Prediction of in-hospital bleeding in acutely ill medical patients: External validation of the IMPROVE bleeding risk score

Summary. A variety of definitions of major bleeding have been used in published clinical studies, and this diversity adds to the difficulty in comparing data between trials and in performing meta-analyses. In the first step towards unified definitions of bleeding complications, the definition of major bleeding in non-surgical patients was discussed at the Control of Anticoagulation Subcommittee of the International Society on Thrombosis and Haemostasis. Arising from that discussion, a definition was developed that should be applicable to studies with all agents that interfere with hemostasis, including anticoagulants, platelet function inhibitors and fibrinolytic drugs. The definition and the text that follows have been reviewed and approved by the cochairs of the subcommittee and the revised version is published here. The intention is to also seek approval of this definition from the regulatory authorities.

Prediction models in health care use predictors to estimate for an individual the probability that a condition or disease is already present (diagnostic model) or will occur in the future (prognostic model). Publications on prediction models have become more common in recent years, and competing prediction models frequently exist for the same outcome or target population. Health care providers, guideline developers, and policymakers are often unsure which model to use or recommend, and in which persons or settings. Hence, systematic reviews of these studies are increasingly demanded, required, and performed. A key part of a systematic review of prediction models is examination of risk of bias and applicability to the intended population and setting. To help reviewers with this process, the authors developed PROBAST (Prediction model Risk Of Bias ASsessment Tool) for studies developing, validating, or updating (for example, extending) prediction models, both diagnostic and prognostic. PROBAST was developed through a consensus process involving a group of experts in the field. It includes 20 signaling questions across 4 domains (participants, predictors, outcome, and analysis). This explanation and elaboration document describes the rationale for including each domain and signaling question and guides researchers, reviewers, readers, and guideline developers in how to use them to assess risk of bias and applicability concerns. All concepts are illustrated with published examples across different topics. The latest version of the PROBAST checklist, accompanying documents, and filled-in examples can be downloaded from www.probast.org.