Prediction models for @entity932 or XXXX in patients on antiplatelet therapy: a systematic review and external validation study.

@entity1 @entity932 @entity64 @entity2806 @entity548 @entity63 @entity360 @entity1779 @entity385
UNASSIGNED: Essentials Prediction models may help to identify @entity1 at high risk of @entity548 on antiplatelet therapy. We identified existing prediction models for @entity548 and validated them in @entity1 with @entity2806 . Five prediction models were identified, all of which had some methodological shortcomings. Performance in @entity1 with @entity2806 was poor. SUMMARY: Background Antiplatelet therapy is widely used in secondary prevention after a @entity63 ( @entity63 ) or @entity385 . Bleeding is the main adverse effect of antiplatelet therapy and is potentially life threatening. Identification of @entity1 at increased risk of @entity548 may help target antiplatelet therapy. Objective This study sought to identify existing prediction models for @entity932 or @entity548 in patients on antiplatelet therapy and evaluate their performance in @entity1 with @entity2806 . Methods We systematically searched PubMed and Embase for existing prediction models up to December 2014. The methodological quality of the included studies was assessed with the CHARMS checklist. Prediction models were externally validated in the @entity64 Prevention Study 2, comprising 6602 @entity1 with a @entity63 or @entity385 . We assessed discrimination and calibration of included prediction models. Results Five prediction models were identified, of which two were developed in @entity1 with previous @entity2806 . Three studies assessed @entity548 , one studied @entity1779 and one @entity360 . None of the studies met all criteria of good quality. External validation showed poor discriminative performance, with c-statistics ranging from 0.53 to 0.64 and poor calibration. Conclusion A limited number of prediction models is available that predict @entity932 or @entity548 in patients on antiplatelet therapy. The methodological quality of the models varied, but was generally low. Predictive performance in @entity1 with @entity2806 was poor. In order to reliably predict the risk of @entity548 in @entity1 with @entity2806 , development of a prediction model according to current methodological standards is needed.
