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Please use this identifier to cite or link to this item: http://hdl.handle.net/11375/30909
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dc.contributor.advisorArnold, Donald-
dc.contributor.authorGutierrez-Cardona, Nelson-
dc.date.accessioned2025-01-22T16:53:42Z-
dc.date.available2025-01-22T16:53:42Z-
dc.date.issued2024-
dc.identifier.urihttp://hdl.handle.net/11375/30909-
dc.descriptionMSc Health Research Methodology - Health Technology Assessment specializationen_US
dc.description.abstractImmune thrombocytopenia (ITP) is an autoimmune disease characterized by reduced production and augmented destruction of platelets. Adults with ITP have platelet blood counts less than 100x109/L. Ranging from mild to severe, bleeding symptoms may include epistaxis, gingival, petechiae, mucosal, gastrointestinal, vaginal, or intracranial bleeding. ITP can be primary or secondary to other medical conditions. Three phases categorize primary ITP based on the onset and persistence of symptoms: newly diagnosed, persistent, or chronic (1-4). An ITP diagnosis includes a complete blood count, blood film, and viral and autoimmune testing. Depending on patients' comorbidities, the type and number of examinations may vary (3). Diagnosis may also depend on the platelet count response to medications or treatment of secondary causes. There is the need to streamline ITP diagnosis. The time-consuming and high-cost approaches to ruling out other thrombocytopenic conditions, have led Michael G. DeGroote Centre for Transfusion Research (MCTR) researchers to optimize ITP care by developing the Predict-ITP Tool to identify patients with ITP during the initial hematology consultation. The clinical prediction model incorporates data such as platelet count variability, maximum mean platelet volume (MPV), lowest platelet count value, and a history of severe bleeding at any time (5). Improving diagnostic accuracy may improve the quality of life and help reduce expensive, unnecessary, and potentially harmful treatments. Overall, this project vi aims to determine the cost savings of the Predict-ITP tool when implemented in practice, compared to current ITP diagnostic practices. To achieve this goal, in this thesis, we will first estimate the current cost of ITP care and design a health economic evaluation to accompany a randomized controlled trial (RCT) comparing the quality of life and economic impact of the Predict-ITP tool versus current ITP careen_US
dc.language.isoenen_US
dc.subjectCost-utility analysisen_US
dc.subjectHealth economic evaluationen_US
dc.subjectImmune thrombocytopeniaen_US
dc.subjectHealth-Related Quality of Life (HRQoL)en_US
dc.subjectPredict-ITP Toolen_US
dc.subjectEQ-5D-5Len_US
dc.subjectHealth Technology Assessment (HTA)en_US
dc.subjectPatient-Centered Outcomesen_US
dc.titleEconomic impact of the Predict-ITP tool for the diagnosis and treatment of immune thrombocytopeniaen_US
dc.typeThesisen_US
dc.contributor.departmentHealth Research Methodologyen_US
dc.description.degreetypeThesisen_US
dc.description.degreeMaster of Health Sciences (MSc)en_US
dc.description.layabstractPlatelets help to stop bleeding. Immune thrombocytopenia (ITP) is a disease characterized by decreased platelet counts in the blood. This condition may cause symptoms such as recurring gums and nosebleeds, effortless bruising, and potential life-threatening bleeding. There is no specific test to identify ITP, and its diagnosis is based on excluding other causes of low platelet counts. As a result, an incorrect diagnosis is common in the clinic, resulting in unnecessary testing, wrong treatment, decreased quality of life, and increased costs. To identify the probability of a patient having ITP at the initial hematology consultation, McMaster researchers developed a clinical prediction model. The objectives of this study protocol were to determine the cost savings of the prediction model compared to the standard of care. I have designed an economic analysis that will accompany a randomized trial, comparing the use of the prediction model vs. no model (the current standard of care). The primary outcome of the economic analysis will be to demonstrate the clinical prediction model's cost-effectiveness. Secondary outcomes are the difference in costs between the prediction model and current ITP care, resource utilization, and life quality.en_US
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