Cost-effectiveness of noninvasive liver fibrosis tests for treatment decisions in patients with chronic hepatitis B in the UK: systematic review and economic evaluation.

We compared the cost-effectiveness of various noninvasive tests (NITs) in patients with chronic hepatitis B and elevated transaminases and/or viral load who would normally undergo liver biopsy to inform treatment decisions. We searched various databases until April 2012. We conducted a systematic review and meta-analysis to calculate the diagnostic accuracy of various NITs using a bivariate random-effects model. We constructed a probabilistic decision analytical model to estimate health care costs and outcomes quality-adjusted-life-years (QALYs) using data from the meta-analysis, literature, and national UK data. We compared the cost-effectiveness of four decision-making strategies: testing with NITs and treating patients with fibrosis stage ≥F2, testing with liver biopsy and treating patients with ≥F2, treat none (watchful waiting) and treat all irrespective of fibrosis. Treating all patients without prior fibrosis assessment had an incremental cost-effectiveness ratio (ICER) of £28,137 per additional QALY gained for HBeAg-negative patients. For HBeAg-positive patients, using Fibroscan was the most cost-effective option with an ICER of £23,345. The base case results remained robust in the majority of sensitivity analyses, but were sensitive to changes in the ≥ F2 prevalence and the benefit of treatment in patients with F0-F1. For HBeAg-negative patients, strategies excluding NITs were the most cost-effective: treating all patients regardless of fibrosis level if the high cost-effectiveness threshold of £30,000 is accepted; watchful waiting if not. For HBeAg-positive patients, using Fibroscan to identify and treat those with ≥F2 was the most cost-effective option.

Cochrane Centralised Search Service showed high sensitivity identifying randomized controlled trials: A retrospective analysis.

BACKGROUND AND OBJECTIVES: The Cochrane Central Register of Controlled Trials (CENTRAL) is compiled from a number of sources, including PubMed and Embase. Since 2017, we have increased the number of sources feeding into CENTRAL and improved the efficiency of our processes through the use of application programming interfaces, machine learning, and crowdsourcing.Our objectives were twofold: (1) Assess the effectiveness of Cochrane's centralized search and screening processes to correctly identify references to published reports which are eligible for inclusion in Cochrane systematic reviews of randomized controlled trials (RCTs). (2) Identify opportunities to improve the performance of Cochrane's centralized search and screening processes to identify references to eligible trials. METHODS: We identified all references to RCTs (either published journal articles or trial registration records) with a publication or registration date between 1st January 2017 and 31st December 2018 that had been included in a Cochrane intervention review. We then viewed an audit trail for each included reference to determine if it had been identified by our centralized search process and subsequently added to CENTRAL. RESULTS: We identified 650 references to included studies with a publication year of 2017 or 2018. Of those, 634 (97.5%) had been captured by Cochrane's Centralised Search Service. Sixteen references had been missed by the Cochrane's Centralised Search Service: six had PubMed-not-MEDLINE status, four were missed by the centralized Embase search, three had been misclassified by Cochrane Crowd, one was from a journal not indexed in MEDLINE or Embase, one had only been added to Embase in 2019, and one reference had been rejected by the automated RCT machine learning classifier. Of the sixteen missed references, eight were the main or only publication to the trial in the review in which it had been included. CONCLUSION: This analysis has shown that Cochrane's centralized search and screening processes are highly sensitive. It has also helped us to understand better why some references to eligible RCTs have been missed. The CSS is playing a critical role in helping to populate CENTRAL and is moving us toward making CENTRAL a comprehensive repository of RCTs.