Lung nodules are reliably detectable on ultra-low-dose CT utilising model-based iterative reconstruction with radiation equivalent to plain radiography

AIM

To determine if ultra-low-dose (ULD) computed tomography (CT) utilising model-based iterative reconstruction (MBIR) with radiation equivalent to plain radiography allows the detection of lung nodules.

MATERIALS AND METHODS

Ninety-nine individuals undergoing surveillance of solid pulmonary nodules undertook a low-dose (LD) and ULD CT during the same sitting. Image pairs were read blinded, in random order, and independently by two experienced thoracic radiologists. With LD-CT as the reference standard, the number, size, and location of nodules was compared, and inter-rater agreement was established.

RESULTS

There was very good inter-rater agreement with regards nodules ≥4mm for both the LD- (k=0.931) and ULD-CT (k=0.869). One hundred and ninety-nine nodules were reported on the LD-CT by both radiologists and 196 reported on the ULD-CT, with no nodules reported only on the ULD-CT. This gives a sensitivity of 98.5% and specificity of 100% for ULD-CT with MBIR. The effective dose of radiation was significantly different between the two scans (p<0.0001), 1.67 mSv for the LD-CT and 0.13 mSv for the ULD-CT.

CONCLUSION

ULD-CT utilising MBIR and delivering radiation equivalent to plain radiography, allows detection of lung nodules with high sensitivity. The attendant 10-fold reduction in radiation may allow for dramatic reductions in cumulative radiation exposure.

Feasibility of parent communication training with remote coaching using smartphone apps

BACKGROUND

Communication training for parents of young children with neurodisability is often delivered in groups and includes video coaching. Group teaching is problematic when there is wide variation in the characteristics and needs amongst participants.

AIMS

To assess the potential feasibility and acceptability of delivering one-to-one parent training supported by remote coaching using smartphone apps and of conducting further trials of the intervention.

METHODS & PROCEDURES

We aimed to recruit eight children aged 12-48 months with motor disorders and communication difficulties and to provide families with individual parent training in six weekly home visits supplemented by remote coaching via smartphone apps. For outcome measurement, parents recorded their interaction with their child thrice weekly during baseline (3 weeks), intervention, post-intervention (3 weeks) and follow-up (1 week). Measures comprised parent responsiveness and counts of children's communication and vocalization. Research design feasibility was measured through rates of recruitment, attrition, outcome measure completion and agreement between raters on outcome measurement. Intervention feasibility was assessed through the proportion of therapy sessions received, the number of videos and text messages shared using the apps in remote coaching, and message content. Parents were interviewed about the acceptability of the intervention and trial design. Interviews were transcribed and analyzed using inductive thematic analysis.

OUTCOMES & RESULTS

Nine children were recruited over 16 weeks. All fitted the inclusion criteria. Four families withdrew from the study. Five families completed the intervention. No family submitted the target number of video recordings for outcome measurement. Interrater agreement was moderate for child communication (K = 0.46) and vocalization (K = 0.60) and high for The Responsive Augmentative and Alternative Communication Style scale (RAACS) (r_s = 0.96). Parents who completed the intervention reported positive experiences of the programme and remote coaching via the apps. Therapist messages via the app contained comments on parent and child behaviour and requests for parental reflection/action; parental messages contained reflections on children's communication.

CONCLUSIONS & IMPLICATIONS

The intervention and study design demanded high levels of parental involvement and was not suitable for all families. Recording shorter periods of interaction via mobile phones or using alternative methods of data collection may increase feasibility of outcome measurement.

A Cross-Sectional Investigation of Cognition and Ultrasound-Based Vascular Strain Indices

OBJECTIVE

We examine the relationship between variability in the plaque strain distribution estimated using ultrasound with multiple cognitive domains including executive, language, visuospatial reasoning, and memory function.

METHOD

Asymptomatic (n = 42) and symptomatic (n = 34) patients with significant (>60%) carotid artery stenosis were studied for plaque instability using ultrasound strain imaging and multiple cognitive domains including executive, language, visuospatial reasoning, and memory function. Correlation and ROC analyses were performed between ultrasound strain indices and cognitive function. Strain indices and cognition scores were also compared between symptomatic and asymptomatic patients to determine whether there are significant group differences.

