Systematic review with network meta‐analysis: the comparative effectiveness and safety of interventions in patients with overt hepatic encephalopathy

Abstract

Summary Background Interventional treatment for overt hepatic encephalopathy (OHE), includes non‐absorbable disaccharides, neomycin, rifaximin, L‐ornithine‐L‐aspartate and branched chain amino acids (BCAA). However, the optimum regimen remains inconclusive. Aim To compare interventions in terms of patients’ adverse events and major clinical outcomes. Methods Literature search of PubMed, Embase, Scopus, and Cochrane Library studies published up to July 31 2014. RCTs of above interventions in OHE patients were included. Network meta‐analysis combined direct and indirect evidence to estimate odds ratios (ORs) and mean difference (MD) between treatments and the probabilities of ranking for treatment based on clinical outcomes. Results Twenty eligible RCTs were included. When compared with observation, only L‐ornithine‐L‐aspartate (OR 3.71, P < 0.001) and BCAA (OR 3.37, P < 0.001) improved clinical efficacy significantly. However, when L‐ornithine‐L‐aspartate was compared with BCAA, non‐absorbable disaccharides and neomycin, there was a trend suggesting that L‐ornithine‐L‐aspartate may be the most effective intervention with respect to clinical improvement (OR 1.10), rifaximin (OR 1.31), non‐absorbable disaccharides (OR 2.75), neomycin (OR 2.22). In addition, L‐ornithine‐L‐aspartate (MD −20.18, 95% CI −40.12 to −0.27) provided a significant reduction in blood ammonia concentration compared with observation. Neomycin appeared to be associated with more adverse events in comparison with non‐absorbable disaccharides (OR 10.15), rifaximin (OR 17.31), L‐ornithine‐L‐aspartate (OR 3.16) or BCAA (OR 7.69). Conclusions L‐ornithine‐L‐aspartate treatment may show a trend in superiority for clinical efficacy among standard interventions for OHE. Rifaximin shows the greatest reduction in blood ammonia concentration, and treatment with neomycin demonstrates a higher probability in causing adverse effects among the five compared interventions.

Systematic review with network meta-analysis: statins and risk of hepatocellular carcinoma

“…Then a Bayesian network meta-analysis to compare different treatments (rosuvastatin, atorvastatin, simvastatin, pravastatin, fluvastatin, cerivastatin, and lovastatin, as well as observation) to each other was followed respectively, with a random-effects model using Markov chain Monte Carlo methods in WinBUGS (MRC Bio-statistics Unit, Cambridge, UK) as described in our previous work [28–30]. The DerSimonian and Laird random effects model was utilized to calculate pooled estimates of hazard ratios (HRs), relative risks (RRs), and 95% confidence intervals (CIs) of direct comparisons between two strategies according to Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0.…”

Predictive Value of Age-Bilirubin-International Normalized Ratio-Creatinine Score in Short-Term Survival of Acute-on-Chronic Hepatitis B Liver Failure

“…Several prognostic models and biomarkers have been proposed for predicting the outcomes of these patients; these could assist in guiding management and intervention strategies. Though MELD is treated as the gold standard for the prediction of outcome in patients with end-stage liver disease, the survival rate of approximately 15%-20% cannot be accurately predicted [9,29]. In addition, CP and MELD scores also have heterogeneous benefits in specific applications [30].…”

Diagnostic Value of Nineteen Different Imaging Methods for Patients with Breast Cancer: a Network Meta-Analysis

“…Lack of auto-correlation and convergence were checked and confirmed by four chains and a 20, 000-simulation burn-in phase; finally, direct probability statements were derived from an additional 50, 000-simulation phase [28]. The node-splitting method was adopted to evaluate the consistency between direct and indirect evidence, and the consistency or inconsistency model was selected based on the results of the aforementioned evaluation [29]. To provide assistance in the interpretation of ORs, the surface under the cumulative ranking curve (SUCRA) was used to calculate the probability of each intervention, being the most effective diagnostic method based on a Bayesian approach using probability values, and the larger the SUCRA value is, the better the rank of the intervention [30, 31].…”