Predicting major bleeding in patients with noncardioembolic stroke on antiplatelets: S2TOP-BLEED.

Hilkens, Nina A; Algra, Ale; Diener, Hans-Christoph; Reitsma, Johannes B; Bath, Philip M; Csiba, Laszlo; Hacke, Werner; Kappelle, L Jaap; Koudstaal, Peter J; Leys, Didier; Mas, Jean-Louis; Sacco, Ralph L; Amarenco, Pierre; Sissani, Leila; Greving, Jacoba P

OBJECTIVE To develop and externally validate a prediction model for major bleeding in patients with a TIA or ischemic stroke on antiplatelet agents. METHODS We combined individual patient data from 6 randomized clinical trials (CAPRIE, ESPS-2, MATCH, CHARISMA, ESPRIT, and PRoFESS) investigating antiplatelet therapy after TIA or ischemic stroke. Cox regression analyses stratified by trial were performed to study the association between predictors and major bleeding. A risk prediction model was derived and validated in the PERFORM trial. Performance was assessed with the c statistic and calibration plots. RESULTS Major bleeding occurred in 1,530 of the 43,112 patients during 94,833 person-years of follow-up. The observed 3-year risk of major bleeding was 4.6% (95% confidence interval [CI] 4.4%-4.9%). Predictors were male sex, smoking, type of antiplatelet agents (aspirin-clopidogrel), outcome on modified Rankin Scale ≥3, prior stroke, high blood pressure, lower body mass index, elderly, Asian ethnicity, and diabetes (S2TOP-BLEED). The S2TOP-BLEED score had a c statistic of 0.63 (95% CI 0.60-0.64) and showed good calibration in the development data. Major bleeding risk ranged from 2% in patients aged 45-54 years without additional risk factors to more than 10% in patients aged 75-84 years with multiple risk factors. In external validation, the model had a c statistic of 0.61 (95% CI 0.59-0.63) and slightly underestimated major bleeding risk. CONCLUSIONS The S2TOP-BLEED score can be used to estimate 3-year major bleeding risk in patients with a TIA or ischemic stroke who use antiplatelet agents, based on readily available characteristics. The discriminatory performance may be improved by identifying stronger predictors of major bleeding.