Target product profile for a test for the early assessment of treatment efficacy in Chagas disease patients: An expert consensus.

Six to 7 million people are estimated to be infected by Trypanosoma cruzi, the parasite causing Chagas disease. Thirty to 40% of them, i.e., 1.8 to 2.4 million people, will suffer cardiac disorders and/or digestive clinical manifestations if they are not treated early during the course of the infection [1, 2]. However, only a small fraction of patients are properly diagnosed and treated [3]. Current clinical guidelines recommend treating T. cruzi–infected people if they are asymptomatic or present early symptoms of the disease (Table 1) [4, 5]. Benznidazole (BNZ) and nifurtimox (NFX) are the first-line antiparasitic treatments currently available, both with long administration regimens (60 days) that can produce adverse side effects [6–8]. Despite the fact they are not 100% effective in patients with chronic disease [9–12], they are the only drugs currently registered, and the benefits of their administration have been confirmed in several clinical studies. Currently, clinical trials with new compounds, using alternative regimens that aim to maintain efficacy whilst reducing toxicity, are ongoing and could lead to new therapeutic opportunities and/or policy change

Erratum to: Methods for evaluating medical tests and biomarkers

[This corrects the article DOI: 10.1186/s41512-016-0001-y.]

Erratum to: Methods for evaluating medical tests and biomarkers

[This corrects the article DOI: 10.1186/s41512-016-0001-y.]

Evidence synthesis to inform model-based cost-effectiveness evaluations of diagnostic tests: a methodological systematic review of health technology assessments

Background: Evaluations of diagnostic tests are challenging because of the indirect nature of their impact on patient outcomes. Model-based health economic evaluations of tests allow different types of evidence from various sources to be incorporated and enable cost-effectiveness estimates to be made beyond the duration of available study data. To parameterize a health-economic model fully, all the ways a test impacts on patient health must be quantified, including but not limited to diagnostic test accuracy. Methods: We assessed all UK NIHR HTA reports published May 2009-July 2015. Reports were included if they evaluated a diagnostic test, included a model-based health economic evaluation and included a systematic review and meta-analysis of test accuracy. From each eligible report we extracted information on the following topics: 1) what evidence aside from test accuracy was searched for and synthesised, 2) which methods were used to synthesise test accuracy evidence and how did the results inform the economic model, 3) how/whether threshold effects were explored, 4) how the potential dependency between multiple tests in a pathway was accounted for, and 5) for evaluations of tests targeted at the primary care setting, how evidence from differing healthcare settings was incorporated. Results: The bivariate or HSROC model was implemented in 20/22 reports that met all inclusion criteria. Test accuracy data for health economic modelling was obtained from meta-analyses completely in four reports, partially in fourteen reports and not at all in four reports. Only 2/7 reports that used a quantitative test gave clear threshold recommendations. All 22 reports explored the effect of uncertainty in accuracy parameters but most of those that used multiple tests did not allow for dependence between test results. 7/22 tests were potentially suitable for primary care but the majority found limited evidence on test accuracy in primary care settings. Conclusions: The uptake of appropriate meta-analysis methods for synthesising evidence on diagnostic test accuracy in UK NIHR HTAs has improved in recent years. Future research should focus on other evidence requirements for cost-effectiveness assessment, threshold effects for quantitative tests and the impact of multiple diagnostic tests

Erratum to: Methods for evaluating medical tests and biomarkers

The original MEMTAB Abstracts in Diagnostic and Prognostic Research contains the incorrect year on individual abstracts in the PDF [1].“Diagnostic and Prognostic Research 2016” under the correspondence line should therefore have been written as “Diagnostic and Prognostic Research 2017” as the journal did not launch until 2017