Meta-Analysis Demonstrates Statistically Significant Reduction in Postoperative Myocardial Infarction with the Use of Thoracic Epidural Analgesia
Bibliographic record
Abstract
To the Editor: We read with interest the secondary analysis of the MASTER trial in which the authors found no significant improvement in major morbidity or mortality after major abdominal surgery from perioperative epidural analgesia (1). The authors comment that their results are at odds with the results of previous meta-analyses (2–4). We wish to clarify some questions raised by Peyton et al. regarding our meta-analysis (2). All patients, including those who died in the first 24 h after surgery, were included in our analysis. Funnel plotting suggested an absence of publication bias. Furthermore, we examined the effect of known comorbidities on the outcome. Thoracic epidural analgesia reduced postoperative myocardial infarction (MI) despite a higher proportion of patients with angina, previous MI, use of beta-blockers, or chronic obstructive pulmonary disease in the group receiving epidural analgesia. Four randomized controlled trials (RCTs) (5–8), which compared postoperative epidural analgesia with systemic opioid analgesia, were published after our meta-analysis. We have updated our meta-analysis on postoperative MI with the results from three of the RCTs (5–7) in which data were available (Fig. 1). Our meta-analysis continues to demonstrate statistically significant reduction in postoperative MI with the use of thoracic epidural analgesia (odds ratio 0.60; 95% confidence interval 0.37, 0.96; P = 0.03).Figure 1: The effect of postoperative epidural analgesia on postoperative myocardial infarction. CI = confidence interval, OR = odds ratio.The MASTER Trial was powered to detect an absolute risk reduction of 10% (or a relative risk reduction of 20%) with a control event rate of 50%, a type I error rate of 5%, and a power of only 80% for the combined outcome of mortality and major postoperative morbidity (8). Unfortunately, with the exception of the outcome “at least one morbid end point,” the rates of all postoperative complications were less than 50% in the control group (1). The MASTER Trial appears underpowered to detect clinically significant differences in important postoperative outcomes. For example, to detect a 20% relative risk reduction (or increase) in cardiovascular events with 5% type I error rate, 80% power, and the control event rate seen in the MASTER Trial (24%), a sample size of 2,388 subjects is needed. Peyton et al. have pointed out that large RCTs may reach different conclusions from meta-analyses of small RCTs addressing the same clinical question. In such instances, the estimate of treatment effect from the large RCT would be more accurate than the estimate from the meta-analysis. We agree with the authors’ concerns with the limitations of meta-analysis. However, with regards to specific postoperative complications such as pneumonia or MI, the definitive answer to the question of benefit (or harm) of perioperative epidural analgesia remains to be found. A much larger RCT is needed. W. Scott Beattie, MD, PhD Neal H. Badner, MD Peter T-L. Choi, MD, MSc(Epid)
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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 | 0.017 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".