25doc003 10.3205/25doc003 urn:nbn:de:0183-25doc0030 Meeting Abstract Chan Chan Yarn Kit YK

Singapore General Hospital, Singapore, Singapur

author Peng Peng Qingsheng Q

Singapore National Eye Centre, Singapore, Singapur

author Cheng Cheng Ching-Yu CY

Yong Loo Lin School of Medicine, Singapore, Singapur

author German Medical Science GMS Publishing House

Düsseldorf

610 20250513 engl This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). M0613 003 37. Internationaler Kongress der Deutschen Ophthalmochirurgie (DOC) Allgemeine Ophthalmologie Nürnberg 20250515 20250517 FP 1.3 TextPurpose: Early detection and precise risk stratification of cardiovascular diseases (CVD) are critical in reducing morbidity, mortality, and healthcare costs. We utilized deep learning algorithms (DLAs) to identify biomarkers for early-stage CVD detection through retinal imaging.Methods: We utilized RetiAGE, an advanced DLA that estimated biological age from retinal photographs to predict CVD risk. We utilized data from 57,297 patients from the UK Biobank and excluded images with poor quality and participants with CVD at baseline. RetiAGE scores were generated for the remaining samples. Statistical analysis included multivariable Cox proportional hazards (CoxPH) regression, adjusted for age and sex, to evaluate the utility of RetiAGE scores in predicting CVD outcomes. Subgroup analyses were performed to risk stratify CVD subtypes using RetiAGE quartiles.Results: RetiAGE scores correlated with CVD events (z score = 3.11, p = 0.002). Our novel findings are that RetiAGE predicts 37 of 176 CVD subtypes. RetiAGE scores were highly correlated with risk for nonrheumatic aortic (valve) stenosis (HR 11.86, p = 0.01), cerebral infarction due to embolism of cerebral arteries (HR 6.86, p = 0.02), unstable angina (HR 5.73, p = 0.002), and essential (primary) hypertension (HR 3.01, p < 0.001). HR ranged from 2 to 12 in all subtypes with statistical significance.We performed subgroup analyses within each CVD subtype after stratifying the entire cohort by quartiles based on RetiAge score, with higher quartiles associated with greater risk. Quartiles 2, 3, and 4 were compared to quartile 1 as baseline. In essential (primary) hypertension, the 2nd (HR 1.80, p < 0.0001), 3rd (HR 2.37, p < 0.0001), and 4th (HR 3.44, p < 0.0001) quartiles were significantly correlated with CVD. This trend of hazard ratios of CVD events increasing from quartiles 2 to 4 was also present in most other subtypes, showing that RetiAge can capture subclinical changes in the retina in early-stage CVD.Conclusion: RetiAGE primarily predicts CVD subtypes with hemodynamic instability, and demonstrated good predictive accuracy across diverse CVD subtypes, paving the way for early risk stratification. 0 0 0 0