Key result
Transformer-DAPT outperforms standard deep learning models by up to ~12% in predicting post-PCI risks.
Why the study?
Rule-based scores guiding DAPT post-PCI offer only static or fixed time-interval estimates with modest discrimination, limiting individualized DAPT duration decisions.
Does Transformer-DAPT improve the dynamic prediction of ischemic and bleeding risks in patients on DAPT following PCI compared to benchmark deep learning models?
Cohort (n=48,205)
Yes
Does Transformer-DAPT improve the dynamic prediction of ischemic and bleeding risks in patients on DAPT following PCI compared to benchmark deep learning models?
Transformer-DAPT provides a highly discriminative and well-calibrated AI framework for dynamically predicting individualized ischemic and bleeding risks over the first year post-PCI, outperforming existing deep learning models.
Answers are grounded in this paper and cite their sources.
May support dynamic post-PCI DAPT risk stratification; extends deep learning survival models in observational data but leaves open prospective validation needs.
Dual antiplatelet therapy (DAPT) following percutaneous coronary intervention (PCI) is traditionally guided by rule-based scores providing static, single-time-point or fixed time-interval estimates with modest discrimination (C-index 0.63–0.73), limiting individualized DAPT duration decisions. We developed Transformer-DAPT, a transformer-based deep learning survival framework designed to estimate patient-specific ischemic and bleeding risks across clinically relevant time intervals during the first year after PCI. Using electronic health records from 29,032 patients at Mayo Clinic and externally validated in 19,173 patients from the OneFlorida+ Clinical Research Consortium, Transformer-DAPT achieved time-dependent concordance indices ( C td ) of 0.84–0.87 for ischemic events and 0.81–0.88 for bleeding events, outperforming DeepSurv and DeepHit by 2%–12%. In the external cohort, C td ranged from 0.74–0.84 and 0.75–0.83 for ischemic and bleeding events, respectively. Transformer-DAPT demonstrated improved discrimination and calibration performance, providing a framework for multi-interval risk prediction to support personalized DAPT management after PCI.
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Abdelhameed et al. (2026) conducted a cohort in Patients on DAPT following PCI (n=48,205). Transformer-DAPT vs. DeepSurv and DeepHit was evaluated on Time-dependent concordance index (Ctd) for ischemic events at 1 month (95% CI 0.81-0.86, p=0.008). Transformer-DAPT achieved time-dependent concordance indices of 0.84-0.87 for ischemic events and 0.81-0.88 for bleeding events, outperforming DeepSurv and DeepHit by 2-12%.
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