Search

Network meta-analysis has emerged as a method for analysing clinical trials, with a large increase in the number of publications over the past decade. Network meta-analysis offers advantages over traditional pairwise meta-analysis, including increased power, the ability to compare treatments not compared in the original trials, and the ability to rank treatments. However, network meta-analyses are inherently more complex than pairwise meta-analyses, requiring additional statistical expertise and assumptions. Many factors can affect the certainty of evidence from pairwise meta-analysis and can often lead to unreliable results. Network meta-analysis is prone to all these issues, although it has the additional assumption of transitivity. Here we review network meta-analyses, problems with their conduct and reporting, and methodological strategies that can be used by those conducting reviews to help improve the reliability of their findings. We provide evidence that violation of the assumption of transitivity is relatively common and inadequately considered in published network meta-analyses. We explain key concepts with clinically relevant examples for those unfamiliar with network meta-analysis to facilitate their appraisal and application of their results to clinical practice., Competing Interests: Declaration of interest JH is associate editor-in-chief of the British Journal of Anaesthesia. The content of this article represents the experience of the authors and a presentation of the reviewed evidence. It does not represent the policy of the British Journal of Anaesthesia., (Copyright © 2025 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: Chronic postsurgical pain is common after surgery. Identification of non-opioid analgesics with potential for preventing chronic postsurgical pain is important, although trials are often underpowered. Network meta-analysis offers an opportunity to improve power and to identify the most promising therapy for clinical use and future studies., Methods: We conducted a PRISMA-NMA-compliant systematic review and network meta-analysis of randomised controlled trials of non-opioid analgesics for chronic postsurgical pain. Outcomes included incidence and severity of chronic postsurgical pain, serious adverse events, and chronic opioid use., Results: We included 132 randomised controlled trials with 23 902 participants. In order of efficacy, i.v. lidocaine (odds ratio [OR] 0.32; 95% credible interval [CrI] 0.17-0.58), ketamine (OR 0.64; 95% CrI 0.44-0.92), gabapentinoids (OR 0.67; 95% CrI 0.47-0.92), and possibly dexmedetomidine (OR 0.36; 95% CrI 0.12-1.00) reduced the incidence of chronic postsurgical pain at ≤6 months. There was little available evidence for chronic postsurgical pain at >6 months, combinations agents, chronic opioid use, and serious adverse events. Variable baseline risk was identified as a potential violation to the network meta-analysis transitivity assumption, so results are reported from a fixed value of this, with analgesics more effective at higher baseline risk. The confidence in these findings was low because of problems with risk of bias and imprecision., Conclusions: Lidocaine (most effective), ketamine, and gabapentinoids could be effective in reducing chronic postsurgical pain ≤6 months although confidence is low. Moreover, variable baseline risk might violate transitivity in network meta-analysis of analgesics; this recommends use of our methods in future network meta-analyses., Systematic Review Protocol: PROSPERO CRD42021269642., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Systematic reviews and meta-analyses (SRMAs) are increasing in popularity, but should they be used to inform clinical decision-making in anaesthesia? We present evidence that the certainty of evidence from SRMAs in anaesthesia (and in general) may be unacceptably low because of risks of bias exaggerating treatment effects, unexplained heterogeneity reducing certainty in estimates, random errors, and widespread prevalence of publication bias. We also present the latest methodological advances to help improve the certainty of evidence from SRMAs. The target audience includes both review authors and practising clinicians to help with SRMA appraisal. Issues discussed include minimising risks of bias from included trials, trial sequential analysis to reduce random error, updated methods for presenting effect estimates, and novel publication bias tests for commonly used outcome measures. These methods can help to reduce spurious conclusions on clinical significance, explain statistical heterogeneity, and reduce false positives when evaluating small-study effects. By reducing concerns in these domains of Grading of Recommendations, Assessment, Development and Evaluation, it should help improve the certainty of evidence from SRMAs used for decision-making in anaesthesia, pain, and perioperative medicine., (Copyright © 2021 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved.)