Comparing interventions for early psychosis: a systematic review and component network meta-analysis

Background

'Early Intervention in Psychosis' (EIP) services have been associated with improved outcomes for early psychosis. However, these services are heterogeneous and many provide different components of treatment. The impact of this variation on the sustained treatment effects is unknown.

Methods

We performed a systematic review and component network meta-analysis (cNMA) of randomised controlled trials (RCTs) that compared specialised intervention services for early psychosis. We searched CENTRAL (published and unpublished), EMBASE, MEDLINE, CINAHL, PsycINFO and Web of Science from inception to February 2023. Primary outcomes were negative and positive psychotic symptoms at 3-month and 1-year follow-up and treatment dropouts. Secondary outcomes were depressive symptoms and social functioning at 1-year follow-up. We registered a protocol for our study in PROSPERO (CRD42017057420).

Findings

We identified 37 RCTs including 4599 participants. Participants' mean age was 25.8 years (SD 6.0) and 64.0% were men. We found evidence that psychological interventions (this component grouped all psychological treatment intended to treat, or ameliorate the consequences of, psychotic symptoms) are beneficial for reducing negative symptoms (iSMD -0.24, 95% CI -0.44 to -0.05, p = 0.014) at 3-month follow-up and may be associated with clinically relevant benefits in improving social functioning scores at 1-year follow-up (iSMD -0.52, 95% CI -1.05 to 0.01, p = 0.052). The addition of case management has a beneficial effect on reducing negative symptoms (iSMD -1.17, 95% CI -2.24 to -0.11, p = 0.030) and positive symptoms (iSMD -1.05, 95% CI -2.02 to -0.08, p = 0.033) at 1-year follow-up. Pharmacotherapy was present in all trial arms, meaning it was not possible to examine the specific effects of this component.

Interpretation

Our findings suggest psychological interventions and case management in addition to pharmacotherapy as the core components of services for early psychosis to achieve sustained clinical benefits. Our conclusions are limited by the small number of studies and sparsely connected networks.

Funding

National Institute for Health and Care Research.

Effectiveness of sound and darkness interventions for critically ill patients' sleep quality: A systematic review and component network meta-analysis

BACKGROUND

Noise and lighting are prime factors of poor sleep quality in critically ill patients, which impair recovery and increase the risk of delirium or complications.

AIM

To identify and rank the effectiveness of sound and darkness interventions on the sleep quality of critically ill patients.

STUDY DESIGN

This systematic review and component network meta-analysis was based on the Preferred Reporting Items for Systematic Reviews incorporating the Network Meta-Analyses (PRISMA-NMA) Statement. The Embase, MEDLINE, Cochrane CENTRAL, CINAHL, Airiti Library, and Google Scholar databases were searched from inception to August 10, 2021, for randomized controlled trials (RCTs) on sound and darkness interventions targeting critically ill patients' sleep quality. We applied standard and component NMA to determine the effects of interventions. The certainty of evidence was evaluated using the Cochrane risk-of-bias tool (V.2.0) and the online Confidence in Network Meta-Analysis (CINeMA) application.

RESULTS

Twenty-four RCTs with 1507 participants who used combined interventions constituting seven competing interventions were included in the standard NMA. The combination of earplugs, eye masks, and music; eye masks alone; earplugs combined with eye masks; and music alone had beneficial intervention effects. The combination of earplugs, eye masks, and music was the best intervention, and these components had no interaction effect. An eye mask had the best relative effect, followed by music, quiet time, and earplugs.

CONCLUSIONS

This study provides clinical evidence of the effectiveness of using eye masks, music, and earplugs to improve sleep quality in critically ill patients. We also recommend future research using bedtime music, nocturnal eye masks, and quiet time, which had the best relative effects on sleep quality.

RELEVANCE TO CLINICAL PRACTICE

This study provides recommendations for interventions that nurses can use to improve critically ill patients' sleep quality.

Data visualisation approaches for component network meta-analysis: visualising the data structure

BACKGROUND

Health and social care interventions are often complex and can be decomposed into multiple components. Multicomponent interventions are often evaluated in randomised controlled trials. Across trials, interventions often have components in common which are given alongside other components which differ across trials. Multicomponent interventions can be synthesised using component NMA (CNMA). CNMA is limited by the structure of the available evidence, but it is not always straightforward to visualise such complex evidence networks. The aim of this paper is to develop tools to visualise the structure of complex evidence networks to support CNMA.

METHODS

We performed a citation review of two key CNMA methods papers to identify existing published CNMA analyses and reviewed how they graphically represent intervention complexity and comparisons across trials. Building on identified shortcomings of existing visualisation approaches, we propose three approaches to standardise visualising the data structure and/or availability of data: CNMA-UpSet plot, CNMA heat map, CNMA-circle plot. We use a motivating example to illustrate these plots.

