\n\n
The Reti-CVD offers a valuable tool for risk stratification among prediabetic and diabetic patients, indicating its potential in managing these high-risk groups.<\/p>\n","protected":false},"template":"","publication_type_categories":[12],"publication_categories":[16],"class_list":["post-1044","publication","type-publication","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/mediwhale.stage.studio-jt.co.kr\/ko\/wp-json\/wp\/v2\/publication\/1044","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mediwhale.stage.studio-jt.co.kr\/ko\/wp-json\/wp\/v2\/publication"}],"about":[{"href":"https:\/\/mediwhale.stage.studio-jt.co.kr\/ko\/wp-json\/wp\/v2\/types\/publication"}],"version-history":[{"count":1,"href":"https:\/\/mediwhale.stage.studio-jt.co.kr\/ko\/wp-json\/wp\/v2\/publication\/1044\/revisions"}],"predecessor-version":[{"id":1045,"href":"https:\/\/mediwhale.stage.studio-jt.co.kr\/ko\/wp-json\/wp\/v2\/publication\/1044\/revisions\/1045"}],"wp:attachment":[{"href":"https:\/\/mediwhale.stage.studio-jt.co.kr\/ko\/wp-json\/wp\/v2\/media?parent=1044"}],"wp:term":[{"taxonomy":"publication_type_categories","embeddable":true,"href":"https:\/\/mediwhale.stage.studio-jt.co.kr\/ko\/wp-json\/wp\/v2\/publication_type_categories?post=1044"},{"taxonomy":"publication_categories","embeddable":true,"href":"https:\/\/mediwhale.stage.studio-jt.co.kr\/ko\/wp-json\/wp\/v2\/publication_categories?post=1044"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}