Component network meta-analysis identifies the most effective components of psychological preparation for adults undergoing surgery under general anesthesia

Abstract

Objectives: To apply component network meta-analysis (CNMA) models to an existing Cochrane review of psychological preparation interventions for adults undergoing surgery and to extend the models to account for covariates to identify the most effective components for improving postoperative outcomes.

Study design and setting: Interventions consisted of between one and four components of psychological preparation: procedural information (P), sensory information (S), behavioral instruction (B), cognitive interventions (C), relaxation (R), and emotion-focused techniques (E). We used CNMA models to assess the effect of each component for three outcomes: length of stay, pain, and negative affect.

Results: We found evidence that the most effective component for reducing length of stay depends on the type of surgery and that R may improve pain. There was insufficient evidence that individual components contributed to the overall reduction in negative affect, but P and S emerged as the most likely beneficial components. Overall, we were unable to identify any one component as the most effective across all three outcomes.

Conclusion: The CNMA method allowed us to address questions about the effects of specific components that could not be answered using standard Cochrane methodology.

Keywords: Bayesian; Complex interventions; Component; Network meta-analysis; Psychological preparation; Surgery.