Treatments for Chronic Kidney Disease: A Systematic Literature Review of Randomized Controlled Trials

Delaying disease progression and reducing the risk of mortality are key goals in the treatment of chronic kidney disease (CKD). New drug classes to augment renin-angiotensin-aldosterone system (RAAS) inhibitors as the standard of care have scarcely met their primary endpoints until recently. This systematic literature review explored treatments evaluated in patients with CKD since 1990 to understand what contemporary data add to the treatment landscape. Eighty-nine clinical trials were identified that had enrolled patients with estimated glomerular filtration rate 13.9-102.8 mL/min/1.73 m2 and urinary albumin-to-creatinine ratio (UACR) 29.9-2911.0 mg/g, with (75.5%) and without (20.6%) type 2 diabetes (T2D). Clinically objective outcomes of kidney failure and all-cause mortality (ACM) were reported in 32 and 64 trials, respectively. Significant reductions (P < 0.05) in the risk of kidney failure were observed in seven trials: five small trials published before 2008 had evaluated the RAAS inhibitors losartan, benazepril, or ramipril in patients with (n = 751) or without (n = 84-436) T2D; two larger trials (n = 2152-2202) published onwards of 2019 had evaluated the sodium-glucose co-transporter 2 (SGLT2) inhibitors canagliflozin (in patients with T2D and UACR > 300-5000 mg/g) and dapagliflozin (in patients with or without T2D and UACR 200-5000 mg/g) added to a background of RAAS inhibition. Significant reductions in ACM were observed with dapagliflozin in the DAPA-CKD trial. Contemporary data therefore suggest that augmenting RAAS inhibitors with new drug classes has the potential to improve clinical outcomes in a broad range of patients with CKD.

Models of cardiovascular surgery biobanking to facilitate translational research and precision medicine

Biobanking in health care has evolved over the last few decades from simple biological sample repositories to complex and dynamic units with multi‐organizational infrastructure networks and has become an essential tool for modern medical research. Cardiovascular tissue biobanking provides a unique opportunity to utilize cardiac and vascular samples for translational research into heart failure and other related pathologies. Current techniques for diagnosis, classification, and treatment monitoring of cardiac disease relies primarily on interpretation of clinical signs, imaging, and blood biomarkers. Further research at the disease source (i.e. myocardium and blood vessels) has been limited by a relative lack of access to quality human cardiac tissue and the inherent shortcomings of most animal models of heart disease. In this review, we describe a model for cardiovascular tissue biobanking and databasing, and its potential to facilitate basic and translational research. We share techniques to procure endocardial samples from patients with hypertrophic cardiomyopathy, heart failure with reduced ejection fraction, and heart failure with preserved ejection fraction, in addition to aortic disease samples. We discuss some of the issues with respect to data collection, privacy, biobank consent, and the governance of tissue biobanking. The development of tissue biobanks as described here has significant scope to improve and facilitate translational research in multi‐omic fields such as genomics, transcriptomics, proteomics, and metabolomics. This research heralds an era of precision medicine, in which patients with cardiovascular pathology can be provided with optimized and personalized medical care for the treatment of their individual phenotype.

Glutaredoxins regulate maize inflorescence meristem development via redox control of TGA transcriptional activity

Glutaredoxins (GRXs) are small oxidoreductases that can modify target protein activities through control of the redox (reduction/oxidation) state by reducing or glutathionylating disulfide bridges. Although CC-type GRXs are plant specific and play important roles in many processes, the mechanisms by which they modulate the activity of target proteins in vivo are unknown. In this study, we show that a maize CC-type GRX, MALE STERILE CONVERTED ANTHER1 (MSCA1), acts redundantly with two paralogues, ZmGRX2 and ZmGRX5, to modify the redox state and the activity of its putative target, the TGA transcription factor FASCIATED EAR4 (FEA4) that acts as a negative regulator of inflorescence meristem development. We used CRISPR-Cas9 to create a GRX triple knockout, resulting in severe suppression of meristem, ear and tassel growth and reduced plant height. We further show that GRXs regulate the redox state, DNA accessibility and transcriptional activities of FEA4, which acts downstream of MSCA1 and its paralogues to control inflorescence development. Our findings reveal the function of GRXs in meristem development, and also provide direct evidence for GRX-mediated redox modification of target proteins in plants.

