Contrastive Learning With Transformer for Adverse Endpoint Prediction in Patients on DAPT Post-Coronary Stent Implantation

Authors

Fang Li
Zenan Sun
Ahmed Abdelhameed
Tiehang Duan
Laila Rasmy
Xinyue Hu
Jianping He
Yifang Dang
Jingna Feng
Jianfu Li
Yichen Wang
Tianchen Lyu
Naomi Braun
Si Pham
Michael Gharacholou
DeLisa Fairweather
Degui Zhi
Jiang Bian
Cui Tao

Language

English

Publication Date

1-1-2024

Journal

Frontiers in Cardiovascular Medicine

DOI

10.3389/fcvm.2024.1460354

PMID

39872877

PMCID

PMC11769931

PubMedCentral® Posted Date

1-13-2025

PubMedCentral® Full Text Version

Post-print

Abstract

Background: Effective management of dual antiplatelet therapy (DAPT) following drug-eluting stent (DES) implantation is crucial for preventing adverse events. Traditional prognostic tools, such as rule-based methods or Cox regression, despite their widespread use and ease, tend to yield moderate predictive accuracy within predetermined timeframes. This study introduces a new contrastive learning-based approach to enhance prediction efficacy over multiple time intervals.

Methods: We utilized retrospective, real-world data from the OneFlorida + Clinical Research Consortium. Our study focused on two primary endpoints: ischemic and bleeding events, with prediction windows of 1, 2, 3, 6, and 12 months post-DES implantation. Our approach first utilized an auto-encoder to compress patient features into a more manageable, condensed representation. Following this, we integrated a Transformer architecture with multi-head attention mechanisms to focus on and amplify the most salient features, optimizing the representation for better predictive accuracy. Then, we applied contrastive learning to enable the model to further refine its predictive capabilities by maximizing intra-class similarities and distinguishing inter-class differences. Meanwhile, the model was holistically optimized using multiple loss functions, to ensure the predicted results closely align with the ground-truth values from various perspectives. We benchmarked model performance against three cutting-edge deep learning-based survival models, i.e., DeepSurv, DeepHit, and SurvTrace.

Results: The final cohort comprised 19,713 adult patients who underwent DES implantation with more than 1 month of records after coronary stenting. Our approach demonstrated superior predictive performance for both ischemic and bleeding events across prediction windows of 1, 2, 3, 6, and 12 months, with time-dependent concordance (Ctd) index values ranging from 0.88 to 0.80 and 0.82 to 0.77, respectively. It consistently outperformed the baseline models, including DeepSurv, DeepHit, and SurvTrace, with statistically significant improvement in the Ctd-index values for most evaluated scenarios.

Conclusion: The robust performance of our contrastive learning-based model underscores its potential to enhance DAPT management significantly. By delivering precise predictive insights at multiple time points, our method meets the current need for adaptive, personalized therapeutic strategies in cardiology, thereby offering substantial value in improving patient outcomes.

Keywords

dual antiplatelet therapy, contrastive learning, transformer, predictive modeling, adverse endpoint, drug-eluting coronary artery stent implantation, survival analysis

Published Open-Access

yes

Recommended Citation

Li, Fang; Sun, Zenan; Abdelhameed, Ahmed; et al., "Contrastive Learning With Transformer for Adverse Endpoint Prediction in Patients on DAPT Post-Coronary Stent Implantation" (2024). Faculty, Staff and Student Publications. 683.
https://digitalcommons.library.tmc.edu/uthshis_docs/683