Contrastive learning with transformer for adverse endpoint prediction in patients on DAPT post-coronary stent implantation.

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 (C) 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 C-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.