Baseline Risk of Major Bleeding in Non–ST-segment Elevation Myocardial Infarction: The CRUSADE Bleeding Score

By S. Subherwal, Richard G. Bach, Anita Y. Chen, Brian F. Gage, Sunil V. Rao, L. K. Newby, Tracy Y. Wang, W. Gibler, E. Ohman, Matthew T. Roe and 3 more. Circulation, 2009

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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.

Subfields: Cardiology and Cardiovascular Medicine, Surgery

View Paper: https://www.semanticscholar.org/paper/e426dab204fb19d9792577fa931ab3d47700b8c5

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In Cardiology and Cardiovascular Medicine

  1. G. Fonarow
  2. J. Halperin
  3. Judith Hochman
  4. M. Creager
  5. C. Yancy
  6. M. Gheorghiade
  7. Véronique L. Roger

In Surgery

  1. Judith Hochman
  2. M. Creager

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