Cardiac risk assessment after noncardiac surgery: a historical cohort study on guideline adherence at a Canadian quaternary care centre
Bibliographic record
Abstract
PURPOSE: Cardiac complications after noncardiac surgery remain a leading source of postoperative morbidity and mortality. In 2016, the Canadian Cardiovascular Society (CCS) published guidelines that outlined an approach to perioperative cardiac risk assessment for noncardiac surgery, integrating biomarkers. We sought to evaluate the adherence to these guidelines at McGill University Health Centre, a quaternary care hospital in Montreal, QC, Canada. METHODS: We conducted a historical cohort study of all patients undergoing elective noncardiac surgery requiring overnight stay between January 2018 and December 2019. The primary outcome was adherence to preoperative B-type natriuretic peptide (BNP) measurement. Secondary outcomes included adherence to postoperative troponin and electrocardiogram (ECG) acquisition, and 30-day postoperative outcomes. RESULTS: Among our cohort of 3,623 patients, BNP measurement adherence was 52.4%. Troponin and ECG acquisition adherence was 34.6% and 30.5%, respectively. Patients with an elevated preoperative BNP had higher incidences of 30-day myocardial injury after noncardiac surgery (20.2% vs 4.3%; P < 0.001), myocardial infarction (5.2% vs 0.5%; P < 0.001), mortality (2.5% vs 0.6%; P < 0.001), and to a lesser extent, cardiac arrest and heart failure decompensation. Patients with elevated postoperative troponin levels had higher incidences of 30-day myocardial infarction (20.7% vs 0.0%; P < 0.001), mortality (7.8% vs 0.6%; P < 0.001), and to a lesser extent, cardiac arrest and heart failure decompensation. Elevated BNP and troponin levels were associated with higher physician follow-up rates. CONCLUSIONS: About half of the patients undergoing noncardiac surgery in our cohort underwent BNP screening as recommended by CCS guidelines; troponin and ECG acquisition adherence was even lower. While postoperative cardiac ischemia is associated with increased 30-day morbidity and mortality, more studies exploring physician risk stratification practice and the impact of increased testing on long-term outcomes are needed.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".