Cancer-Related Fatigue – Clinical Evaluation Scales and Interventions: A Systematic Review

Background: Cancer-related fatigue (CRF) is one of the most frequent and prevalent symptoms expressed by cancer patients and cancer survivors. It is a multifactorial phenomenon that causes a direct detrimental impact on quality of life. Objectives: This systematic review aims to identify different clinical evaluation scales and interventions available for fatigue associated with cancer. Materials and Methods: A methodology of the systematic literature review was carried out. Two separate databases PubMed and Google Scholar searches were performed using different MeSH terms. Results: A total of 2611 research articles were screened and identified 10 unidimensional scales (four with one item scales and six with numerous item scales) and 13 multidimensional scales which are available for the screening and clinical evaluation of fatigue. Reviews have also revealed non-pharmacological interventions such as exercise, complementary therapies, nutritional and psychoeducational interventions, sleep therapy, energy therapy, bright white light, restorative therapies upcoming anthroposophical medicine, and various pharmacological agents effective in managing CRF. Conclusion: Clinical evaluation of fatigue and its management is crucial for improving the quality of life. Yet, more rigorous research studies with higher statistical power need to be conducted on these interventions to generate adequate evidences for managing the CRF.

DeepNull models non-linear covariate effects to improve phenotypic prediction and association power

Genome-wide association studies (GWASs) examine the association between genotype and phenotype while adjusting for a set of covariates. Although the covariates may have non-linear or interactive effects, due to the challenge of specifying the model, GWAS often neglect such terms. Here we introduce DeepNull, a method that identifies and adjusts for non-linear and interactive covariate effects using a deep neural network. In analyses of simulated and real data, we demonstrate that DeepNull maintains tight control of the type I error while increasing statistical power by up to 20% in the presence of non-linear and interactive effects. Moreover, in the absence of such effects, DeepNull incurs no loss of power. When applied to 10 phenotypes from the UK Biobank (n = 370K), DeepNull discovered more hits (+6%) and loci (+7%), on average, than conventional association analyses, many of which are biologically plausible or have previously been reported. Finally, DeepNull improves upon linear modeling for phenotypic prediction (+23% on average).

The Contribution of “Individual Participant Data” Meta-Analyses of Psychotherapies for Depression to the Development of Personalized Treatments: A Systematic Review

While randomized trials typically lack sufficient statistical power to identify predictors and moderators of outcome, “individual participant data” (IPD) meta-analyses, which combine primary data of multiple randomized trials, can increase the statistical power to identify predictors and moderators of outcome. We conducted a systematic review of IPD meta-analyses on psychological treatments of depression to provide an overview of predictors and moderators identified. We included 10 (eight pairwise and two network) IPD meta-analyses. Six meta-analyses showed that higher baseline depression severity was associated with better outcomes, and two found that older age was associated with better outcomes. Because power was high in most IPD meta-analyses, non-significant findings are also of interest because they indicate that these variables are probably not relevant as predictors and moderators. We did not find in any IPD meta-analysis that gender, education level, or relationship status were significant predictors or moderators. This review shows that IPD meta-analyses on psychological treatments can identify predictors and moderators of treatment effects and thereby contribute considerably to the development of personalized treatments of depression.

dsSurvival: Privacy preserving survival models for federated individual patient meta-analysis in DataSHIELD

Objective Achieving sufficient statistical power in a survival analysis usually requires large amounts of data from different sites. Sensitivity of individual-level data, ethical and practical considerations regarding data sharing across institutions could be a potential challenge for achieving this added power. Hence we implemented a federated meta-analysis approach of survival models in DataSHIELD, where only anonymous aggregated data are shared across institutions, while simultaneously allowing for exploratory, interactive modelling. In this case, meta-analysis techniques to combine analysis results from each site are a solution, but a manual analysis workflow hinders exploration. Thus, the aim is to provide a framework for performing meta-analysis of Cox regression models across institutions without manual analysis steps for the data providers. Results We introduce a package ( dsSurvival) which allows privacy preserving meta-analysis of survival models, including the calculation of hazard ratios. Our tool can be of great use in biomedical research where there is a need for building survival models and there are privacy concerns about sharing data.

