Utilization and Costs of Noninvasive Cardiac Tests After Acute Coronary Syndromes: Insights From the Alberta COAPT Study
Bibliographic record
Abstract
Background Although appropriate noninvasive cardiac tests (NICTs) after an acute coronary syndrome (ACS) provide useful prognostic information, inappropriate use leads to inefficient expenditure of existing healthcare resources. By using the Alberta Co ntemporary A cute Coronary Syndrome P atient Invasive T reatment Strategies (COAPT) Registry, we evaluated the use and costs of NICTs among patients discharged within 1 year after ACS. Methods All patients discharged from the hospital with a primary diagnosis of ACS in Alberta between 2004/2005 and 2015/2016 were included. Frequency of NICTs (stress tests [± imaging] and nonstress imaging tests) was determined from linked provincial databases. Costs were obtained from the Alberta Health Care Insurance Plan Medical Procedure List. Results Of 55,516 patients with ACS, 30,760 had at least 1 NICT (55.4%), with 13,505 (24.3%) having > 1 NICT performed within 1 year. Temporal trends of NICT increased over time (stress tests: P trend < 0.001; nonstress imaging tests: P trend < 0.001). NICT most commonly occurred within the first 4 months after hospital discharge (stress tests at 2 months; nonstress imaging tests at 3-4 months). In 2015/2016, the total estimated costs of NICT were $1.35M, a 22.4% increase from 2004/2005 (1.10M) ( P < 0.001), whereas a decrease in incidence of ACS over the same time period was noted ( P = 0.008). Conclusions Rates of NICT 1 year after ACS are high and increasing over time. Estimated costs of NICT appear to be escalating out of proportion to the ACS growth. Further investigation is warranted because it is speculative whether the increase in NICT and costs results in clinical benefit after ACS.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".