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Precision-guided anticoagulation strategies are necessary for high-risk populations undergoing PCI, as conventional risk scores and standard approaches are often insufficient.
Purpose of review Intraprocedural anticoagulation during percutaneous coronary intervention (PCI) remains particularly challenging in high-risk and underrepresented populations, where the balance between thrombotic and bleeding risk is complex and often unpredictable. This review summarizes contemporary evidence and remaining knowledge gaps regarding anticoagulant selection, dosing, and monitoring in patients with advanced chronic kidney disease (CKD) or end-stage renal disease, cirrhosis, nonagenarians, thrombocytopenia, chronic oral anticoagulation, mechanical circulatory support, and STEMI following fibrinolytic therapy. Recent findings These populations are frequently excluded from randomized clinical trials. In patients with STEMI following fibrinolytic therapy, optimal anticoagulation strategies remain uncertain, with evidence suggesting potential benefit of anticoagulant continuity. Mechanical circulatory support devices introduce additional complexity due to device-related thrombosis and bleeding risks, requiring dynamic, device-specific anticoagulation and monitoring strategies. Special populations such as elderly patients, cirrhosis, and CKD present unique pathophysiologic challenges, including altered pharmacokinetics and rebalanced hemostasis. Similarly, patients on chronic oral anticoagulation require individualized periprocedural strategies, as baseline therapy alone may be insufficient and supplemental intraprocedural anticoagulation is often necessary. Conventional bleeding risk scores demonstrate reduced predictive performance in these populations, highlighting important limitations in current risk stratification. Summary Emerging evidence supports a shift toward precision-guided anticoagulation strategies that integrate actionable patient-specific factors, including renal function, platelet count, liver disease severity, and procedural complexity, along with pharmacogenomics and real-time monitoring. Advances in machine learning–based risk prediction and artificial intelligence-driven clinical decision support tools further offer the potential to enhance individualized care. However, most available evidence remains extrapolated from broader populations, and dedicated prospective studies are needed to define optimal anticoagulant selection, dosing, and monitoring strategies in these high-risk groups.
Attachaipanich et al. (Fri,) reported a other. This document is a confirmation of publication and licensing rights from BioRender and does not contain clinical trial data.