Pharmacotherapy for the Prevention of Chronic Pain after Surgery in Adults: An Updated Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Chronic postsurgical pain can severely impair patient health and quality of life. This systematic review update evaluated the effectiveness of systemic drugs to prevent chronic postsurgical pain. METHODS: The authors included double-blind, placebo-controlled, randomized controlled trials including adults that evaluated perioperative systemic drugs. Studies that evaluated same drug(s) administered similarly were pooled. The primary outcome was the proportion reporting any pain at 3 or more months postsurgery. RESULTS: The authors identified 70 new studies and 40 from 2013. Most evaluated ketamine, pregabalin, gabapentin, IV lidocaine, nonsteroidal anti-inflammatory drugs, and corticosteroids. Some meta-analyses showed statistically significant-but of unclear clinical relevance-reductions in chronic postsurgical pain prevalence after treatment with pregabalin, IV lidocaine, and nonsteroidal anti-inflammatory drugs. Meta-analyses with more than three studies and more than 500 participants showed no effect of ketamine on prevalence of any pain at 6 months when administered for 24 h or less (risk ratio, 0.62 [95% CI, 0.36 to 1.07]; prevalence, 0 to 88% ketamine; 0 to 94% placebo) or more than 24 h (risk ratio, 0.91 [95% CI, 0.74 to 1.12]; 6 to 71% ketamine; 5 to 78% placebo), no effect of pregabalin on prevalence of any pain at 3 months (risk ratio, 0.88 [95% CI, 0.70 to 1.10]; 4 to 88% pregabalin; 3 to 80% placebo) or 6 months (risk ratio, 0.78 [95% CI, 0.47 to 1.28]; 6 to 68% pregabalin; 4 to 69% placebo) when administered more than 24 h, and an effect of pregabalin on prevalence of moderate/severe pain at 3 months when administered more than 24 h (risk ratio, 0.47 [95% CI, 0.33 to 0.68]; 0 to 20% pregabalin; 4 to 34% placebo). However, the results should be interpreted with caution given small study sizes, variable surgical types, dosages, timing and method of outcome measurements in relation to the acute pain trajectory in question, and preoperative pain status. CONCLUSIONS: Despite agreement that chronic postsurgical pain is an important topic, extremely little progress has been made since 2013, likely due to study designs being insufficient to address the complexities of this multifactorial problem.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.026 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".