NPJ Digit Med. 2026 Jul 14. doi: 10.1038/s41746-026-02977-9. Online ahead of print.
ABSTRACT
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 (Ctd) 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, Ctd 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.
PMID:42448824 | DOI:10.1038/s41746-026-02977-9