Risk Assessment of Venous Thromboembolism and Bleeding in Hospitalized Medical Patients

Abstract

Determining the prognosis or risk of an individual experiencing a specific health outcome within a certain time period is essential to improve health. An important aspect of prognostic research is the development of risk assessment models (RAMs). In support of the movement towards personalized medicine, health care professionals have employed RAMs to stratify an individual patient’s absolute risk of developing a health condition and select the optimal management strategy for that patient. The development of RAMs is generally conducted using data driven methods or through expert consensus. However, these methods present limitations. Accordingly, we recognized the need to select factors for RAM development or update that are evidence-based and clinically relevant using a structured and transparent approach. In this sandwich thesis, I highlight the methods used to select prognostic factors for VTE and bleeding RAMs for hospitalized medical patients. However, the same methods can be applied to any clinical outcome of interest. This work presents a conceptualized and tested novel mixed methods approach to select prognostic factors for VTE and bleeding in hospitalized medical patients that are evidence-based, clinically meaningful and relevant. Our findings may inform the development of new RAMs, the update of widely used RAMs, and external validation and prospective impact assessment studies. Also, these findings may assist decision makers in evaluating the risk of an individual having an outcome to optimize patient care.