Comparison of coronary CT angiography versus functional imaging for CABG patients: A resource utilization analysis
Bibliographic record
Abstract
AIMS: The impact of anatomical versus functional testing in patients with prior coronary artery bypass surgery (CABG) is poorly defined. We therefore sought to determine the rates of downstream investigations and the attendant healthcare costs in CABG patients undergoing CCTA versus SPECT. METHODS AND RESULTS: 2754 consecutive CABG patients were imaged by SPECT (2163) or CCTA (591). 425 patients (15.4%) underwent downstream testing which was more common in those imaged with CCTA versus SPECT (23.18% vs 13.31% respectively, p < 0.01). When a propensity score adjustment was made for differences in baseline characteristics, the findings in downstream testing persisted (p < 0.01). When patients who subsequently underwent repeat revascularization (arguably the highest risk patients) were removed from the analysis, downstream testing remained more frequent in CCTA (12.7%) versus SPECT imaged patients (8.8%) (p = 0.01). Costs of downstream tests per patient were two-fold greater in the CCTA group in comparison to the SPECT group ($366.79 ± 29.59 vs $167.35 ± 10.12 respectively, p < 0.01). Conversely, total costs which included the index costs were less in the CCTA group, $764.66 ± 29.59 versus $1396.73 ± 1012 for the SPECT cohort, p < 0.0001). CONCLUSIONS: Index imaging with SPECT versus CCTA in CABG patients was associated with fewer downstream tests, less ICA, less repeat revascularization but greater expense. Cost however is only part of the decision making process that determines an optimal index test. Until CCTA demonstrates improved risk stratification over SPECT in CABG patients it is likely SPECT will remain the preferred first imaging test.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".