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During the congress, E-Posters will be accessible to all participants on the congress website 24/7, as well as in the E-poster stations in the congress center.
Preparing your E-Poster
Please review the E-Poster format requirements carefully when preparing your E-Poster. Should your E-Poster not meet the mentioned requirements, it may not be displayed as described above.
E-Poster Submission Deadline
Please prepare and upload your E-Poster no later than March 14, 2026 11.59PM CET. After this date, you will no longer be able to prepare and upload your E-poster and it will not be displayed and accessible on the congress website.
Please follow the instructions below to input your abstract title.
Abstract titles should be brief and reflect the content of the abstract.
Kidney transplant recipients have a markedly higher risk of cardiovascular disease (CVD) due to both general and transplant-specific factors. Conventional risk prediction models, such as the Framingham Risk Score (FRS), Pooled Cohort Equations (PCE), and Systematic Coronary Risk Estimation 2 (SCORE2), were developed for the general population, whereas the recently introduced American Heart Association’s predicting risk of cardiovascular disease events (PREVENT) equation incorporates kidney function and broader cardio-renal-metabolic parameters. Using data from the Korean Organ Transplant Registry, this study evaluated the predictive performance and clinical utility of the PREVENT equation for CVD risk stratification in Korean kidney transplant recipients, extending beyond kidney function–based assessment.
Baseline demographic and clinical data from the Korean Organ Transplant Registry (KOTRY) were collected 6 months post-transplant to estimate 10-year cardiovascular risk using the FRS, PCE, SCORE2, and PREVENT models. The PREVENT-CVD model, which incorporates estimated glomerular filtration rate (eGFR), was applied according to its original specifications. The primary outcome was incident CVD, and the secondary outcome was all-cause mortality.
A total of 6,351 kidney transplant recipients (mean age, 49.7 years; 59.7% male) were analyzed over a mean follow-up period of approximately 40 months, with a mean CVD risk score of 26.83 ± 10.91. CVD risk increased progressively with higher PREVENT-CVD scores, showing a sharp rise beyond 20. Older age and glucose-lowering drug use were independent predictors of CVD, while lower HDL and BMI were associated with higher mortality. For each 1-unit increase in the PREVENT-CVD score, the risk of CVD increased by 7% (HR, 1.07; 95% CI, 1.05–1.09; P < 0.001), and the risk of mortality increased by 8% (HR, 1.08; 95% CI, 1.07–1.10; P < 0.001). Compared with conventional models, the PREVENT-CVD model demonstrated the strongest predictive performance, with an optimal cut-off of 3.91 (AUC 0.81; adjusted HR 10.69), supporting its clinical utility in Korean kidney transplant recipients.
The PREVENT equation scores were significantly associated with composite clinical outcomes in Korean kidney transplant recipients. Further research is needed to improve risk prediction in this population.
