Association Between Hospital Postoperative Troponin Use and Patient Outcomes After Vascular Surgery
Bibliographic record
Abstract
BACKGROUND: Acute myocardial injury after noncardiac surgery, which is most often symptomatically silent, is associated with increased mortality and morbidity. However, it is not known if routine postoperative troponin testing will affect patient outcomes. METHODS: We assembled a cohort of patients who underwent carotid endarterectomy or abdominal aortic aneurysm repair in Ontario, Canada, from 2010 to 2017. Hospitals were categorized into high, medium, and low troponin testing intensity based on the proportion of patients who received postoperative troponin testing. Cox proportional hazards modeling was used to assess the association between hospital-specific testing intensity and 30-day and 1-year major adverse cardiovascular events (MACEs) while adjusting for patient-, surgery-, and hospital-level factors. RESULTS: The cohort consisted of 18,467 patients from 17 hospitals. Mean age was 72 years, and 74.0% were men. Rates of postoperative troponin testing were 77.5%, 35.8%, and 21.6% in the high-, medium-, and low-testing intensity hospitals, respectively. At 30 days, 5.3%, 5.3%, and 6.5% of patients in high-, medium-, and low-testing intensity hospitals experienced MACE, respectively. Higher troponin testing rate was associated with lower adjusted hazard ratios (HRs) for MACE at 30 days (0.94; 95% confidence interval [CI], 0.89-0.98) and at 1 year (0.97; 95% CI, 0.94-0.99) for each 10% increase in hospital troponin rate. Hospitals with high-testing intensity had higher rates of postoperative cardiology referrals, cardiovascular testing, and rates of new cardiovascular prescriptions. CONCLUSIONS: Patients undergoing vascular surgery at hospitals with higher postoperative troponin testing intensity experienced fewer adverse outcomes than patients who had surgery at hospitals with lower testing intensity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".