A US payer budget impact analysis of Flurpiridaz-PET-MPI compared to SPECT-MPI in the diagnosis of coronary artery disease
Bibliographic record
Abstract
Aims: This economic model was developed to assess the budget impact of a novel radiotracer, Flurpiridaz (F 18 -PET-MPI), compared to SPECT-MPI from a US payer perspective. Materials and Methods:The model was developed comparing F 18 -PET-MPI and SPECT-MPI, with F 18 -PET-MPI modality share increasing from 0.5% to 2.5% of the total MPI modality share, over a 5-year time horizon.The model estimates the impact of diagnostic performance on downstream healthcare resource utilization (HCRU) including invasive coronary angiography (ICA), revascularization, pharmacological treatment, and cardiac outcomes (CO) such as cardiac mortality (CM) and myocardial infarction (MI).Four suspected CAD populations, including general and difficult-to-image subgroups, were analyzed.Clinical inputs used to support the parameterization of the model were sourced from a systematic literature search and included claims-based real-world evidence, observational, and multicenter registry studies to inform the rates of HCRU and CO, and head-to-head comparative clinical trial data advised diagnostic performance inputs.Reimbursement codes informed MPI modality costs.Results are reported as per-member per-month (PMPM) based on a hypothetical health plan.Results: In all suspected CAD populations analyzed, there was a nominal cost increase in the world with F 18 -PET-MPI.The 5-year average PMPM incremental budget impact ranged from $0.02 to $0.05 across all suspected CAD subgroups.Cost-savings were associated with decreased downstream CO such as CM, MI, and ICA. Limitations and Conclusion:The available literature to source all parameters in the model was limited; therefore, assumptions and additional calculations were made based on published evidence to inform the model.A one-way sensitivity analysis was performed to confirm and address uncertainty in key parameters.This comprehensive analysis illustrates that the superior diagnostic performance of F 18 -PET-MPI may result in reduced adverse CO events and associated costs, increased appropriate identification and treatment of CAD, and a minimal increase in overall costs among general and difficult-to-image patient subgroups.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".