New Bleeding Model Shows Promise for Predicting Post-Procedure Bleeding Risk Among PCI Patients

A new bleeding definition and risk model used in the ACC’s CathPCI Registry to improve the monitoring and safety of PCI has the potential to better inform clinical decision making and facilitate risk-adjusted provider feedback to support quality improvement, according to a report published Sept. 16 in the JACC: Cardiovascular Interventions .

Using detailed clinical data from 1,043,759 PCI procedures at 1,142 centers enrolled in the CathPCI Registry between February 2008 and April 2011, the report authors were able to update the definition of bleeding to capture hemorrhagic events that had been previously excluded, as well as develop and validate contemporary predictive and risk-adjustment models for post-PCI bleeding.

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Based on the new model, data showed that 60,194 PCI procedures resulted in post-procedure bleeding, yielding a bleeding event rate of 5.8 percent, or bleeding events in about one in 20 patients. The factors that most predicted post-procedure bleeding risk were being a female patient, followed by shock and salvage PCI. The least predictive factor was non-insulin-requiring diabetes.

The authors note that the 5.8 percent bleeding rate was higher than the previously reported 2.4 percent rate, and reflects the inclusion of bleeding complications, such as tamponade and transfusions, not included in the previous definition of bleeding.

Moving forward, the risk model “can be used to risk-adjust post-PCI bleeding rates for the centers participating in the CathPCI Registry, identify leaders and laggards, and ultimately improve the safety of PCI by encouraging the adoption of BAS [bleeding avoidance strategies] at centers that have higher-than-expected risk-adjusted bleeding rates,” the authors said.

Further, “the use of the CathPCI Registry bleeding risk score may encourage greater adoption of bivalirudin, vascular closure devices or radial approach among patients in these higher-risk categories,” the authors conclude.

Keywords: Quality Improvement, Registries, Insulin, Decision Making, Peptide Fragments, Hirudins, Hemorrhage, Diabetes Mellitus, Risk Adjustment

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