Budget Impact Model Framework of Positron Emission Tomography Compared to Single Photon Emission Computed Tomography Myocardial Perfusion Imaging for Coronary Artery Disease Diagnosis


Walczyk Mooradally A1, Priest S1, Ferko N1, Szabo E2, Cabra HA3
1EVERSANA, Burlington, ON, Canada, 2GE HealthCare, Pittsburgh, PA, USA, 3GE HealthCare, Miami, FL, USA

OBJECTIVES: Nuclear-based myocardial perfusion imaging (MPI) single-photon emission computed tomography (SPECT) and positron emission tomography (PET) are used for coronary artery disease (CAD) diagnosis, with SPECT being the prevailing modality. SPECT poses key limitations that translate into sub-optimal diagnosis in high-risk subgroups (women, diabetes, high body mass index [BMI]) that cannot realize SPECT efficiently due to attenuation artifacts. PET presents a promising alternative, including a novel radiotracer with increased diagnostic performance. Existing MPI economic models do not connect diagnostic performance to downstream outcomes to capture the long-term impact of sub-optimal diagnosis. This study employed a comprehensive framework to inform the development of a budget impact model (BIM) that links diagnostic performance to healthcare resource use (HCRU) and cardiac events.

METHODS: The BIM compares two diagnostic modalities (PET and SPECT) in four analysis populations including general CAD and high-risk subgroups (women, diabetes, high BMI). Diagnostic performance (i.e., sensitivity, specificity, repeat testing), HCRU (i.e., coronary angiography, revascularization), and cardiac events (i.e., myocardial infarction, cardiac mortality) are evaluated.

RESULTS: Diagnostic performance is connected to downstream clinical outcomes. The model assumes that patients with a true positive or true negative scan are appropriately receiving or avoiding invasive procedures, cardiac events, and pharmacological treatment, respectively. Patients with a false positive scan are subjected to unnecessary coronary angiography, while those with a false negative scan may experience a cardiac event and associated treatment costs. Costing input parameters include MPI modality, pharmacological treatment, HCRU, and cardiac outcome costs.

CONCLUSIONS: This BIM includes a new approach designed to quantify improved CAD diagnostic performance and its impact on downstream clinical outcomes, capturing all costs in the CAD clinical pathway. The economic outputs yielded from this BIM can be used to support healthcare decision coverage for the use of MPI in patients with CAD and high-risk subgroups.

Conference/Value in Health Info

2024-05, ISPOR 2024, Atlanta, GA, USA

Value in Health, Volume 27, Issue 6, S1 (June 2024)




Economic Evaluation, Medical Technologies

Topic Subcategory

Budget Impact Analysis, Diagnostics & Imaging


Cardiovascular Disorders (including MI, Stroke, Circulatory), Medical Devices

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