Background: Bleeding is a common complication of percutaneous coronary intervention (PCI) that is associated with worse clinical outcomes and increased costs. Improved pre-procedural bleeding risk prediction could promote strategies that have been shown to reduce post-PCI bleeding, including increased adoption of radial access.
Methods: We studied patients in the Veterans Affairs Clinical Assessment, Reporting, and Tracking (VA CART) program receiving PCI in VA hospitals. Logistic regression was performed to develop a model for major in-hospital bleeding using demographic, clinical, and procedural variables. The discriminatory ability of the model was compared to the existing National Cardiovascular Data Registry (NCDR) CathPCI bleeding risk model.
Results: Among 107,451 patients treated from 2008 to 2019, 5218 (4.86%) experienced an in-hospital bleeding event. Twelve variables were associated with bleeding risk. Predictors of bleeding included emergency or salvage status, cardiogenic shock, NSTEMI, Atrial fibrillation, elevated INR, and peripheral vascular disease, while radial access, greater body surface area, and stable or unstable angina were associated with lower risk of bleeding. The developed model had superior discrimination compared with the NCDR CathPCI model (c-index 0.756, 95% CI 0.749-0.764 vs. 0.707, 95% CI 0.700-0.714, p < 0.001), especially among the highest risk patients. A web-based tool has been created to facilitate calculation of bleeding risk using this model at the point of care.
Conclusion: The VA CART bleeding risk model uses baseline clinical and procedural variables to predict post-PCI in-hospital bleeding events and has improved discrimination compared to other available models in this patient population. Implementation of this model can facilitate risk stratification at the point of care and permit improved risk-adjustment for quality assessment.
Keywords: Bleeding; Percutaneous coronary intervention; Quality improvement; Risk prediction.
Published by Elsevier Inc.