Not all systematic reviews are systematic: A meta-review of the quality of systematic reviews for non-invasive remote monitoring in heart failure

We carried out a critical appraisal and synthesis of the systematic reviews and meta-analyses of remote monitoring for heart failure. A comprehensive literature search identified 65 relevant publications from 3333 citations. Seventeen studies fulfilled the inclusion and exclusion criteria. Seven (41%) systematic reviews pooled results for meta-analysis. Eight (47%) considered all non-invasive remote monitoring strategies. Five (29%) focused on telemonitoring. Four (24%) included both non-invasive and invasive technologies. The reviews were appraised by two independent reviewers for their quality and risk of bias using the AMSTAR tool. According to the AMSTAR criteria, ten (58%) systematic reviews were of poor methodological quality. In the high quality reviews, the relative risk of mortality in patients who received remote monitoring ranged from 0.53 to 0.88. The high quality reviews also reported that remote monitoring reduced the relative risk of all-cause (0.52 to 0.96) and heart failure-related hospitalizations (0.72 to 0.79) and, as a consequence, healthcare costs. However, further research is required before considering widespread implementation of remote monitoring. The subset of the heart failure population that derives the most benefit from intensive monitoring, the best technology, and the optimum duration of monitoring, all need to be identified. © The Author(s) 2013

Facilitating return to work through early specialist health-based interventions (FRESH): protocol for a feasibility randomised controlled trial

Background Over one million people sustain traumatic brain injury each year in the UK and more than 10 % of these are moderate or severe injuries, resulting in cognitive and psychological problems that affect the ability to work. Returning to work is a primary rehabilitation goal but fewer than half of traumatic brain injury survivors achieve this. Work is a recognised health service outcome, yet UK service provision varies widely and there is little robust evidence to inform rehabilitation practice. A single-centre cohort comparison suggested better work outcomes may be achieved through early occupational therapy targeted at job retention. This study aims to determine whether this intervention can be delivered in three new trauma centres and to conduct a feasibility, randomised controlled trial to determine whether its effects and cost effectiveness can be measured to inform a definitive trial. Methods/design Mixed methods study, including feasibility randomised controlled trial, embedded qualitative studies and feasibility economic evaluation will recruit 102 people with traumatic brain injury and their nominated carers from three English UK National Health Service (NHS) trauma centres. Participants will be randomised to receive either usual NHS rehabilitation or usual rehabilitation plus early specialist traumatic brain injury vocational rehabilitation delivered by an occupational therapist. The primary objective is to assess the feasibility of conducting a definitive trial; secondary objectives include measurement of protocol integrity (inclusion/exclusion criteria, intervention adherence, reasons for non-adherence) recruitment rate, the proportion of eligible patients recruited, reasons for non-recruitment, spectrum of TBI severity, proportion of and reasons for loss to follow-up, completeness of data collection, gains in face-to-face Vs postal data collection and the most appropriate methods of measuring primary outcomes (return to work, retention) to determine the sample size for a larger trial. Discussion To our knowledge, this is the first feasibility randomised controlled trial of a vocational rehabilitation health intervention specific to traumatic brain injury. The results will inform the design of a definitive trial

The Unequal World of Health Data

Peter Byass argues that less data are available on the health of the poor than of the rich, and discusses several alternative strategies to improve the representativeness of health data

Relation between the Global Burden of Disease and Randomized Clinical Trials Conducted in Latin America Published in the Five Leading Medical Journals

Background: Since 1990 non communicable diseases and injuries account for the majority of death and disability-adjusted life years in Latin America. We analyzed the relationship between the global burden of disease and Randomized Clinical Trials (RCTs) conducted in Latin America that were published in the five leading medical journals.Methodology/Principal Findings: We included all RCTs in humans, exclusively conducted in Latin American countries, and published in any of the following journals: Annals of Internal Medicine, British Medical Journal, Journal of the American Medical Association, Lancet, and New England Journal of Medicine. We described the trials and reported the number of RCTs according to the main categories of the global burden of disease. Sixty-six RCTs were identified. Communicable diseases accounted for 38 (57%) reports. Maternal, perinatal, and nutritional conditions accounted for 19 (29%) trials. Non-communicable diseases represent 48% of the global burden of disease but only 14% of reported trials. No trial addressed injuries despite its 18% contribution to the burden of disease in 2000.Conclusions/Significance: A poor correlation between the burden of disease and RCTs publications was found. Non communicable diseases and injuries account for up to two thirds of the burden of disease in Latin America but these topics are seldom addressed in published RCTs in the selected sample of journals. Funding bodies of health research and editors should be aware of the increasing burden of non communicable diseases and injuries occurring in Latin America to ensure that this growing epidemic is not neglected in the research agenda and not affected by publication bias

Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study

Background: There is no consensus on the most appropriate approach to handle missing covariate data within prognostic modelling studies. Therefore a simulation study was performed to assess the effects of different missing data techniques on the performance of a prognostic model. Methods: Datasets were generated to resemble the skewed distributions seen in a motivating breast cancer example. Multivariate missing data were imposed on four covariates using four different mechanisms; missing completely at random (MCAR), missing at random (MAR), missing not at random (MNAR) and a combination of all three mechanisms. Five amounts of incomplete cases from 5% to 75% were considered. Complete case analysis (CC), single imputation (SI) and five multiple imputation (MI) techniques available within the R statistical software were investigated: a) data augmentation (DA) approach assuming a multivariate normal distribution, b) DA assuming a general location model, c) regression switching imputation, d) regression switching with predictive mean matching (MICE-PMM) and e) flexible additive imputation models. A Cox proportional hazards model was fitted and appropriate estimates for the regression coefficients and model performance measures were obtained. Results: Performing a CC analysis produced unbiased regression estimates, but inflated standard errors, which affected the significance of the covariates in the model with 25% or more missingness. Using SI, underestimated the variability; resulting in poor coverage even with 10% missingness. Of the MI approaches, applying MICE-PMM produced, in general, the least biased estimates and better coverage for the incomplete covariates and better model performance for all mechanisms. However, this MI approach still produced biased regression coefficient estimates for the incomplete skewed continuous covariates when 50% or more cases had missing data imposed with a MCAR, MAR or combined mechanism. When the missingness depended on the incomplete covariates, i.e. MNAR, estimates were biased with more than 10% incomplete cases for all MI approaches. Conclusion: The results from this simulation study suggest that performing MICE-PMM may be the preferred MI approach provided that less than 50% of the cases have missing data and the missing data are not MNAR

Multiple Imputation Ensembles (MIE) for dealing with missing data

Missing data is a significant issue in many real-world datasets, yet there are no robust methods for dealing with it appropriately. In this paper, we propose a robust approach to dealing with missing data in classification problems: Multiple Imputation Ensembles (MIE). Our method integrates two approaches: multiple imputation and ensemble methods and compares two types of ensembles: bagging and stacking. We also propose a robust experimental set-up using 20 benchmark datasets from the UCI machine learning repository. For each dataset, we introduce increasing amounts of data Missing Completely at Random. Firstly, we use a number of single/multiple imputation methods to recover the missing values and then ensemble a number of different classifiers built on the imputed data. We assess the quality of the imputation by using dissimilarity measures. We also evaluate the MIE performance by comparing classification accuracy on the complete and imputed data. Furthermore, we use the accuracy of simple imputation as a benchmark for comparison. We find that our proposed approach combining multiple imputation with ensemble techniques outperform others, particularly as missing data increases

Effectiveness of different databases in identifying studies for systematic reviews: experience from the WHO systematic review of maternal morbidity and mortality

BACKGROUND: Failure to be comprehensive can distort the results of a systematic review. Conversely, extensive searches may yield unmanageable number of citations of which only few may be relevant. Knowledge of usefulness of each source of information may help to tailor search strategies in systematic reviews. METHODS: We conducted a systematic review of prevalence/incidence of maternal mortality and morbidities from 1997 to 2002. The search strategy included electronic databases, hand searching, screening of reference lists, congress abstract books, contacting experts active in the field and web sites from less developed countries. We evaluated the effectiveness of each source of data and discuss limitations and implications for future research on this topic. RESULTS: Electronic databases identified 64098 different citations of which 2093 were included. Additionally 487 citations were included from other sources. MEDLINE had the highest yield identifying about 62% of the included citations. BIOSIS was the most precise with 13.2% of screened citations included. Considering electronic citations alone (2093), almost 20% were identified uniquely by MEDLINE (400), 7.4% uniquely by EMBASE (154), and 5.6% uniquely by LILACS (117). About 60% of the electronic citations included were identified by two or more databases. CONCLUSIONS: This analysis confirms the need for extending the search to other sources beyond well-known electronic databases in systematic reviews of maternal mortality and morbidity prevalence/incidence. These include regional databases such as LILACS and other topic specific sources such as hand searching of relevant journals not indexed in electronic databases. Guidelines for search strategies for prevalence/incidence studies need to be developed

Coring, profiling, and trenching: Archaeological field strategies for investigating the Pleistocene-Holocene-Anthropocene continuum

Archaeologists have long emphasized the importance of large-scale excavations and multi-year or even decades-long projects at a single site or site complex. Here, we highlight archaeological field strategies, termed coring, profiling, and trenching (CPT), that rely on relatively small-scale excavations or the collection of new samples from intact deposits in previously excavated trenches (aka test units or pits). Examples from multiple sites in Africa, Asia, and North America demonstrate that CPT is highly effective for obtaining high-resolution archaeobiological and geoarchaeological samples (e.g., faunal and botanical remains, sediments) and artefacts from areas that have seen limited or no archaeological research, little systematic application of archaeological science methods, or research only on a relatively narrow time period or geographic scale. Designed to complement large-scale excavations at single sites, CPT is ideal for multi-scalar research that works in tandem with remote sensing techniques, providing samples for detailed laboratory analyses and offering a bridge between surface surveys and large-scale excavation. Given the threats facing archaeological sites around the world from climate change and human development, as well as financial, training and infrastructure constraints, and concerns from many Indigenous communities about large excavations, we argue that CPT is an important method for addressing 21st century human-environmental research questions

Screening of DUB activity and specificity by MALDI-TOF mass spectrometry

Deubiquitylases (DUBs) are key regulators of the ubiquitin system which cleave ubiquitin moieties from proteins and polyubiquitin chains. Several DUBs have been implicated in various diseases and are attractive drug targets. We have developed a sensitive and fast assay to quantify in vitro DUB enzyme activity using matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry. Unlike other current assays, this method uses unmodified substrates, such as diubiquitin topoisomers. By analyzing 42 human DUBs against all diubiquitin topoisomers we provide an extensive characterization of DUB activity and specificity. Our results confirm the high specificity of many members of the OTU and JAMM DUB families and highlight that all USPs tested display low linkage selectivity. We also demonstrate that this assay can be deployed to assess the potency and specificity of DUB inhibitors by profiling 11 compounds against a panel of 32 DUBs