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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.
Due to retrospective data and the exclusion of patients with kidney failure in clinical trials, the ability to predict mortality in patients with acute coronary syndrome (ACS) receiving dialysis is limited, particularly in females, by the type of revascularization (Percutaneous Coronary Intervention [PCI] and Coronary Artery Bypass Graft [CABG]).
Using the United States Renal Data System, 51 clinical and demographic features were extracted from 60,491 dialysis patients with acute coronary syndrome. The performance of an artificial intelligence-based mortality prediction model in dialysis patients by revascularization strategy type and sex from 30 days to 5 years after the first acute coronary syndrome hospitalization, based on precision, recall, F1 score, and area under the receiver operating curve (AUROC). Stratified random sampling was employed with an 80-20 split and a 3-fold cross-validation. Five machine learning models and one deep learning model were trained and evaluated.
The gradient-boosting machine learning model outperformed other models with an AUROC between 0.6532 and 0.7898. The deep neural network had the highest F1 score, achieving 0.9299 at the 5-year prediction. At 30 days, PCI decreased predicted mortality by 6.48%, while CABG decreased predicted mortality by 2.33%. At 5 years, PCI decreased the predicted mortality by 3.45%, while CABG decreased the predicted mortality by 3.30%. In both males and females, from 30 days to 5 years, PCI led to the greatest decrease in predicted mortality, while no treatment led to the greatest increase in predicted mortality.
This study is the first to develop an artificial intelligence-based mortality prediction model for patients with acute coronary syndrome undergoing dialysis. The deep neural network showed the best overall performance. PCI had a greater effect on reducing predicted mortality as compared to CABG from 30 days to 5 years in both males and females with acute coronary syndrome undergoing dialysis.
