Low-value preoperative cardiac testing before low-risk surgical procedures: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Choosing Wisely Canada (CWC) recommends avoiding noninvasive advanced cardiac testing (e.g., exercise stress testing [EST], echocardiography and myocardial perfusion imaging [MPI]) for preoperative assessment in patients scheduled to undergo low-risk noncardiac surgery. In this study, we assessed the temporal trends in testing, overlapping with the introduction of the CWC recommendations in 2014, and patient and provider factors associated with low-value testing. METHODS: In this population-based retrospective cohort study, we used linked health administrative data in Alberta, Canada, to identify adult patients who underwent elective noncardiac surgery between Apr. 1, 2011, and Mar. 31, 2019, who had preoperative noninvasive advanced cardiac tests (EST, echocardiography or MPI) within 6 months before surgery. We included electrocardiography as an exploratory outcome. We excluded patients at high risk using the Revised Cardiac Risk Index (score ≥ 1 considered to indicate high risk), and modelled patient and temporal factors associated with the number of tests. RESULTS: We identified 1 045 896 elective noncardiac operations performed in 798 599 patients and 25 599 advanced preoperative cardiac tests; 2.1% of operations were preceded by advanced cardiac testing. The incidence of testing increased over the study period, and, by 2018/19, patients were 1.3 times (95% confidence interval 1.2-1.4) more likely to receive a preoperative advanced test compared to 2011/12. Urban patients were more likely to receive a preoperative advanced cardiac test than their rural counterparts. Electrocardiography was the most common preoperative cardiac test, preceding 182 128 procedures (17.4%). INTERPRETATION: Preoperative advanced cardiac testing was infrequent in adult Albertans who underwent low-risk elective noncardiac operations. Despite CWC recommendations, the use of some tests appears to be increasing, and there was substantial variation across geographic areas.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".