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Prediction models for intracranial hemorrhage or major bleeding in patients on antiplatelet therapy: a systematic review and external validation study
Author(s) -
Hilkens N. A.,
Algra A.,
Greving J. P.
Publication year - 2016
Publication title -
journal of thrombosis and haemostasis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.947
H-Index - 178
eISSN - 1538-7836
pISSN - 1538-7933
DOI - 10.1111/jth.13196
Subject(s) - medicine , stroke (engine) , intracerebral hemorrhage , ischemia , checklist , subarachnoid hemorrhage , mechanical engineering , psychology , engineering , cognitive psychology
Essentials Prediction models may help to identify patients at high risk of bleeding on antiplatelet therapy. We identified existing prediction models for bleeding and validated them in patients with cerebral ischemia. Five prediction models were identified, all of which had some methodological shortcomings. Performance in patients with cerebral ischemia was poor.Summary Background Antiplatelet therapy is widely used in secondary prevention after a transient ischemic attack ( TIA ) or ischemic stroke. Bleeding is the main adverse effect of antiplatelet therapy and is potentially life threatening. Identification of patients at increased risk of bleeding may help target antiplatelet therapy. Objective This study sought to identify existing prediction models for intracranial hemorrhage or major bleeding in patients on antiplatelet therapy and evaluate their performance in patients with cerebral ischemia. 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 European Stroke Prevention Study 2, comprising 6602 patients with a TIA or ischemic stroke. We assessed discrimination and calibration of included prediction models. Results Five prediction models were identified, of which two were developed in patients with previous cerebral ischemia. Three studies assessed major bleeding, one studied intracerebral hemorrhage and one gastrointestinal bleeding. 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 intracranial hemorrhage or major bleeding in patients on antiplatelet therapy. The methodological quality of the models varied, but was generally low. Predictive performance in patients with cerebral ischemia was poor. In order to reliably predict the risk of bleeding in patients with cerebral ischemia, development of a prediction model according to current methodological standards is needed.