Principal Investigators: Drs. Karin Humphries and Colleen Norris
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Vancouver General Hospital
St. Paul’s Hospital
Mazankowski Alberta Heart Institute
There are three treatment options for patients with stable coronary artery disease (CAD): optimal medical therapy alone (a combination of lipid lowering, anti-anginal, and antihypertensive therapies), or coronary revascularization by either percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG). Contemporary clinical trials in patients with stable CAD have demonstrated no survival advantage for either form of revascularization over optimal medical therapy alone, but improvements in symptoms and health-related quality of life (HRQoL) were demonstrated. In contrast, research indicates that patients undergoing revascularization believe the procedure is performed to improve their survival or prevent myocardial infarction. Additionally, evidence suggests patients’ and physicians’ preferences, with respect to treatment options and prioritization of outcomes, differ. Failure to include patient-preferred outcomes in treatment decision-making represents a significant gap in clinical research and practice.
Focus groups with patients and physicians will be conducted to assess the importance of different prognostic information and to prioritize the clinical and HRQoL outcomes that are most relevant. Using information obtained from the focus groups, prediction models for important clinical and HRQoL outcomes will be developed and validated using data of patients undergoing coronary angiography from 2004 to 2013, extracted from two well-established cardiac registries in Alberta and B.C. (APPROACH and HeartIS, respectively). Patients and physicians will be involved in identifying formats that effectively communicate the modeling results and in reviewing the SDM tool to ensure it meets the needs of both audiences.
Once the final models and presentation format are defined, ePRISM®, a company specializing in implementing clinical assessment tools by building interfaces with hospital information systems and clinical registries, will create the interfaces that will populate the final model in order to generate patient-specific risks for the identified outcomes. The study will then focus on the implementation and assessment of the SDM process in clinical practice by exploring potential implementation strategies to seamlessly integrate the SDM tool into the routine flow of care, minimize data entry, and limit disruption of the patient-physician dialogue.
Better strategies are needed to address the discrepancies between evidence and practice, and the failure to consider patients’ preferences. Novel shared decision-making (SDM) tools that engage patients and communicate individualized risks and benefits of treatment can lead to more informed treatment decisions that can ultimately optimize the patient’s health outcomes.