A meta-analysis of gabapentin and multimodal analgesics

Abstract

Multimodal analgesia has been proposed as a useful strategy to reduce postoperative pain while decreasing opioid consumption and thus opioid adverse events. Gabapentin is one such agent although previous results have been heterogeneous. This thesis aimed to review randomised controlled trials of gabapentin for reducing pain, opioid adverse effects and the haemodynamic response to intubation while attempted to predict clinical effectiveness from these trials using meta-regression. Extending this principle, we evaluated other multimodal analgesic agents to identify whether heterogeneity could be explained by various clinical and methodological covariates. Our gabapentin review included 133 randomised controlled trials and demonstrated its efficacy in reducing pain scores, opioid consumption and opioid adverse events such as nausea, vomiting and pruritus. However, gabapentin increased the risk of sedation. Gabapentin was effective at reducing the haemodynamic response to intubation in 29 randomised controlled trials although trials failed to report on clinically relevant outcomes. Gabapentin exhibited no pre-emptive analgesic effect in 4 randomised controlled trials. There was evidence of considerable statistical heterogeneity on meta-analysis of gabapentin for pain scores and 24-hour morphine consumption. Meta-regression analysis showed however that baseline risk predicted the majority of the heterogeneity between studies. Extending this approach to other multimodal analgesics from 344 randomised controlled trials; we demonstrated this was true for analgesic agents in general. In addition to baseline risk, methodological limitations, especially inadequate allocation concealment, explained some of the residual heterogeneity. There was evidence of funnel plot asymmetry for most analgesic agents, suggesting publication bias. However, this may be a product of trials with higher baseline risk having larger standard errors, rather than true publication bias. Indeed, when we simulated meta-analyses with no publication bias, with both effect size and standard deviations dependent on baseline risk, funnel plot asymmetry was still evident (p<0.001). Therefore, conventional funnel plots may be an unsuitable method of detecting publication bias where baseline risk predicts between-study heterogeneity. We present an alternative method using meta-regression residuals that corrects funnel plot asymmetry in the presence of no publication bias. Finally, due to concerns that methodological limitations exaggerated effect estimates, we used trial sequential analysis to determine whether sufficient low risk of bias evidence exists to reject type I and type II errors in the analyses of analgesic adjuncts. We demonstrated there is currently insufficient evidence from low risk of bias trials to be confident of the efficacy of the majority of analgesic adjuncts