AI-Driven Personalization of Dual Antiplatelet Therapy Duration Post-PCI: A Novel Approach Balancing Ischemic and Bleeding Risks in the UAE Population

Background

Determining the optimal dual antiplatelet therapy (DAPT) duration remains a pivotal concern in managing patients following percutaneous coronary intervention (PCI). While current guidelines emphasize risk stratification, the integration of artificial intelligence (AI)-driven models, including LightGBM, random forest, and logistic regression, for personalized treatment recommendations has not been extensively explored. This study develops and validates an AI-driven framework that leverages UAE-specific and global datasets to refine DAPT duration and optimize post-PCI outcomes.

Methods

Patient data from the Bayanat Data Portal (UAE) and the global MIMIC-IV PhysioNet database were analyzed. Baseline characteristics, ischemic and bleeding events, and long-term clinical outcomes were assessed over a 37-month follow-up period. Among the tested AI models, LightGBM demonstrated the highest predictive accuracy compared to conventional DAPT risk scores. Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves, and feature importance analysis were used to compare risk-adjusted DAPT strategies. Cost-effectiveness was evaluated based on healthcare resource utilization and quality-adjusted life years (QALYs).

Results

Among 5,000 patients, factors such as obesity, prior myocardial infarction (MI), and genetic predispositions significantly influenced DAPT-related outcomes. LightGBM achieved an area under the curve (AUC) of 0.89, surpassing conventional risk scores ( AUC: 0.75, p<0.001 ). Kaplan-Meier curves revealed a significant survival advantage for AI-personalized DAPT (log-rank p<0.01). Shorter DAPT durations increased ischemic risk in high-risk patients, while longer therapy heightened bleeding complications. AI-driven risk stratification reduced unnecessary medication exposure, translating into improved cost-effectiveness and optimized treatment outcomes.

Conclusion

AI-based DAPT personalization significantly enhances risk prediction and clinical decision-making, outperforming traditional models. Integrating UAE-specific data ensures regional applicability, reinforcing the need for precision-driven post-PCI management. These findings support AI-powered decision support systems as a transformative approach to improving cardiovascular outcomes, warranting further validation in prospective trial

Clinical Perspective

• Unmet Need: Current DAPT guidelines provide general recommendations, but individualized therapy remains a challenge due to interpatient variability in ischemic and bleeding risk.

• Novelty of AI Integration: This study uniquely incorporates AI-driven models trained on both UAE-specific and global datasets to refine DAPT duration selection.

• Enhanced Risk Stratification: Machine learning algorithms outperform traditional risk scores in predicting ischemic and bleeding events, enabling a more precise balance of safety and efficacy.

• Real-World Applicability: Using both regional and international data ensures generalizability while maintaining UAE-specific relevance for clinical implementation.

• Economic Impact: Cost-effectiveness analysis demonstrates that AI-driven DAPT adjustments reduce unnecessary medication use and healthcare burden while maintaining cardiovascular protection.

Clinical Implications

• Personalized DAPT Strategies: AI-based models enable individualized treatment plans, moving beyond conventional one-size-fits-all guidelines.

• Reduction of Adverse Events: Optimized therapy duration minimizes ischemic complications in high-risk groups while preventing bleeding events in low-risk patients.

• Informed Clinical Decision-Making: Clinicians can leverage AI predictions to refine treatment decisions, ensuring better patient adherence and outcomes.

• Regional Healthcare Advancements: Findings provide a framework for AI implementation in UAE-based cardiology practice, improving precision medicine approaches.

• Future Research Directions: This study paves the way for prospective clinical trials validating AI-based risk stratification in large, diverse populations.