Abstract 12738: Comparing Costs of Non-Invasive Cardiac Diagnostic Tests and Deferred Testing for Incident Chest Pain - A Population-Based Study
Bibliographic record
Abstract
Introduction: Cardiac non-invasive diagnostic tests (NIT) for patients with suspected coronary artery disease cost > $3 billion annually in the US, and may be overused. Consequently, comparing costs of different NIT strategies, including deferred testing, is of urgent importance to healthcare planning. Methods: We compared population-based downstream costs between patients undergoing evaluation for chest pain in Ontario, CA with one of four NIT tests (exercise stress testing (GXT), stress echocardiography, cardiac computed tomography angiography (CCTA) and myocardial perfusion imaging (MPI)) as well as no-testing. To compare costs among the tested and non-tested groups, we used a log-gamma generalized linear model to account for the skewed distribution of health care costs, adjusting for relevant clinical covariates. Results: Of 2,340,699 included patients, 481,170 (21%) received one of four NITs: GXT: 254,492 (53%), MPI: 154,137 (32%), stress echo: 69,160 (14%), and CCTA: 3,381 (<1%). After adjustment for patient characteristics including cardiac risk factors, frailty and location (Table 1), receipt of any NIT was associated with a 12% reduction in downstream 1-year mean costs compared to those without an NIT (cost ratio 0.88, 95%CI 0.87, 0.89). Comparing the different testing strategies with no testing, both GXT (cost ratio 0.80, 95%CI 0.79-0.81) and stress echocardiography (cost ratio 0.82, 95%CI 0.81-0.83) had lower downstream costs, while both MPI (cost ratio 1.26, 95% CI 1.25, 1.27) and CCTA (cost ratio 1.29, 95% CI 1.23, 1.35) had higher downstream costs. Conclusions: In a large (>2 million) population-based cohort with incident chest pain, receipt of any type of non-invasive testing was associated with a 12% reduction in downstream costs compared to no testing. GXT and stress echocardiography had the least downstream costs, whereas CCTA and MPI had the highest costs. These findings may help inform testing decisions in chest pain patients.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".