Appropriate utilization of cardiac magnetic resonance for the assessment of heart failure and potential associated cost savings
Bibliographic record
Abstract
BACKGROUND: The rapid growth in cardiac imaging utilization has led to the development of appropriate use criteria (AUC) in an effort to control costs. Recently, cardiac MRI has developed into a valuable modality in the evaluation of cardiac disease. However, there are no studies examining the appropriate use of cardiac MRI in clinical practice. PURPOSE: To determine the appropriate utilization of cardiac MRI in a large quaternary care institution and to compare percentages of appropriate utilization pre- and postpublication of the AUC document. We hypothesized that percentages of appropriate cardiac MRI utilization will be similar to those of other comparable cardiac imaging modalities and that there would be a significant change in appropriate use pre- and post-AUC publication. STUDY TYPE: Retrospective cohort study. POPULATION: In all, 2032 consecutive patients undergoing cardiac MRI for the assessment of heart failure between 2012-2016. FIELD STRENGTH: 1.5T. ASSESSMENT: Data were collected and an appropriateness category was assigned for each cardiac MRI. STATISTICAL TESTS: Rates of major cardiac risk factors were compared between those undergoing cardiac MRIs pre- and post-AUC using the chi-square and the Mann-Whitney tests for categorical and continuous variables, respectively. Appropriateness classification was compared pre- and post-AUC publication using the chi-square test. RESULTS: There were no significant differences in the prevalence of major cardiovascular risk factors before and after publication of the AUC. 95.5% of all cardiac MRIs were appropriate based on the AUC. Further, there was a significant difference when comparing the appropriateness classification before and after publication of the AUC (P = 0.0003), potentially associated with annual cost savings of ∼$14.8 million. DATA CONCLUSION: We report a very high percentage of appropriate use of cardiac MRI and a significant increase in the proportion of tests classified as appropriate after AUC publication. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2019;49:e132-e138.
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 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 | 0.007 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".