RESULTS

Association of high-strain distributions with dysexecutive function was observed in both asymptomatic and symptomatic patients. For memory, visuospatial, and language functions, the correlations between strain and cognition were weaker for the asymptomatic compared to symptomatic group.

CONCLUSIONS

Both asymptomatic and symptomatic patients demonstrate a relationship between vessel strain indices and executive function indicating that silent strokes and micro-emboli could initially contribute to a decline in executive function, whereas strokes and transient ischemic attacks may cause the further decline in other cognitive functions.

Incorporating external evidence on between-trial heterogeneity in network meta-analysis

In a network meta‐analysis, between‐study heterogeneity variances are often very imprecisely estimated because data are sparse, so standard errors of treatment differences can be highly unstable. External evidence can provide informative prior distributions for heterogeneity and, hence, improve inferences. We explore approaches for specifying informative priors for multiple heterogeneity variances in a network meta‐analysis.

First, we assume equal heterogeneity variances across all pairwise intervention comparisons (approach 1); incorporating an informative prior for the common variance is then straightforward. Models allowing unequal heterogeneity variances are more realistic; however, care must be taken to ensure implied variance‐covariance matrices remain valid. We consider three strategies for specifying informative priors for multiple unequal heterogeneity variances. Initially, we choose different informative priors according to intervention comparison type and assume heterogeneity to be proportional across comparison types and equal within comparison type (approach 2). Next, we allow all heterogeneity variances in the network to differ, while specifying a common informative prior for each. We explore two different approaches to this: placing priors on variances and correlations separately (approach 3) or using an informative inverse Wishart distribution (approach 4).

Our methods are exemplified through application to two network metaanalyses. Appropriate informative priors are obtained from previously published evidence‐based distributions for heterogeneity.

Relevant prior information on between‐study heterogeneity can be incorporated into network meta‐analyses, without needing to assume equal heterogeneity across treatment comparisons. The approaches proposed will be beneficial in sparse data sets and provide more appropriate intervals for treatment differences than those based on imprecise heterogeneity estimates.

When should meta-analysis avoid making hidden normality assumptions?

Meta-analysis is a widely used statistical technique. The simplicity of the calculations required when performing conventional meta-analyses belies the parametric nature of the assumptions that justify them. In particular, the normal distribution is extensively, and often implicitly, assumed. Here, we review how the normal distribution is used in meta-analysis. We discuss when the normal distribution is likely to be adequate and also when it should be avoided. We discuss alternative and more advanced methods that make less use of the normal distribution. We conclude that statistical methods that make fewer normality assumptions should be considered more often in practice. In general, statisticians and applied analysts should understand the assumptions made by their statistical analyses. They should also be able to defend these assumptions. Our hope is that this article will foster a greater appreciation of the extent to which assumptions involving the normal distribution are made in statistical methods for meta-analysis. We also hope that this article will stimulate further discussion and methodological work.

A comparison of heterogeneity variance estimators in simulated random-effects meta-analyses

Studies combined in a meta-analysis often have differences in their design and conduct that can lead to heterogeneous results. A random-effects model accounts for these differences in the underlying study effects, which includes a heterogeneity variance parameter. The DerSimonian-Laird method is often used to estimate the heterogeneity variance, but simulation studies have found the method can be biased and other methods are available. This paper compares the properties of nine different heterogeneity variance estimators using simulated meta-analysis data. Simulated scenarios include studies of equal size and of moderate and large differences in size. Results confirm that the DerSimonian-Laird estimator is negatively biased in scenarios with small studies and in scenarios with a rare binary outcome. Results also show the Paule-Mandel method has considerable positive bias in meta-analyses with large differences in study size. We recommend the method of restricted maximum likelihood (REML) to estimate the heterogeneity variance over other methods. However, considering that meta-analyses of health studies typically contain few studies, the heterogeneity variance estimate should not be used as a reliable gauge for the extent of heterogeneity in a meta-analysis. The estimated summary effect of the meta-analysis and its confidence interval derived from the Hartung-Knapp-Sidik-Jonkman method are more robust to changes in the heterogeneity variance estimate and show minimal deviation from the nominal coverage of 95% under most of our simulated scenarios.