Optimizing the discovery and assessment of therapeutic targets in heart failure with preserved ejection fraction

There is an urgent need for models that faithfully replicate heart failure with preserved ejection fraction (HFpEF), now recognized as the most common form of heart failure in the world. In vitro approaches have several shortcomings, most notably the immature nature of stem cell‐derived human cardiomyocytes [induced pluripotent stem cells (iPSC)] and the relatively short lifespan of primary cardiomyocytes. Three‐dimensional ‘organoids’ incorporating mature iPSCs with other cell types such as endothelial cells and fibroblasts are a significant advance, but lack the complexity of true myocardium. Animal models can replicate many features of human HFpEF, and rodent models are the most common, and recent attempts to incorporate haemodynamic, metabolic, and ageing contributions are encouraging. Differences relating to species, physiology, heart rate, and heart size are major limitations for rodent models. Porcine models mitigate many of these shortcomings and approximate human physiology more closely, but cost and time considerations limit their potential for widespread use. Ex vivo analysis of failing hearts from animal models offer intriguing possibilities regarding cardiac substrate utilisation, but are ultimately subject to the same constrains as the animal models from which the hearts are obtained. Ex vivo approaches using human myocardial biopsies can uncover new insights into pathobiology leveraging myocardial energetics, substrate turnover, molecular changes, and systolic/diastolic function. In collaboration with a skilled cardiothoracic surgeon, left ventricular endomyocardial biopsies can be obtained at the time of valvular surgery in HFpEF patients. Critically, these tissues maintain their disease phenotype, preserving inter‐relationship of myocardial cells and extracellular matrix. This review highlights a novel approach, where ultra‐thin myocardial tissue slices from human HFpEF hearts can be used to assess changes in myocardial structure and function. We discuss current approaches to modelling HFpEF, describe in detail the novel tissue slice model, expand on exciting opportunities this model provides, and outline ways to improve this model further.

Correlation Between Early Endpoints and Overall Survival in Non-Small-Cell Lung Cancer: A Trial-Level Meta-Analysis

Early endpoints, such as progression-free survival (PFS), are increasingly used as surrogates for overall survival (OS) to accelerate approval of novel oncology agents. Compiling trial-level data from randomized controlled trials (RCTs) could help to develop a predictive framework to ascertain correlation trends between treatment effects for early and late endpoints. Through trial-level correlation and random-effects meta-regression analysis, we assessed the relationship between hazard ratio (HR) OS and (1) HR PFS and (2) odds ratio (OR) PFS at 4 and 6 months, stratified according to the mechanism of action of the investigational product. Using multiple source databases, we compiled a data set including 81 phase II-IV RCTs (35 drugs and 156 observations) of patients with non-small-cell lung cancer. Low-to-moderate correlations were generally observed between treatment effects for early endpoints (based on PFS) and HR OS across trials of agents with different mechanisms of action. Moderate correlations were seen between treatment effects for HR PFS and HR OS across all trials, and in the programmed cell death-1/programmed cell death ligand-1 and epidermal growth factor receptor trial subsets. Although these results constitute an important step, caution is advised, as there are some limitations to our evaluation, and an additional patient-level analysis would be needed to establish true surrogacy.

Study specific prediction intervals for random-effects meta-analysis: A tutorial: Prediction intervals in meta-analysis

The pooled estimate of the average effect is of primary interest when fitting the random-effects model for meta-analysis. However estimates of study specific effects, for example those displayed on forest plots, are also often of interest. In this tutorial, we present the case, with the accompanying statistical theory, for estimating the study specific true effects using so called 'empirical Bayes estimates' or 'Best Unbiased Linear Predictions' under the random-effects model. These estimates can be accompanied by prediction intervals that indicate a plausible range of study specific true effects. We coalesce and elucidate the available literature and illustrate the methodology using two published meta-analyses as examples. We also perform a simulation study that reveals that coverage probability of study specific prediction intervals are substantially too low if the between-study variance is small but not negligible. Researchers need to be aware of this defect when interpreting prediction intervals. We also show how empirical Bayes estimates, accompanied with study specific prediction intervals, can embellish forest plots. We hope that this tutorial will serve to provide a clear theoretical underpinning for this methodology and encourage its widespread adoption.

