Bleeding in patients undergoing percutaneous coronary intervention: the development of a clinical risk algorithm from the National Cardiovascular Data Registry.
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 after PCI and developed a simplified, clinically useful algorithm to predict patient risk.


RESULTS
Data were analyzed from 302 152 PCI procedures performed at 440 US centers participating in the National Cardiovascular Data Registry. As defined by the National Cardiovascular Data Registry, 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, previous heart failure, glomerular filtration rate, peripheral vascular disease, no previous PCI, New York Heart Association/Canadian Cardiovascular Society Functional Classification class IV heart failure, ST-elevation myocardial infarction, non-ST-elevation myocardial infarction, 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 3 categories, bleeding rates were greater in patients with higher estimates (<or=7, 0.7%; 8 to 17, 1.8%; >or=18, 5.1%).


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.
Mehta
Sameer K
SK
Division of Cardiovascular Research, Mid America Heart Institute, Saint Luke's Hospital, Kansas City, MO 64111, USA.
Frutkin
Andrew D
AD
Lindsey
Jason B
JB
House
John A
JA
Spertus
John A
JA
Rao
Sunil V
SV
Ou
Fang-Shu
FS
Roe
Matthew T
MT
Peterson
Eric D
ED
Marso
Steven P
SP
D000368:Aged; D000465:Algorithms; D015906:Angioplasty, Balloon, Coronary / Q000009:adverse effects*; D001803:Blood Transfusion; D003661:Decision Support Techniques; D005260:Female; D006454:Hemoglobins / Q000378:metabolism; D006470:Hemorrhage / Q000097:blood / Q000139:chemically induced* / Q000628:therapy; D006801:Humans; D007902:Length of Stay; D008297:Male; D008875:Middle Aged; D010975:Platelet Aggregation Inhibitors / Q000009:adverse effects*; D012042:Registries; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D013927:Thrombosis / Q000209:etiology / Q000517:prevention & control*; D013997:Time Factors; D016896:Treatment Outcome; D014481:United States
D006454:Hemoglobins; D010975:Platelet Aggregation Inhibitors