Building and Maintaining a Citizen Science Network With Fishermen and Fishing Communities Post Deepwater Horizon Oil Disaster Using a CBPR Approach

When the Deepwater Horizon oil rig blew out in 2010, the immediate threats to productive deep water and estuarial fisheries and the region's fishing and energy economies were obvious. Less immediately obvious, but equally unsettling, were risks to human health posed by potential damage to the regional food web. This paper describes grassroots and regional efforts by the Gulf Coast Health Alliance: health risks related to the Macondo Spill Fishermen's Citizen Science Network project. Using a community-based participatory research approach and a citizen science structure, the multiyear project measured exposure to petrogenic polycyclic aromatic hydrocarbons, researched the toxicity of these polycyclic aromatic hydrocarbon compounds, and communicated project findings and seafood consumption guidelines throughout the region (coastal Louisiana, Mississippi, and Alabama). Description/analysis focuses primarily on the process of building a network of working fishermen and developing group environmental health literacy competencies.

Multistep estimators of the between-study variance: The relationship with the Paule-Mandel estimator

A wide variety of estimators of the between-study variance are available in random-effects meta-analysis. Many, but not all, of these estimators are based on the method of moments. The DerSimonian-Laird estimator is widely used in applications, but the Paule-Mandel estimator is an alternative that is now recommended. Recently, DerSimonian and Kacker have developed two-step moment-based estimators of the between-study variance. We extend these two-step estimators so that multiple (more than two) steps are used. We establish the surprising result that the multistep estimator tends towards the Paule-Mandel estimator as the number of steps becomes large. Hence, the iterative scheme underlying our new multistep estimator provides a hitherto unknown relationship between two-step estimators and Paule-Mandel estimator. Our analysis suggests that two-step estimators are not necessarily distinct estimators in their own right; instead, they are quantities that are closely related to the usual iterative scheme that is used to calculate the Paule-Mandel estimate. The relationship that we establish between the multistep and Paule-Mandel estimator is another justification for the use of the latter estimator. Two-step and multistep estimators are perhaps best conceptualized as approximate Paule-Mandel estimators.

Leadership and the functioning of maternal health services in two rural district hospitals in South Africa

Maternal mortality remains high in Eastern Cape Province, South Africa, despite over 90% of pregnant women utilizing maternal health services. A recent survey showed wide variation in performance among districts in the province. Heterogeneity was also found at the district level, where maternal health outcomes varied considerably among district hospitals. In ongoing research, leadership emerged as one of the key health systems factors affecting the performance of maternal health services at facility level. This article reports on a subsequent case study undertaken to examine leadership practices and the functioning of maternal health services in two resource-limited hospitals with disparate maternal health outcomes. An exploratory mixed-methods case study was undertaken with the two rural district hospitals as the units of analysis. The hospitals were purposively selected based on their maternal health outcomes: one reported good maternal health outcomes (pseudonym: Chisomo) and the other had poor outcomes (pseudonym: Tinyade). Comparative data were collected through a facility survey, non-participant observation of management and perinatal meetings, record reviews and interviews with hospital leadership, staff and patients to elicit information about leadership practices including supervision, communication and teamwork. Descriptive and thematic data analysis was undertaken. The two hospitals had similar infrastructure and equipment. Hospital managers at Chisomo used their innovation and entrepreneurial skills to improve quality of care, and leadership style was described as supportive, friendly, approachable but 'firm'. They also undertook frequent and supportive supervisory meetings. Each department at Chisomo developed its own action plan and used data to monitor their actions. Good performers were acknowledged in group meetings. Staff in this facility were motivated and patients were happy about the quality of services. The situation was different at Tinyade hospital. Participants described the leadership style of their senior managers as authoritarian. Managers were rarely available in the office and did not hold regular meetings, leading to poor communication across teams and poor coordination to address resource constraints. This demotivated the staff. The differences in leadership style, structures, processes and work culture affected teamwork, managerial supervision and support. The study demonstrates how leadership styles and practices influence maternal health care services in resource limited hospitals. Supportive leadership manifested itself in the form of focused efforts to build teamwork, enhance entrepreneurship and in management systems that are geared to improving maternal care.

