Abstract 324: Use of Troponin Testing After Non-cardiac Surgery in Ontario
Bibliographic record
Abstract
Background: Myocardial infarction after non-cardiac surgery is common and associated with worse patient outcomes. In 2017, the Canadian Cardiovascular Society (CCS) published guidelines endorsing postoperative cardiac troponin surveillance in higher-risk patients having non-cardiac surgery. The objective of this study was to evaluate the proportion of non-cardiac surgery patients recommended for post-operative troponin testing and use of troponin testing in accordance with this guideline. Methods: We conducted a retrospective observational study of patients aged 40-105 years having moderate to high risk non-cardiac surgery in Ontario, Canada from January 1, 2010 to December 31, 2017. Classes of surgeries included orthopedics, gynecology, general, urology, vascular, and thoracic. Recommendations for troponin testing was determined by CCS criteria. Troponin testing within 2 days of the surgery was ascertained using the Ontario Laboratory Information System. Results: There were 268,269 patients in the cohort recommended for troponin testing during the study period. Mean age was 66.7 ± 11.9 years, 58.2% were female, and 12.3% underwent urgent surgery. According to CCS guidelines, 72.4% of elective surgery patients and 81.2% of urgent surgery patients would be recommended for post-operative troponin screening. The observed testing rate was 10.5% for elective patients and 26.4% for urgent surgery patients. Observed rates of testing for CCS recommended patients varied significantly by surgery: 5.5% for hysterectomies to 64.0% for open abdominal aortic aneurism repair (see Figure). Conclusions: Based on the current CCS guidelines, the majority of patients undergoing moderate to high-risk surgery should receive troponin testing. However, testing rates in Ontario were substantially lower with significant variations based on the type of surgery. The implication for routine troponin testing recommendation is substantial given the low utilization of troponin testing.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".