Enhancing Treatment Decisions by Integrating Patient Data with Clinical Expert Knowledge
Prognostic risk scores such as TIMI and GRACE are commonly used for risk stratification in the management of coronary artery disease (CAD). However, these scores are known to over-estimate patient-specific risk of adverse outcomes because they fail to account for other important risk factors (e.g., fragility, commorbidities) that might be modified patient-specific risks. This inaccurate risk stratification has limited the uptake of standardized risk scores in the management of people with complex CAD (.e.g, multivessel CAD) clinical settingsIn this project, we examine how the integration of clinician expert knowledge with patient data might improve the accuracy of clinical risk prediction tools for prognostic purposes in chronic disease populations. Using a combination of elicitation methodology, data analysis, and bayesian prediction models, we develop more accurate prognostic tools for evaluating treatment options in the management of multivessel CAD, We are exploring the application of these methodologies to the diagnosis of dementia in individuals with mild cognitive impairment