Epidural Analgesia Reduces Postoperative Myocardial Infarction: A Meta-Analysis
Bibliographic record
Abstract
UNLABELLED: Postoperative cardiac morbidity and mortality continue to pose considerable risks to surgical patients. Postoperative epidural analgesia is considered to have beneficial effects on cardiac outcomes. The use in high-risk cardiac patients remains controversial. No study has shown that postoperative epidural analgesia decreases postoperative myocardial infarction (PMI) or death. All studies are underpowered to show such a result, and the cost of conducting a large trial is prohibitive. We performed a metaanalysis to determine whether postoperative epidural analgesia continued for more than 24 h after surgery reduces PMI or in-hospital death. The available databases were searched for randomized controlled trials of epidural analgesia that was extended at least 24 h into the postoperative period. The search yielded 17 studies, of which 11 were randomized controlled trials comprising 1173 patients. Metaanalysis was conducted by using the fixed-effects model, calculating both an odds ratio and a rate difference. Postoperative epidural analgesia resulted in better analgesia for the first 24 h after surgery. The rate of PMI was 6.3%, with lower rates in the Epidural group (rate difference, -3.8%; 95% confidence interval [CI] -7.4%, -0.2%; P = 0.049). The frequency of in-hospital death was 3.3%, with no significant difference between Epidural and Nonepidural groups (rate difference, -1.3%; 95% CI, -3.8%, 1.2%, P = 0.091). Subgroup analysis of postoperative thoracic epidural analgesia showed a significant reduction in PMI in the Epidural group (rate difference, -5.3%; 95% CI, -9.9%, -0.7%; P = 0.04). IMPLICATIONS: Postoperative epidural analgesia, especially thoracic epidural analgesia, continued for more than 24 h reduces postoperative myocardial infarctions.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.044 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".