Safety and Efficacy of Fast Track in Patients Undergoing Coronary Artery Bypass Surgery
Bibliographic record
Abstract
BACKGROUND: The incidence of coronary artery bypass surgery has been increasing annually with increasing pressure on the health care system. Fast track has been proposed as a means to increase efficiency and volume, without an increase in hospital resources. To date this approach has not been critically assessed in Canada. METHODS: We examined 617 consecutive patients undergoing isolated CABG surgery. The patients were divided into (1) fast track (FT) recovery (n = 219), without admission to an ICU, and (2) non-fast track (NFT) recovery (n = 398) with direct admission to the ICU. There were no differences in age, gender, timing of surgery, left main stenosis, preoperative myocardial infarction, renal failure, diabetes, peripheral vascular disease, or in the incidence of chronic obstructive pulmonary disease between the two groups. The NFT group had a higher proportion of patients with NYHA Class III/IV symptoms preoperatively (65.7% vs. 57.3%, p = 0.048), in patients with an ejection fraction < 40% (42.5% vs. 30.6%, p = 0.004), or in the number of individuals with an IABP inserted before surgery (13 vs. 1, p < 0.001). RESULTS: In the FT group the average period of aortic occlusion (40.7 +/- 15.2 min vs. 71.8 +/- 26.5 min, p < 0.001) and perfusion time (67.8 +/- 24.5 min vs. 117.5 +/- 40.2 min, p < 0.001) were significantly less than in the NFT group. The number of grafts per patient was 3.3 +/- 1.0 vs. 3.2 +/- 1.0, respectively (p = 0.38). Operative mortality was 0.9% in the FT group and 1.3% in the NFT group (p = 1.0). Significant differences were seen in the proportion of patients that suffered from postoperative ventilatory failure (3.2% in FT vs. 12.1% in NFT, p < 0.001), and the proportion of patients that suffered any postoperative complication was significantly higher in the NFT group (21.4%) than in the FT group (9.1%, p < 0.001). The differences in postoperative complications resulted in a shorter length of stay (LOS) in FT patients (5.6 +/- 4.1 days vs. 9.7 +/- 9.4 days NFT, p < 0.001). Only 4.1% of patients that entered the FT group failed and required admission to the ICU. Multivariate stepwise logistic regression analysis identified non-fast track recovery as an independent predictor of morbidity in CABG surgery patients. CONCLUSIONS: The data indicate it is possible to perform isolated CABG surgery, in a large proportion of the population, without the need for admission to an ICU for postoperative care.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".