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

Abstract

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 < or = 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; P(trend) <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: > or = 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.

Figure 1

Predicted probability of in-hospital major bleeding across the spectrum of CRUSADE bleeding score in the derivation cohort.

Figure 2

Rate of major bleeding across CRUSADE bleeding score risk groups in the derivation and validation cohorts. Very low (bleeding score ≤20): derivation N=19,486 and validation N=4920; low (bleeding score 21–30): derivation N=12,545 and validation N=3141; moderate (bleeding score 31–40): derivation N=11,530 and validation N=2873; high (bleeding score 41–50): derivation and validation N=2787; and very high (bleeding score >50): derivation and validation N=3761. P_trend <0.001.

Figure 3

Rate of major bleeding among patients treated with <2 antithrombotics versus ≥2 antithrombotics across CRUSADE bleeding score in the derivation cohort. Quintiles defined as: very low (≤20), N=18,406; low (21–30), N=11,368; moderate (31–40), N=9871; high (41–50), N=8290; and very high (>50), N=8965. P_trend<0.001 within each of the two strata.

Figure 4

Rate of major bleeding among patients treated with ≥2 antithrombotics undergoing an invasive approach (catheterization) versus a conservative approach (no catheterization) across CRUSADE bleeding score in the derivation cohort. Quintiles defined as: very low (≤20), N=16,974; low (21–30), N=10,067; moderate (31–40), N=8142; high (41–50), N=6105; and very high (>50), N=5404. P_trend<0.001 within each of the two strata.

Figure 5

In-hospital mortality among patients having a major bleed versus those without a major bleed across CRUSADE bleeding score quintiles in the derivation cohort. Within each risk quintile, the P value for difference between patients who had a bleed versus those who did not was (chi-square adjusting for hospital clustering) <0.0001.