The drivers of sea lice management policies and how best to integrate them into a risk management strategy: An ecosystem approach to sea lice management

The control of sea lice infestations on cultivated Atlantic salmon is a major issue in many regions of the world. The numerous drivers which shape the priorities and objectives of the control strategies vary for different regions/jurisdictions. These range from the animal welfare and economic priorities of the producers, to the mitigation of any potential impacts on wild stocks. Veterinary ethics, environmental impacts of therapeutants, and impacts for organic certification of the produce are, amongst others, additional sets of factors which should be considered. Current best practice in both EU and international environmental law advocates a holistic ecosystem approach to assessment of impacts and risks. The issues of biosecurity and ethics, including the impacts on the stocks of species used as cleaner fish, are areas for inclusion in such a holistic ecosystem assessment. The Drivers, Pressures, State, Impacts, Responses (DPSIR) process is examined as a decision-making framework and potential applications to sea lice management are outlined. It is argued that this is required to underpin any integrated sea lice management (ISLM) strategy to balance pressures and outcomes and ensure a holistic approach to managing the issue of sea lice infestations on farmed stock on a medium to long-term basis.

Prolonged and large outbreak of invasive group A Streptococcus disease within a nursing home: repeated intrafacility transmission of a single strain

OBJECTIVES

Multiple invasive group A Streptococcus (GAS) infections were reported to public health by a skilled nursing facility (facility A) in Illinois between May 2014 and August 2016. Cases continued despite interventions including antibiotic prophylaxis for all residents and staff. Two other geographically close facilities reported contemporaneous outbreaks of GAS. We investigated potential reasons for ongoing transmission.

METHODS

We obtained epidemiologic data from chart review of cases and review of facility and public health records from previous investigations into the outbreak. Infection control practices at facility A were observed and evaluated. Whole genome sequencing followed by phylogenetic analysis was performed on available isolates from the three facilities.

RESULTS

From 2014 to 2016, 19 invasive and 60 noninvasive GAS infections were identified at facility A occurring in three clusters. Infection control evaluations during clusters 2 and 3 identified hand hygiene compliance rates of 14% to 25%, appropriate personal protective equipment use in only 33% of observed instances, and deficient wound-care practices. GAS isolates from residents and staff of all three facilities were subtype emm89.0; on phylogenetic analysis, facility A isolates were monophyletic and distinct.

CONCLUSIONS

Inadequate infection control and improper wound-care practices likely led to this 28-month-long outbreak of severe infections in a skilled nursing facility. Whole genome sequencing and phylogenetic analysis suggested that intrafacility transmission of a single highly transmissible GAS strain was responsible for the outbreak in facility A. Integration of genomic epidemiology tools with traditional epidemiology and infection control assessments was helpful in investigation of a facility-wide outbreak.

The role of secondary outcomes in multivariate meta-analysis

Univariate meta‐analysis concerns a single outcome of interest measured across a number of independent studies. However, many research studies will have also measured secondary outcomes. Multivariate meta‐analysis allows us to take these secondary outcomes into account and can also include studies where the primary outcome is missing. We define the efficiency E as the variance of the overall estimate from a multivariate meta‐analysis relative to the variance of the overall estimate from a univariate meta‐analysis. The extra information gained from a multivariate meta‐analysis of n studies is then similar to the extra information gained if a univariate meta‐analysis of the primary effect had a further n(1−E)/E studies. The variance contribution of a study's secondary outcomes (its borrowing of strength) can be thought of as a contrast between the variance matrix of the outcomes in that study and the set of variance matrices of all the studies in the meta‐analysis. In the bivariate case this is given a simple graphical interpretation as the borrowing‐of‐strength plot. We discuss how these findings can also be used in the context of random‐effects meta‐analysis. Our discussion is motivated by a published meta‐analysis of 10 antihypertension clinical trials.

