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Circulation. 2009;119:1873-1882
Published online before print March 30, 2009, doi: 10.1161/CIRCULATIONAHA.108.828541
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Circulation: April 14, 2009, Volume 119, Number 14
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(Circulation. 2009;119:1873-1882.)
© 2009 American Heart Association, Inc.


Coronary Heart Disease

Baseline Risk of Major Bleeding in Non–ST-Segment–Elevation Myocardial Infarction

The CRUSADE (Can Rapid risk stratification of Unstable angina patients Suppress ADverse outcomes with Early implementation of the ACC/AHA guidelines) Bleeding Score

Sumeet Subherwal, MD; Richard G. Bach, MD; Anita Y. Chen, MS; Brian F. Gage, MD, MSc; Sunil V. Rao, MD; L. Kristin Newby, MD, MHS; Tracy Y. Wang, MD, MS; W. Brian Gibler, MD; E. Magnus Ohman, MD; Matthew T. Roe, MD, MHS; Charles V. Pollack, Jr, MD, MA; Eric D. Peterson, MD, MPH; Karen P. Alexander, MD

From the Washington University School of Medicine (S.S., R.G.B., B.F.G.), St Louis, Mo; Duke Clinical Research Institute (A.Y.C., S.V.R., L.K.N., T.Y.W., E.M.O., M.T.R., E.D.P., K.P.A.), Durham, NC; University of Cincinnati (W.B.G.), Cincinnati, Ohio; and Pennsylvania Hospital (C.V.P.), Philadelphia, Pa.

Correspondence to Richard G. Bach, MD, Cardiovascular Division, Washington University School of Medicine, Campus Box 8086, 660 S Euclid Ave, St Louis, MO 63110. E-mail rbach{at}im.wustl.edu

Received October 13, 2008; accepted January 28, 2009.

Background— Treatments for non–ST-segment–elevation myocardial infarction (NSTEMI) reduce ischemic events but increase bleeding. Baseline prediction of bleeding risk can complement ischemic risk prediction for optimization of NSTEMI care; however, existing models are not well suited for this purpose.

Methods and Results— We developed (n=71 277) and validated (n=17 857) a model that identifies 8 independent baseline predictors of in-hospital major bleeding among community-treated NSTEMI patients enrolled in the Can Rapid risk stratification of Unstable angina patients Suppress ADverse outcomes with Early implementation of the ACC/AHA guidelines (CRUSADE) Quality Improvement Initiative. Model performance was tested by c statistics in the derivation and validation cohorts and according to postadmission treatment (ie, invasive and antithrombotic therapy). The CRUSADE bleeding score (range 1 to 100 points) was created by assignment of weighted integers that corresponded to the coefficient of each variable. The rate of major bleeding increased by bleeding risk score quintiles: 3.1% for those at very low risk (score ≤20); 5.5% for those at low risk (score 21–30); 8.6% for those at moderate risk (score 31–40); 11.9% for those at high risk (score 41–50); and 19.5% for those at very high risk (score >50; Ptrend <0.001). The c statistics for the major bleeding model (derivation=0.72 and validation=0.71) and risk score (derivation=0.71 and validation=0.70) were similar. The c statistics for the model among treatment subgroups were as follows: ≥2 antithrombotics=0.72; <2 antithrombotics=0.73; invasive approach=0.73; conservative approach=0.68.

Conclusions— The CRUSADE bleeding score quantifies risk for in-hospital major bleeding across all postadmission treatments, which enhances baseline risk assessment for NSTEMI care.


 

CLINICAL PERSPECTIVE


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