Suzanne C Freeman1, Neil W Scott2, Rachael Powell3, Marie Johnston2, Alex J Sutton4, Nicola J Cooper4. 1. NIHR Complex Reviews Support Unit, Biostatistics Research Group, Department of Health Sciences, University of Leicester, Leicester, UK; Medical Statistics Team, Division of Applied Health Sciences, University of Aberdeen, Aberdeen, UK. Electronic address: suzanne.freeman@leicester.ac.uk. 2. Medical Statistics Team, Division of Applied Health Sciences, University of Aberdeen, Aberdeen, UK. 3. Manchester Centre for Health Psychology and School of Health Sciences, University of Manchester, Manchester, UK. 4. NIHR Complex Reviews Support Unit, Biostatistics Research Group, Department of Health Sciences, University of Leicester, Leicester, UK.
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.
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.
Authors: Jennifer K Burton; Louise Craig; Shun Qi Yong; Najma Siddiqi; Elizabeth A Teale; Rebecca Woodhouse; Amanda J Barugh; Alison M Shepherd; Alan Brunton; Suzanne C Freeman; Alex J Sutton; Terry J Quinn Journal: Cochrane Database Syst Rev Date: 2021-11-26
Authors: Danielle Roberts; Lawrence Mj Best; Suzanne C Freeman; Alex J Sutton; Nicola J Cooper; Sivapatham Arunan; Tanjia Begum; Norman R Williams; Dana Walshaw; Elisabeth Jane Milne; Maxine Tapp; Mario Csenar; Chavdar S Pavlov; Brian R Davidson; Emmanuel Tsochatzis; Kurinchi Selvan Gurusamy Journal: Cochrane Database Syst Rev Date: 2021-04-10
Authors: Maria Corina Plaz Torres; Lawrence Mj Best; Suzanne C Freeman; Danielle Roberts; Nicola J Cooper; Alex J Sutton; Davide Roccarina; Amine Benmassaoud; Laura Iogna Prat; Norman R Williams; Mario Csenar; Dominic Fritche; Tanjia Begum; Sivapatham Arunan; Maxine Tapp; Elisabeth Jane Milne; Chavdar S Pavlov; Brian R Davidson; Emmanuel Tsochatzis; Kurinchi Selvan Gurusamy Journal: Cochrane Database Syst Rev Date: 2021-03-30
Authors: Jennifer K Burton; Louise E Craig; Shun Qi Yong; Najma Siddiqi; Elizabeth A Teale; Rebecca Woodhouse; Amanda J Barugh; Alison M Shepherd; Alan Brunton; Suzanne C Freeman; Alex J Sutton; Terry J Quinn Journal: Cochrane Database Syst Rev Date: 2021-07-19