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. / Thesis / Doctor of Philosophy (PhD) / Measuring the probability of an individual experiencing a specific health outcome in a certain period of time based on that individual’s risk factors is important to improve health. Prediction tools are often used to calculate the probability of an outcome. Health care practitioners use prediction tools to assess an individual’s risk of a certain health outcome and in turn provide individualized management. Prediction tools include a number of agreed upon risk factors that should be assessed in order to best estimate the risk of an outcome. These risk factors are usually selected through exploring sets of data or by consulting a group of experts in the field. However, these methods have limitations. Therefore, we recognized that it is important, when developing prediction tools, to select risk factors that are evidence-based and clinically relevant by adopting a systematic, comprehensive, structured and transparent approach. These sets of risk factors can then aid health researchers when developing new prediction tools or updating existing ones and help clinicians predicting risk. In this thesis, I highlight the methods used to select factors for prediction tools that evaluate the risk of having a venous clot or a bleeding event in patients that are hospitalized for a medical condition. However, the same methods can be applied to any clinical condition and outcome of interest.
This work presents a new approach that we conceptualized and tested to select risk factors for venous clots and bleeding events in hospitalized medical patients that are evidence-based, clinically meaningful and relevant. Our findings may inform the development of new prediction tools, the update of widely used tools, and the design of studies to validate these tools. Also, these findings may assist decision makers in evaluating the risk of an individual having an outcome to optimize patient care.
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/25793 |
| Date | January 2020 |
| Creators | Darzi, Andrea |
| Contributors | Schünemann, Holger, Health Research Methodology |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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