Abstract 14288: Substantial Hospital-level Variation in Troponin Testing After Non-cardiac Surgery
Bibliographic record
Abstract
Introduction: There is an increasing emphasis in detecting postoperative myocardial infarction (PMI) using routine troponin testing after non-cardiac surgery. However, clinical practice guidelines vary considerably in their recommendations. We conducted a population-based cohort study in Ontario, Canada to assess the degree of hospital-level variation associated with troponin testing after three commonly performed surgeries. Methods: We conducted a retrospective cohort study of adults (40-105 y) undergoing major orthopedic, colorectal, or vascular surgery in Ontario, Canada from January 1, 2010 to December 31, 2017. Hierarchical logistic regression modeling was used to model the association of patient, surgery, and hospital factors with postoperative troponin testing, while accounting for clustering at the hospital level. Results: We identified 176,454 eligible patients. Canadian Cardiovascular Society guidelines recommended troponin testing for 73.5%, 90.8% and 95.6% of orthopedic, colorectal, and vascular surgery patients respectively, but only 6.7%, 16.6%, and 50.2% were actually tested. Inter hospital variation in testing rates was considerable for the three surgeries (Figure; 0-33%, 0-38% and 18-84%). Even after risk-adjustment, the median odds ratio for testing across hospitals was still 1.74, 1.63, and 2.65 for orthopedic, colorectal, and vascular surgery, respectively. This corresponded to intraclass correlation coefficients of 9.3%, 7.4%, and 24.2% respectively. Conclusion: Despite strong recommendations by Canadian guidelines for troponin testing after non-cardiac surgery, testing rates were low overall and varied significantly across hospitals.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".