RESULTS

We identified 34 articles reporting CNMAs. A network diagram was the most common plot type used to visualise the data structure for CNMA (26/34 papers), but was unable to express the complex data structures and large number of components and potential combinations of components associated with CNMA. Therefore, we focused visualisation development around representing the data structure of a CNMA more completely. The CNMA-UpSet plot presents arm-level data and is suitable for networks with large numbers of components or combinations of components. Heat maps can be utilised to inform decisions about which pairwise interactions to consider for inclusion in a CNMA model. The CNMA-circle plot visualises the combinations of components which differ between trial arms and offers flexibility in presenting additional information such as the number of patients experiencing the outcome of interest in each arm.

CONCLUSIONS

As CNMA becomes more widely used for the evaluation of multicomponent interventions, the novel CNMA-specific visualisations presented in this paper, which improve on the limitations of existing visualisations, will be important to aid understanding of the complex data structure and facilitate interpretation of the CNMA results.

Model selection for component network meta-analysis in connected and disconnected networks: a simulation study

BACKGROUND

Network meta-analysis (NMA) allows estimating and ranking the effects of several interventions for a clinical condition. Component network meta-analysis (CNMA) is an extension of NMA which considers the individual components of multicomponent interventions. CNMA allows to "reconnect" a disconnected network with common components in subnetworks. An additive CNMA assumes that component effects are additive. This assumption can be relaxed by including interaction terms in the CNMA.

METHODS

We evaluate a forward model selection strategy for component network meta-analysis to relax the additivity assumption that can be used in connected or disconnected networks. In addition, we describe a procedure to create disconnected networks in order to evaluate the properties of the model selection in connected and disconnected networks. We apply the methods to simulated data and a Cochrane review on interventions for postoperative nausea and vomiting in adults after general anaesthesia. Model performance is compared using average mean squared errors and coverage probabilities.

RESULTS

CNMA models provide good performance for connected networks and can be an alternative to standard NMA if additivity holds. For disconnected networks, we recommend to use additive CNMA only if strong clinical arguments for additivity exist.

CONCLUSIONS

CNMA methods are feasible for connected networks but questionable for disconnected networks.

Modeling Multicomponent Interventions in Network Meta-Analysis

There is a rapid increase in trials assessing healthcare interventions consisting of a combination of drugs (polytherapies) or multiple components. In the latter type of interventions (also known as complex interventions), the aspect of complexity is of paramount importance. For example, nonpharmacological interventions, such as psychological interventions or self-management interventions, usually share common components that relate to the nature of intervention, who delivers it, or where and how. In a network of trials, there is often the need to identify the most effective (or safest) component and/or combination of components. Four key meta-analytical approaches have been presented in the literature to handle complex interventions. These include (a) the single-effect model, (b) the full interaction model, (c) the additive main effects model, and (d) the two-way interaction model. In this chapter, we present and discuss the advantages and limitations of these approaches. We illustrate these methods using a network that assesses the relative effects of self-management interventions on waist size in patients with type 2 diabetes.

Understanding the effectiveness and underlying mechanisms of lifestyle modification interventions in adults with learning disabilities: protocol for a mixed-methods systematic review

BACKGROUND

Adults with learning disabilities have an increased disposition to unhealthy lifestyle behaviours which often occur simultaneously. Existing studies focus on complex interventions targeting unhealthy diet, physical inactivity, sedentary behaviour, smoking, and alcohol use to reduce health risks experienced. It is essential to understand how well these interventions work, what works, for whom, in what context and why. This study aims to investigate the effectiveness and underlying mechanisms of lifestyle modification interventions for adults with learning disabilities.

METHODS

This is a mixed-methods systematic review consisting of a network meta-analysis (NMA) and realist synthesis. Electronic databases (ASSIA, CINAHL, EMBASE, MEDLINE, and PsycINFO) will be searched from inception to 14 January 2021 with no language restriction. Additionally, trial registries, grey literature databases and references lists will be searched. Studies related to lifestyle modification interventions on the adult population (>18 years) with learning disabilities will be eligible for inclusion. Two independent researchers will screen studies, extract data and assess its quality and risk of bias using the Cochrane Collaboration's Risk of Bias Assessment Tool (RoB Version 2) and ROBINS-I. The strength of the body of evidence will be assessed based on the GRADE approach. The NMA will incorporate results from RCTs and quasi-experimental studies to estimate the effectiveness of various lifestyle interventions. Where appropriate, a component NMA (CNMA) will be used to estimate effectiveness. The realist synthesis will complement and explain the findings of NMA and CNMA by including additional qualitative and mixed-methods studies. Studies will be included based on their relevance to the programme theory and the rigour of their methods, as determined by quality appraisal tools appropriate to the study design. Results from both syntheses will be incorporated into a logic model.

DISCUSSION

The paucity of population-specific lifestyle interventions contributes to the challenges of behaviour change in adults with learning disabilities. This study will provide an evidence-base from which various stakeholders can develop effective interventions for adults with learning disabilities. The evidence will also help prioritise and inform research recommendations for future primary research so that people with learning disabilities live happier, healthier and longer lives.

TRIAL REGISTRATION

PROSPERO CRD 42020223290.