Frequentist rules for regulatory approval of subgroups in phase III trials: A fresh look at an old problem

Background

The number of Phase III trials that include a biomarker in design and analysis has increased due to interest in personalised medicine. For genetic mutations and other predictive biomarkers, the trial sample comprises two subgroups, one of which, say B+ is known or suspected to achieve a larger treatment effect than the other B−. Despite treatment effect heterogeneity, trials often draw patients from both subgroups, since the lower responding B− subgroup may also gain benefit from the intervention. In this case, regulators/commissioners must decide what constitutes sufficient evidence to approve the drug in the B− population.

Methods and Results

Assuming trial analysis can be completed using generalised linear models, we define and evaluate three frequentist decision rules for approval. For rule one, the significance of the average treatment effect in B− should exceed a pre-defined minimum value, say ZB−>L. For rule two, the data from the low-responding group B− should increase statistical significance. For rule three, the subgroup-treatment interaction should be non-significant, using type I error chosen to ensure that estimated difference between the two subgroup effects is acceptable. Rules are evaluated based on conditional power, given that there is an overall significant treatment effect. We show how different rules perform according to the distribution of patients across the two subgroups and when analyses include additional (stratification) covariates in the analysis, thereby conferring correlation between subgroup effects.

Conclusions

When additional conditions are required for approval of a new treatment in a lower response subgroup, easily applied rules based on minimum effect sizes and relaxed interaction tests are available. Choice of rule is influenced by the proportion of patients sampled from the two subgroups but less so by the correlation between subgroup effects.

Pre-existing influenza-specific nasal IgA or nasal viral infection does not affect live attenuated influenza vaccine immunogenicity in children

The United Kingdom has a national immunization programme which includes annual influenza vaccination in school-aged children, using live attenuated influenza vaccine (LAIV). LAIV is given annually, and it is unclear whether repeat administration can affect immunogenicity. Because LAIV is delivered intranasally, pre-existing local antibody might be important. In this study, we analysed banked samples from a study performed during the 2017/18 influenza season to investigate the role of pre-existing influenza-specific nasal immunoglobulin (Ig)A in children aged 6-14 years. Nasopharyngeal swabs were collected prior to LAIV immunization to measure pre-existing IgA levels and test for concurrent upper respiratory tract viral infections (URTI). Oral fluid samples were taken at baseline and 21-28 days after LAIV to measure IgG as a surrogate of immunogenicity. Antibody levels at baseline were compared with a pre-existing data set of LAIV shedding from the same individuals, measured by reverse transcription-polymerase chain reaction. There was detectable nasal IgA specific to all four strains in the vaccine at baseline. However, baseline nasal IgA did not correlate with the fold change in IgG response to the vaccine. Baseline nasal IgA also did not have an impact upon whether vaccine virus RNA was detectable after immunization. There was no difference in fold change of antibody between individuals with and without an URTI at the time of immunization. Overall, we observed no effect of pre-existing influenza-specific nasal antibody levels on immunogenicity, supporting annual immunization with LAIV in children.

Alternative weighting schemes when performing matching-adjusted indirect comparisons

Methods for indirect comparisons and network meta-analysis use aggregate level data from multiple studies. A very common, and closely related, scenario is where a company has individual patient data (IPD) from its own trial, but only has published aggregate data from a competitor's trial, and an indirect comparison of the treatments evaluated in these two trials is required. Matching-Adjusted Indirect Comparison (MAIC) has been developed for this situation, where we use the available IPD to adjust for between-trial imbalances in the distributions of observed baseline covariates between the two trials. We extend the current MAIC methodology, where we compute the weights that satisfy the conventional method of moments and result in the largest possible effective sample size (ESS). We show that the approach proposed by Zubizarreta in a previous study can be used for this purpose. We derive a new analytical result that shows why this alternative approach provides a larger ESS than a conventional MAIC. We also derive a new formula for the maximum ESS that can be achieved, even when permitting negative weights, when adjusting for one covariate. This can be used as an easily computed new metric that quantifies the difficulty in adjusting for covariates. What is already known: MAIC is an established way to perform population adjustment in the situation where IPD is available from one trial but only aggregate level data is available from another trial, and an indirect comparison is required. However the effective sample size (ESS) can be small after making the adjustment. What is new: We show that an alternative method can result in a larger ESS. We provide new analytical results showing why this is the case. We derive a new descriptive statistic that is based on maximising the ESS that quantifies the difficulties in adjusting for particular covariates. Potential impact for RSM readers outside the authors' field: Reweighting methods for population adjustment are becoming more commonly used and their implications for research synthesis methodology is now considerable. This paper provides important new links between the theoretical literature, and the more applied research synthesis methodology literature, relating to this topic.