Prompting arm activity after stroke: A clinical proof of concept study of wrist-worn accelerometers with a vibrating alert function

Background

Frequent practice of functional movements after stroke may optimise motor recovery; however, it is challenging for patients to remember to integrate an impaired limb into daily activities. We report the activity responses of stroke patients receiving a vibrating alert delivered by a tri-axial accelerometer wristband to prompt movement of the impaired arm if hourly activity levels fell.

Methods

Adults with upper limb impairment ≤28 days post-stroke wore the device for four weeks. Therapists and patients reviewed movement activity data twice weekly to agree ongoing rehabilitation activities and programme the wristband with a personalised prompt threshold (median baseline activity + 5%, 25% or 50%).

Results: Seven patients completed the programme (five males; mean ± standard deviation (age) 64 ± 5 years; days post-stroke 13 ± 7; baseline/four-week Action Research Arm Test median (Interquartile range (IQR)) 39 (8, 44)/56 (11, 57)). Wristbands were worn for 89% of programme duration. A total of 1,288 prompts were delivered, with a median of four (IQR 3,7) prompts per patient per day. Mean activity increases following a prompt ranged from 11% to 29%.

Conclusions

Feedback delivered by a programmable accelerometer increased impaired arm activity. Improvements are required in device reliability before conducting a pragmatic clinical trial to examine the impact upon recovery.

Genetic fingerprinting of salmon louse (Lepeophtheirus salmonis) populations in the North-East Atlantic using a random forest classification approach

Caligid sea lice represent a significant threat to salmonid aquaculture worldwide. Population genetic analyses have consistently shown minimal population genetic structure in North Atlantic Lepeophtheirus salmonis, frustrating efforts to track louse populations and improve targeted control measures. The aim of this study was to test the power of reduced representation library sequencing (IIb-RAD sequencing) coupled with random forest machine learning algorithms to define markers for fine-scale discrimination of louse populations. We identified 1286 robustly supported SNPs among four L. salmonis populations from Ireland, Scotland and Northern Norway. Only weak global structure was observed based on the full SNP dataset. The application of a random forest machine-learning algorithm identified 98 discriminatory SNPs that dramatically improved population assignment, increased global genetic structure and resulted in significant genetic population differentiation. A large proportion of SNPs found to be under directional selection were also identified to be highly discriminatory. Our data suggest that it is possible to discriminate between nearby L. salmonis populations given suitable marker selection approaches, and that such differences might have an adaptive basis. We discuss these data in light of sea lice adaption to anthropogenic and environmental pressures as well as novel approaches to track and predict sea louse dispersal.

A comparison of seven random-effects models for meta-analyses that estimate the summary odds ratio

Comparative trials that report binary outcome data are commonly pooled in systematic reviews and meta-analyses. This type of data can be presented as a series of 2-by-2 tables. The pooled odds ratio is often presented as the outcome of primary interest in the resulting meta-analysis. We examine the use of 7 models for random-effects meta-analyses that have been proposed for this purpose. The first of these models is the conventional one that uses normal within-study approximations and a 2-stage approach. The other models are generalised linear mixed models that perform the analysis in 1 stage and have the potential to provide more accurate inference. We explore the implications of using these 7 models in the context of a Cochrane Review, and we also perform a simulation study. We conclude that generalised linear mixed models can result in better statistical inference than the conventional 2-stage approach but also that this type of model presents issues and difficulties. These challenges include more demanding numerical methods and determining the best way to model study specific baseline risks. One possible approach for analysts is to specify a primary model prior to performing the systematic review but also to present the results using other models in a sensitivity analysis. Only one of the models that we investigate is found to perform poorly so that any of the other models could be considered for either the primary or the sensitivity analysis.

Editor's Choice - The Implications of Non-compliance to Endovascular Aneurysm Repair Surveillance: A Systematic Review and Meta-analysis

OBJECTIVE/BACKGROUND

Increasingly, reports show that compliance rates with endovascular aneurysm repair (EVAR) surveillance are often suboptimal. The aim of this study was to determine the safety implications of non-compliance with surveillance.

