Volatile versus total intravenous anesthesia for 30-day mortality following non-cardiac surgery in patients with preoperative myocardial injury
Bibliographic record
Abstract
We evaluated whether volatile anesthetics can improve the postoperative outcomes of non-cardiac surgery in patients with preoperative myocardial injury defined by the cardiac troponin elevation. From January 2010 to June 2018, 1254 adult patients with preoperative myocardial injury underwent non-cardiac surgery under general anesthesia and were enrolled in this study. Patients were stratified into following two groups according to anesthetic agents; 115 (9.2%) patients whose anesthesia was induced and maintained with continuous infusion of propofol and remifentanil (TIVA group) and 1139 (90.8%) patients whose anesthesia was maintainted with volatile anesthetics (VOLATILE group). The primary outcome was 30-day mortality. To diminish the remifentanil effect, a further analysis was conducted after excluding the patients who received only volatile anesthetics without remifentanil infusion. In a propensity-score matched analysis, 30-day mortality was higher in the TIVA group than the VOLATILE group (17.0% vs. 9.1%; hazard ratio [HR] 2.60; 95% confidence interval [CI], 1.14-5.93; p = 0.02). In addition, the TIVA group showed higher 30-day mortality than the VOLATILE group, even after eliminating the effect of remifentanil infusion (15.8% vs. 8.3%; HR 4.62; 95% CI, 1.82-11.74; p = 0.001). In our study, the use of volatile anesthetics showed the significant survival improvement after non-cardiac surgery in patients with preoperative myocardial injury, which appears to be irrelevant to the remifentanil use. Further studies are needed to confirm this beneficial effect of volatile anesthetics. Clinical trial number and registry URL: KCT0004349 (www.cris.nih.go.kr).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".