Psychosocial and Mental Health Characteristics of RePresent Game Users

The RePresent games are online video games that are publicly available and designed to educate people about legal self-representation in civil court. This study was part of a project to examine use of the RePresent games in Connecticut, Massachusetts, Maine, and New Hampshire from January 2018 to May 2018. Data on game use across the four states were analyzed, and an online survey was conducted to examine characteristics of RePresent game users and nonusers seeking civil legal aid (n = 277). The RePresent games were accessed more than 7,000 times in five months. The most common legal problems reported were related to debt, family, and housing. Compared with nonusers, RePresent game users were significantly more likely to be nonwhite, to have an incarceration history, to have more legal problems, and to screen positive for alcohol use problems. In the total sample, 83 percent screened positive for depression, 81 percent for generalized anxiety disorder, and 45 percent for drug problems. Only 34 percent reported use of mental health services, and 17 percent reported substance abuse treatment in the past year. These findings demonstrate that products like the RePresent games can be widely accessible to adults from disadvantaged backgrounds. In addition, civil legal settings may be a new area for mental health screening and intervention.

One-stage individual participant data meta-analysis models for continuous and binary outcomes: Comparison of treatment coding options and estimation methods

A one-stage individual participant data (IPD) meta-analysis synthesizes IPD from multiple studies using a general or generalized linear mixed model. This produces summary results (eg, about treatment effect) in a single step, whilst accounting for clustering of participants within studies (via a stratified study intercept, or random study intercepts) and between-study heterogeneity (via random treatment effects). We use simulation to evaluate the performance of restricted maximum likelihood (REML) and maximum likelihood (ML) estimation of one-stage IPD meta-analysis models for synthesizing randomized trials with continuous or binary outcomes. Three key findings are identified. First, for ML or REML estimation of stratified intercept or random intercepts models, a t-distribution based approach generally improves coverage of confidence intervals for the summary treatment effect, compared with a z-based approach. Second, when using ML estimation of a one-stage model with a stratified intercept, the treatment variable should be coded using "study-specific centering" (ie, 1/0 minus the study-specific proportion of participants in the treatment group), as this reduces the bias in the between-study variance estimate (compared with 1/0 and other coding options). Third, REML estimation reduces downward bias in between-study variance estimates compared with ML estimation, and does not depend on the treatment variable coding; for binary outcomes, this requires REML estimation of the pseudo-likelihood, although this may not be stable in some situations (eg, when data are sparse). Two applied examples are used to illustrate the findings.

Bivariate network meta-analysis for surrogate endpoint evaluation

Surrogate endpoints are very important in regulatory decision making in healthcare, in particular if they can be measured early compared to the long-term final clinical outcome and act as good predictors of clinical benefit. Bivariate meta-analysis methods can be used to evaluate surrogate endpoints and to predict the treatment effect on the final outcome from the treatment effect measured on a surrogate endpoint. However, candidate surrogate endpoints are often imperfect, and the level of association between the treatment effects on the surrogate and final outcomes may vary between treatments. This imposes a limitation on methods which do not differentiate between the treatments. We develop bivariate network meta-analysis (bvNMA) methods, which combine data on treatment effects on the surrogate and final outcomes, from trials investigating multiple treatment contrasts. The bvNMA methods estimate the effects on both outcomes for all treatment contrasts individually in a single analysis. At the same time, they allow us to model the trial-level surrogacy patterns within each treatment contrast and treatment-level surrogacy, thus enabling predictions of the treatment effect on the final outcome either for a new study in a new population or for a new treatment. Modelling assumptions about the between-studies heterogeneity and the network consistency, and their impact on predictions, are investigated using an illustrative example in advanced colorectal cancer and in a simulation study. When the strength of the surrogate relationships varies across treatment contrasts, bvNMA has the advantage of identifying treatment comparisons for which surrogacy holds, thus leading to better predictions.

