Mortality and Readmission of Patients With Heart Failure, Atrial Fibrillation, or Coronary Artery Disease Undergoing Noncardiac Surgery
Bibliographic record
Abstract
BACKGROUND: The postoperative risks for patients with coronary artery disease (CAD) undergoing noncardiac surgery are well described. However, the risks of noncardiac surgery in patients with heart failure (HF) and atrial fibrillation (AF) are less well known. The purpose of this study is to compare the postoperative mortality of patients with HF, AF, or CAD undergoing major and minor noncardiac surgery. METHODS AND RESULTS: Population-based data were used to create 4 cohorts of consecutive patients with either nonischemic HF (NIHF; n=7700), ischemic HF (IHF; n=12 249), CAD (n=13 786), or AF (n=4312) who underwent noncardiac surgery between April 1, 1999, and September 31, 2006, in Alberta, Canada. The main outcome was 30-day postoperative mortality. The unadjusted 30-day postoperative mortality was 9.3% in NIHF, 9.2% in IHF, 2.9% in CAD, and 6.4% in AF (each versus CAD, P<0.0001). Among patients undergoing minor surgical procedures, the 30-day postoperative mortality was 8.5% in NIHF, 8.1% in IHF, 2.3% in CAD, and 5.7% in AF (P<0.0001). After multivariable adjustment, postoperative mortality remained higher in NIHF, IHF, and AF patients than in those with CAD (NIHF versus CAD: odds ratio 2.92; 95% confidence interval 2.44 to 3.48; IHF versus CAD: odds ratio 1.98; 95% confidence interval 1.70 to 2.31; AF versus CAD: odds ratio 1.69; 95% confidence interval 1.34 to 2.14). CONCLUSIONS: Although current perioperative risk prediction models place greater emphasis on CAD than HF or AF, patients with HF or AF have a significantly higher risk of postoperative mortality than patients with CAD, and even minor procedures carry a risk higher than previously appreciated.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".