Published Online
on May 8, 2009

A more recent version of this article appeared on June 1, 2009

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Original Article

Bleeding in Patients Undergoing Percutaneous Coronary Intervention: The Development of a Clinical Risk Algorithm from the National Cardiovascular Data Registry

Sameer K. Mehta¹; Andrew D. Frutkin¹; Jason B. Lindsey¹; John A. House¹; John A. Spertus¹; Sunil V. Rao²; Fang-Shu Ou²; Matthew T. Roe²; Eric D. Peterson² and Steven Marso^1,3

¹ The Mid America Heart Institute, Kansas City, MO;
² Duke Clinical Research Institute, Durham, NC

³ E-mail: smarso{at}saint-lukes.org

Background—Bleeding in patients undergoing percutaneous coronary intervention (PCI) is associated with increased morbidity, mortality, length of hospitalization, and cost. We identified baseline clinical characteristics associated with bleeding complications following PCI and developed a simplified, clinically useful algorithm to predict patient risk.

Methods and Results—Data were analyzed from 302,152 PCI procedures performed at 440 U.S. centers participating in the National Cardiovascular Data Registry (NCDR). As defined by the NCDR, bleeding required transfusion, prolonged hospital stay, and/or a drop in hemoglobin >3.0 g/dL from any location, including percutaneous entry site, retroperitoneal, gastrointestinal, genitourinary, and other/unknown location. Bleeding complications occurred in 2.4% of patients. From the best-fitting model consisting of 15 clinical elements associated with post-PCI bleeding in a random 80% training cohort, we developed a parsimonious risk algorithm. Predictors of bleeding included age, gender, prior heart failure, glomerular filtration rate, peripheral vascular disease, no prior PCI, NYHA/CCS Class IV heart failure, ST-elevation myocardial infarction (MI), non-ST-elevation MI, and cardiogenic shock. The parsimonious model was validated in the remaining 20% of the population (c-statistic 0.72) and in clinically relevant subgroups of patients. This simplified model was used to derive a clinical risk algorithm, with larger numbers corresponding with greater risk. In three dichotomous categories, bleeding rates were greater in patients with higher estimates (7: 0.7%, 8-17: 1.9%, 18: 5.3%).

Conclusions—This report identifies baseline clinical factors associated with bleeding and proposes a clinically useful algorithm to estimate bleeding risk. This model is potentially actionable in altering therapeutic decision-making and improving outcomes in patients undergoing PCI.

Key Words: catheterization • hemorrhage • risk factors

Author contributions: Submitted on behalf of the NCDR Registry