Converting IMPROVE bleeding and VTE risk assessment models into a fast-and-frugal decision tree for optimal hospital VTE prophylaxis

Abstract

Current hospital venous thromboembolism (VTE) prophylaxis for medical patients is characterized by both underuse and overuse. The American Society of Hematology (ASH) has endorsed the use of risk assessment models (RAMs) as an approach to individualize VTE prophylaxis by balancing overuse (excessive risk of bleeding) and underuse (risk of avoidable VTE). ASH has endorsed IMPROVE (International Medical Prevention Registry on Venous Thromboembolism) risk assessment models, the only RAMs to assess short-term bleeding and VTE risk in acutely ill medical inpatients. ASH, however, notes that no RAMs have been thoroughly analyzed for their effect on patient outcomes. We aimed to validate the IMPROVE models and adapt them into a simple, fast-and-frugal (FFT) decision tree to evaluate the impact of VTE prevention on health outcomes and costs. We used 3 methods: the “best evidence” from ASH guidelines, a “learning health system paradigm” combining guideline and real-world data from the Medical University of South Carolina (MUSC), and a “real-world data” approach based solely on MUSC data retrospectively extracted from electronic records. We found that the most effective VTE prevention strategy used the FFT decision tree based on an IMPROVE VTE score of ≥2 or ≥4 and a bleeding score of <7. This method could prevent 45% of unnecessary treatments, saving ~$5 million annually for patients such as the MUSC cohort. We recommend integrating IMPROVE models into hospital electronic medical records as a point-of-care tool, thereby enhancing VTE prevention in hospitalized medical patients.