Appropriate utilization of cardiac computed tomography for the assessment of stable coronary artery disease
Bibliographic record
Abstract
BACKGROUND: Appropriate use criteria (AUC) have been developed in response to growth in cardiac imaging utilization and concern regarding associated costs. Cardiac computed tomography angiography (CCTA) has emerged as an important modality in the evaluation of coronary artery disease, however its appropriate utilization in actual practice is uncertain. Our objective was to determine the appropriate utilization of CCTA in a large quaternary care institution and to compare appropriate utilization pre and post publication of the 2013 AUC guidelines. We hypothesized that the proportion of appropriate CCTA utilization will be similar to those of other comparable cardiac imaging modalities and that there would be a significant increase in appropriate use post AUC publication. METHODS: We employed a retrospective cohort study design of 2577 consecutive patients undergoing CCTA between January 1, 2012 and December 30, 2016. An appropriateness category was assigned for each CCTA. Appropriateness classifications were compared pre- and post- AUC publication via the chi-square test. RESULTS: Overall, 83.5% of CCTAs were deemed to be appropriate based on the AUC. Before the AUC publication, 75.0% of CCTAs were classified as appropriate whereas after the AUC publication, 88.0% were classified as appropriate (p < 0.001). The increase in appropriate utilization, when extrapolated to the Medicare population of the United States, was associated with potential cost savings of approximately $57 million per year. CONCLUSIONS: We report a high rate of appropriate use of CCTA and a significant increase in the proportion of CCTAs classified as appropriate after the AUC publication.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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".