Systematic review with multiple treatment comparison metaanalysis. on interventions for hepatic encephalopathy

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1 Systematic review with multiple treatment comparison metaanalysis on interventions for hepatic encephalopathy Hepatic encephalopathy (HE) is a reversible neuropsychiatric syndrome associated with severe liver disease associated with high morbidity and mortality. The severity of the condition ranges from minimal signs to coma. The severity of clinically overt HE may be graded using specifically developed scales whereas minimal HE is assessed using psychometric tests. The pathogenesis is yet to be determined, but is believed to include elevated ammonia, imbalances in the ratio between aromatic and branched chain amino acids (BCAA) and false neurotransmitters with changes. The management of patients with HE includes early diagnosis and treatment of precipitating factors such as infections, electrolyte disturbances and gastrointestinal bleeding. A large number of randomized controlled trials have assessed interventions for HE. The currently recommended treatments include non-absorbable disaccharides, rifaximin and BCAA supplements. Other potentially effective interventions include l-ornitin-l-aspartate (LOLA), neomycin, metronidazole, the molecular adsorbent recirculating system (MARS), L-ornithine phenylacetate, probiotics, synbiotics and glycerol phenylbuturate. The relative benefit of these treatments needs to be determined in order to support evidence based treatment guidelines. We therefore plan to perform a multiple treatment comparison meta-analysis. Objective To evaluate the relative beneficial and harmful effect of interventions for HE.

2 Methods Criteria for considering studies for this review Types of studies: Randomised controlled trials are eligible for inclusion irrespective of publication status, language, blinding, or publication year. Types of participants: We plan to include i) patients with clinically overt or minimal HE at baseline and ii) patients at risk of developing HE. Types of interventions: All medical interventions that aim at preventing HE or for the treatment of HE manifestations will be eligible for inclusion (e.g., non-absorbable disaccharides, L-ornithine L-aspartate, BCAA supplements, probiotics/synbiotics, antibiotics (rifaximin and other antibiotics) and glycerol phenylbuturate. Devices such as MARS or other non-medical interventions will not be included. The interventions will be included regardless of dose, interval, treatment duration, and route of administration. Types of outcome measures Primary outcomes All-cause mortality. HE (prevention of HE and improvement of HE manifestations) Secondary outcomes Liver-related mortality

3 Adverse events (any untoward medical occurrence, which does not necessarily have to have a causal relationship with treatments). Search methods for identification of studies Electronic searches: The Cochrane Library, MEDLINE, EMBASE, and Science Citation Index Expanded will be searched for eligible trials. Searching other resources: Manual searches for additional trials will be performed in reference lists from relevant papers, conference proceedings, correspondence with experts, and online trial registers Data collection and analysis Selection of studies: all authors will participate in the identification and selection of trials. Excluded trials will be listed with the reason for exclusion. Data extraction and management: At least two authors will extract data. Disagreements will be resolved through discussion with a third author before the analyses. Data on all patients included will be sought. Primary authors of the included trials will be contacted for additional information if data are not included in the published trial reports. Extracted data will include outcome measures, risk of bias, country of origin, duration of follow up, mean age of included patients, proportion of men, type of HE, proportion with cirrhosis, type, dose and duration of interventions. Assessment of risk of bias in included studies The Cochrane Collaboration's tool for the assessment of bias will be used based on the following definitions

4 Allocation sequence generation: low risk of bias (computer-generated random numbers, a table of random numbers or similar), unclear risk of bias (not described) or high risk of bias (quasi-randomized trial). Allocation concealment: low risk of bias (central independent unit, serially numbered indistinguishable drug containers or bottles, serially numbered opaque sealed envelopes or similar), unclear risk of bias (not described) or high risk of bias (e.g., open table) Blinding: low risk of bias (participants and personnel are blinded), unclear risk of bias (not described), high risk of bias (open trial). Incomplete data: low risk of bias (all patients were accounted for in the published report and analyses), unclear risk of bias (not described), high risk of bias (patients were excluded from the report or analysis). Reporting bias: low risk of bias (clinically relevant outcome measures were described and reported), unclear risk of bias (not described), high risk of bias (clinically important outcomes were omitted from the report or differences between protocols and published trials were seen) Other biases: other apparent biases in the design and conduct of the trial Measures of treatment effect: For binary outcomes the treatment effect measure will be relative risk. For rates the treatment effect measure will be rate ratio. Unit of analysis issues: The unit of analysis for binary outcomes will be number of patients. For cross-over trials, only the first treatment period will be included.

5 Dealing with missing data: Analyses will be performed using the intention-to-treat principle including all patients, irrespective of compliance or follow-up. For patients with missing outcome data, carry forward of the last observed response will be used. Assessment of heterogeneity: The common heterogeneity will be used as a marker of between-trial heterogeneity. A common heterogeneity of 0-30% will be considered unimportant, 30-50% will be considered moderate risk of heterogeneity, 50-75% will be considered substantial risk of heterogeneity and % will be interpreted as considerably heterogeneity. Data synthesis All analyses will be performed in Stata version 12.1 (Statacorp, College Station, Texas, USA) and WinBUGS version (Lunn 2000). For each outcome a network metaanalysis (NMA) will be performed within a Bayesian framework. Due to expected clinical heterogeneity all meta-analyses will be performed using random-effects models. The treatment effect estimates are the posterior median derived from the NMA and will be expressed as Risk Ratio (RR) with 95% CredibleIntervals (CrI). A common heterogeneity standard deviation for each pairwise comparison will be assumed. The models will be fitted by Markov chain Monte Carlo techniques (MCMC), using the computer package WinBUGS. Posterior distribution will be obtained after iterations that followed burn-in iterations and the resulting Markov Chains will be thinned out by taking every 10th MCMC. Convergence will be evaluated at the end of the burn-in period. Estimates of the effect of each intervention relative to each other will be provided, whether or not they have been directly compared in trials; the common heterogeneity, mean tau,

6 with its 95% CrI and the probability that each treatment is the best for each positive outcome, given the results of the NMA which are the Surface Under the Cumulative Ranking Area (SUCRA) values. The assumption of consistency will also be assessed for every closed loop of three interventions formed in the network, and for each outcome. Inconsistency is present when there is excessive discrepancy between direct and indirect effects. The results of the consistency assumption will be reported as inconsistency factors and their 95% confidence interval (CI) for any closed loop (truncated to zero). We will evaluate if the model has a good fit calculating the posterior mean of the residual deviance (Dres) summarised at each data point, the effective number of parameters pd and the DIC, which is the sum of Dres and the pd. The model fit is good if Dres approximate the number of data points. Subgroup analysis and investigation of heterogeneity The following subgroups will be evaluated: Type of HE Patients with cirrhosis Trials with a low risk of bias or unclear/high risk of bias or trials with an unclear/high risk of bias Trials published as full paper articles or abstracts/unpublished trials

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