METHODS

The study was carried out according to the Preferred Items for Reporting of Systematic Reviews and Meta-Analyses (PRISMA) guidelines. An electronic search was undertaken by two independent authors using Embase, MEDLINE, Cochrane, and Web of Science databases from 1990 to July 2017. Only studies that analysed infrarenal EVAR and had a definition of non-compliance described as weeks or months without imaging surveillance were analysed. Meta-analysis was carried out using the random-effects model and restricted maximum likelihood estimation.

RESULTS

Thirteen articles (40,730 patients) were eligible for systematic review; of these, seven studies (14,311 patients) were appropriate for comparative meta-analyses of mortality rates. Three studies (8316 patients) were eligible for the comparative meta-analyses of re-intervention rates after EVAR and four studies (12,995 patients) eligible for meta-analysis for abdominal aortic aneurysm related mortality (ARM). The estimated average non-compliance rate was 42.0% (95% confidence interval [CI] 28-56%). Although there is some evidence that non-compliant patients have better survival rates, there was no statistically significant difference in all cause mortality rates (year 1: odds ratio [OR] 5.77, 95% CI 0.74-45.14; year 3: OR 2.28, 95% CI 0.92-5.66; year 5: OR 1.81, 95% CI 0.88-3.74) and ARM (OR 1.47, 95% CI 0.99-2.19) between compliant and non-compliant patients in the first 5 years after EVAR. The re-intervention rate was statistically significantly higher in compliant patients from 3 to 5 years after EVAR (year 1: OR 6.36, 95% CI 0.23-172.73; year 3: OR 3.94, 85% CI 1.46-10.69; year 5: OR 5.34, 95% CI 1.87-15.29).

CONCLUSION

This systematic review and meta-analysis suggests that patients compliant with EVAR surveillance programmes may have an increased re-intervention rate but do not appear to have better survival rates than non-compliant patients.

Paule-Mandel estimators for network meta-analysis with random inconsistency effects

Network meta-analysis is used to simultaneously compare multiple treatments in a single analysis. However, network meta-analyses may exhibit inconsistency, where direct and different forms of indirect evidence are not in agreement with each other, even after allowing for between-study heterogeneity. Models for network meta-analysis with random inconsistency effects have the dual aim of allowing for inconsistencies and estimating average treatment effects across the whole network. To date, two classical estimation methods for fitting this type of model have been developed: a method of moments that extends DerSimonian and Laird's univariate method and maximum likelihood estimation. However, the Paule and Mandel estimator is another recommended classical estimation method for univariate meta-analysis. In this paper, we extend the Paule and Mandel method so that it can be used to fit models for network meta-analysis with random inconsistency effects. We apply all three estimation methods to a variety of examples that have been used previously and we also examine a challenging new dataset that is highly heterogenous. We perform a simulation study based on this new example. We find that the proposed Paule and Mandel method performs satisfactorily and generally better than the previously proposed method of moments because it provides more accurate inferences. Furthermore, the Paule and Mandel method possesses some advantages over likelihood-based methods because it is both semiparametric and requires no convergence diagnostics. Although restricted maximum likelihood estimation remains the gold standard, the proposed methodology is a fully viable alternative to this and other estimation methods.

Borrowing of strength and study weights in multivariate and network meta-analysis

Multivariate and network meta-analysis have the potential for the estimated mean of one effect to borrow strength from the data on other effects of interest. The extent of this borrowing of strength is usually assessed informally. We present new mathematical definitions of 'borrowing of strength'. Our main proposal is based on a decomposition of the score statistic, which we show can be interpreted as comparing the precision of estimates from the multivariate and univariate models. Our definition of borrowing of strength therefore emulates the usual informal assessment. We also derive a method for calculating study weights, which we embed into the same framework as our borrowing of strength statistics, so that percentage study weights can accompany the results from multivariate and network meta-analyses as they do in conventional univariate meta-analyses. Our proposals are illustrated using three meta-analyses involving correlated effects for multiple outcomes, multiple risk factor associations and multiple treatments (network meta-analysis).