A new justification of the Hartung-Knapp method for random-effects meta-analysis based on weighted least squares regression

The Hartung-Knapp method for random-effects meta-analysis, that was also independently proposed by Sidik and Jonkman, is becoming advocated for general use. This method has previously been justified by taking all estimated variances as known and using a different pivotal quantity to the more conventional one when making inferences about the average effect. We provide a new conceptual framework for, and justification of, the Hartung-Knapp method. Specifically, we show that inferences from fitted random-effects models, using both the conventional and the Hartung-Knapp method, are equivalent to those from closely related intercept only weighted least squares regression models. This observation provides a new link between Hartung and Knapp's methodology for meta-analysis and standard linear models, where it can be seen that the Hartung-Knapp method can be justified by a linear model that makes a slightly weaker assumption than taking all variances as known. This provides intuition for why the Hartung-Knapp method has been found to perform better than the conventional one in simulation studies. Furthermore, our new findings give more credence to ad hoc adjustments of confidence intervals from the Hartung-Knapp method that ensure these are at least as wide as more conventional confidence intervals. The conceptual basis for the Hartung-Knapp method that we present here should replace the established one because it more clearly illustrates the potential benefit of using it.

Empirical comparison of univariate and multivariate meta-analyses in Cochrane Pregnancy and Childbirth reviews with multiple binary outcomes

BACKGROUND

Multivariate meta-analysis (MVMA) jointly synthesizes effects for multiple correlated outcomes. The MVMA model is potentially more difficult and time-consuming to apply than univariate models, so if its use makes little difference to parameter estimates, it could be argued that it is redundant.

METHODS

We assessed the applicability and impact of MVMA in Cochrane Pregnancy and Childbirth (CPCB) systematic reviews. We applied MVMA to CPCB reviews published between 2011 and 2013 with two or more binary outcomes with at least three studies and compared findings with results of univariate meta-analyses. Univariate random effects meta-analysis models were fitted using restricted maximum likelihood estimation (REML).

RESULTS

Eighty CPCB reviews were published. MVMA could not be applied in 70 of these reviews. MVMA was not feasible in three of the remaining 10 reviews because the appropriate models failed to converge. Estimates from MVMA agreed with those of univariate analyses in most of the other seven reviews. Statistical significance changed in two reviews: In one, this was due to a very small change in P value; in the other, the MVMA result for one outcome suggested that previous univariate results may be vulnerable to small-study effects and that the certainty of clinical conclusions needs consideration.

CONCLUSIONS

MVMA methods can be applied only in a minority of reviews of interventions in pregnancy and childbirth and can be difficult to apply because of missing correlations or lack of convergence. Nevertheless, clinical and/or statistical conclusions from MVMA may occasionally differ from those from univariate analyses.

Are Gypsy Roma Traveller communities indigenous and would identification as such better address their public health needs?

INTRODUCTION

Across Europe, large numbers of Gypsy Roma Traveller communities experience significant health inequities such as higher morbidity, mortality and infant mortality. This health inequity is perpetuated by wider determinants such as a lower social status, lower educational attainment and substandard accommodation. This is not dissimilar to other indigenous peoples, even though many Gypsy Roma Traveller communities are not identified as indigenous.

METHODS

This article presents contemporary literature and research alongside the internationally agreed principles of indigenous peoples, examining similarities between Gypsy Roma Traveller communities and other indigenous peoples.

RESULTS

We argue that Gypsy Roma Traveller communities could be recognised as indigenous in terms of the internationally agreed principles of indigeneity and shared experiences of health inequity, colonisation and cultural genocide. Doing so would enable a more robust public health strategy and development of public health guidelines that take into account their cultural views and practices.

CONCLUSION

Recognising Gypsy Roma Traveller communities in this way is important, especially concerning public health, as formal recognition of indigeneity provides certain rights and protection that can be used to develop appropriate public health strategies. Included within this are more nuanced approaches to promoting health, which focus on strengths and assets rather than deficit constructs that can perpetuate problematising of these communities.

Behavioural intervention for weight loss maintenance versus standard weight advice in adults with obesity: A randomised controlled trial in the UK (NULevel Trial)

BACKGROUND

Scalable weight loss maintenance (WLM) interventions for adults with obesity are lacking but vital for the health and economic benefits of weight loss to be fully realised. We examined the effectiveness and cost-effectiveness of a low-intensity technology-mediated behavioural intervention to support WLM in adults with obesity after clinically significant weight loss (≥5%) compared to standard lifestyle advice.

