Economic and healthcare resource utilization assessments of PET imaging in Coronary Artery Disease diagnosis: a systematic review and discussion of opportunities for future economic evaluations
Bibliographic record
Abstract
AIMS: This systematic literature review (SLR) consolidated economic and healthcare resource utilization (HCRU) evidence for positron emission tomography (PET) and single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) to inform future economic evaluations. MATERIALS AND METHODS: An electronic search was conducted in MEDLINE, Embase, and Cochrane databases from 2012-2022. Economic and HCRU studies in adults who underwent PET- or SPECT-MPI for coronary artery disease (CAD) diagnosis were eligible. A qualitative methodological assessment of existing economic evaluations, HCRU, and downstream cardiac outcomes was completed. Exploratory meta-analyses of clinical outcomes were performed. RESULTS: The search yielded 13,439 results, with 71 records included. Economic evaluations and comparative clinical trials were limited in number and outcome types (HCRU, downstream cardiac outcomes, and diagnostic performance) assessed. No studies included all outcome types and only one economic evaluation linked diagnostic performance to HCRU. The meta-analyses of comparative studies demonstrated significantly higher rates of early- and late-invasive coronary angiography and revascularization for PET- compared to SPECT-MPI; however, the rate of repeat testing was lower with PET-MPI. The rate of acute myocardial infarction was lower, albeit non-significant with PET- vs. SPECT-MPI. LIMITATIONS AND CONCLUSIONS: This SLR identified economic and HCRU evaluations following PET- and SPECT-MPI for CAD diagnosis and determined that existing studies do not capture all pertinent outcome parameters or link diagnostic performance to downstream HCRU and cardiac outcomes, thus, resulting in simplified assessments of CAD burden. A limitation of this work relates to heterogeneity in study designs, patient populations, and follow-up times of existing studies. Resultingly, it was challenging to pool data in meta-analyses. Overall, this work provides a foundation for the development of comprehensive economic models for PET- and SPECT-MPI in CAD diagnosis, which should link diagnostic outcomes to HCRU and downstream cardiac events to capture the full CAD scope.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".