METHODS AND FINDINGS

The NULevel trial was an open-label randomised controlled superiority trial in 288 adults recruited April 2014 to May 2015 with weight loss of ≥5% within the previous 12 months, from a pre-weight loss BMI of ≥30 kg/m2. Participants were self-selected, and the majority self-certified previous weight loss. We used a web-based randomisation system to assign participants to either standard lifestyle advice via newsletter (control arm) or a technology-mediated low-intensity behavioural WLM programme (intervention arm). The intervention comprised a single face-to-face goal-setting meeting, self-monitoring, and remote feedback on weight, diet, and physical activity via links embedded in short message service (SMS). All participants were provided with wirelessly connected weighing scales, but only participants in the intervention arm were instructed to weigh themselves daily and told that they would receive feedback on their weight. After 12 months, we measured the primary outcome, weight (kilograms), as well as frequency of self-weighing, objective physical activity (via accelerometry), psychological variables, and cost-effectiveness. The study was powered to detect a between-group weight difference of ±2.5 kg at follow-up. Overall, 264 participants (92%) completed the trial. Mean weight gain from baseline to 12 months was 1.8 kg (95% CI 0.5-3.1) in the intervention group (n = 131) and 1.8 kg (95% CI 0.6-3.0) in the control group (n = 133). There was no evidence of an effect on weight at 12 months (difference in adjusted mean weight change from baseline: -0.07 [95% CI 1.7 to -1.9], p = 0.9). Intervention participants weighed themselves more frequently than control participants and were more physically active. Intervention participants reported greater satisfaction with weight outcomes, more planning for dietary and physical activity goals and for managing lapses, and greater confidence for healthy eating, weight loss, and WLM. Potential limitations, such as the use of connected weighing study in both trial arms, the absence of a measurement of energy intake, and the recruitment from one region of the United Kingdom, are discussed.

CONCLUSIONS

There was no difference in the WLM of participants who received the NULevel intervention compared to participants who received standard lifestyle advice via newsletter. The intervention affected some, but not all, process-related secondary outcomes of the trial.

TRIAL REGISTRATION

This trial is registered with the ISRCTN registry (ISRCTN 14657176; registration date 20 March 2014).

Two new approaches for the visualisation of models for network meta-analysis

Background

Meta-analysis is a useful tool for combining evidence from multiple studies to estimate a pooled treatment effect. An extension of meta-analysis, network meta-analysis, is becoming more commonly used as a way to simultaneously compare multiple treatments in a single analysis. Despite the variety of approaches available for presenting fitted models, ascertaining an intuitive understanding of these models is often difficult. This is especially challenging in large networks with many different treatments. Here we propose two visualisation methods, so that network meta-analysis models can be more easily interpreted.

Methods

Our methods can be used irrespective of the statistical model or the estimation method used and are grounded in network analysis. We define three types of distance measures between the treatments that contribute to the network. These three distance measures are based on 1) the estimated treatment effects, 2) their standard errors and 3) the corresponding p-values. Then, by using a suitable threshold, we categorise some treatment pairs as being “close” (short distances). Treatments that are close are regarded as “connected” in the network analysis theory. Finally, we group the treatments into communities using standard methods for network analysis. We are then able to identify which parts of the network are estimated to have similar (or different) treatment efficacy and which parts of the network are better identified. We also propose a second method using parametric bootstrapping, where a heat map is used in the visualisation. We use the software R and provide the code used.

Results

We illustrate our new methods using a challenging dataset containing 22 treatments, and a previously fitted model for this data. Two communities of treatments that appear to have similar efficacy are identified. Furthermore using our methods we can identify parts of the network that are better (and less well) identified.

Conclusions

Our new visualisation approaches may be used by network meta-analysts to gain an intuitive understanding of the implications of their fitted models. Our visualisation methods may be used informally, to identify the most salient features of the fitted models that can then be reported, or more formally by presenting the new visualisation devices within published reports.

Electronic supplementary material

The online version of this article (10.1186/s12874-019-0689-9) contains supplementary material, which is available to authorized users.

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.