Liver Stiffness, Albuminuria and Chronic Kidney Disease in Patients with NAFLD: A Systematic Review and Meta-Analysis

An association between liver stiffness, a surrogate measure of liver fibrosis, and chronic kidney disease (CKD) in patients with nonalcoholic fatty liver disease (NAFLD) has been proposed. However, most studies were small and had low statistical power. We systematically searched PubMed-MEDLINE and Scopus from inception to August 2021 for cross-sectional or cohort studies reporting the association between liver stiffness diagnosed by vibration controlled transient elastography (VCTE) and renal dysfunction. The primary outcome was CKD, defined as a composite of urinary albumin to creatinine ratio (UACR) ≥ 30 mg/g and estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2. Measures of association from individual studies were meta-analyzed using random effects models. Of the 526 titles initially scrutinized, 7 cross-sectional studies fulfilled the criteria and were included. For CKD, risk was higher in patients with liver fibrosis assessed by VCTE, compared with patients without (n = 5 studies: OR 2.49, 95% CI 1.89–3.29; test for overall effect z = 6.475, p < 0.001). When increased UACR was considered as an outcome, elevated liver stiffness was associated with a significantly increased risk as well (n = 3 studies: OR 1. 98 95% CI 1.29–3.05; test for overall effect z = 3.113, p = 0.002). Neither analysis showed significant heterogeneity (I2 = 0% and I2 = 46.5%, respectively for the two outcomes). This meta-analysis indicates that elevated liver stiffness is associated with increased odds of kidney outcomes among patients with NAFLD. Wider use of VCTE to screen for advanced fibrosis might help identify patients at risk of end-stage renal disease.

Piecewise Structural Equation Modeling of the Quantity Implicature in Child Language

We review an array of experimental methodological factors that either contribute to or detract from the measurement of pragmatic implicatures in child language. We carry out a truth value judgment task to measure children’s interpretations of the Spanish existential quantifier algunos in implicature-consistent and implicature-inconsistent contexts. Independently, we take measures of children’s inhibition, working memory, attention, approximate number ability, phrasal syntax, and lexicon. We model the interplay of these variables using a piecewise structural equation model (SEM), common in the life sciences, but not in the social and behavioral sciences. By 6 years of age, the children in our sample were not statistically different from adults in their interpretations. Syntax, lexicon, and inhibition significantly predict implicature generation, each accounting for unique variance. The approximate number system and inhibition significantly predict lexical development. The statistical power of the piecewise SEM components, with a sample of 64 children, is high, in comparison to a traditional, globally estimated SEM of the same data.

The Impact of a Graded Maximal Exercise Protocol on Exhaled Volatile Organic Compounds: A Pilot Study

Exhaled volatile organic compounds (VOCs) are of interest due to their minimally invasive sampling procedure. Previous studies have investigated the impact of exercise, with evidence suggesting that breath VOCs reflect exercise-induced metabolic activity. However, these studies have yet to investigate the impact of maximal exercise to exhaustion on breath VOCs, which was the main aim of this study. Two-litre breath samples were collected onto thermal desorption tubes using a portable breath collection unit. Samples were collected pre-exercise, and at 10 and 60 min following a maximal exercise test (VO2MAX). Breath VOCs were analysed by thermal desorption-gas chromatography-mass spectrometry using a non-targeted approach. Data showed a tendency for reduced isoprene in samples at 10 min post-exercise, with a return to baseline by 60 min. However, inter-individual variation meant differences between baseline and 10 min could not be confirmed, although the 10 and 60 min timepoints were different (p = 0.041). In addition, baseline samples showed a tendency for both acetone and isoprene to be reduced in those with higher absolute VO2MAX scores (mL(O2)/min), although with restricted statistical power. Baseline samples could not differentiate between relative VO2MAX scores (mL(O2)/kg/min). In conclusion, these data support that isoprene levels are dynamic in response to exercise.

Development of a data classification system for preterm birth cohort studies: the RECAP Preterm project

Background The small sample sizes available within many very preterm (VPT) longitudinal birth cohort studies mean that it is often necessary to combine and harmonise data from individual studies to increase statistical power, especially for studying rare outcomes. Curating and mapping data is a vital first step in the process of data harmonisation. To facilitate data mapping and harmonisation across VPT birth cohort studies, we developed a custom classification system as part of the Research on European Children and Adults born Preterm (RECAP Preterm) project in order to increase the scope and generalisability of research and the evaluation of outcomes across the lifespan for individuals born VPT. Methods The multidisciplinary consortium of expert clinicians and researchers who made up the RECAP Preterm project participated in a four-phase consultation process via email questionnaire to develop a topic-specific classification system. Descriptive analyses were calculated after each questionnaire round to provide pre- and post- ratings to assess levels of agreement with the classification system as it developed. Amendments and refinements were made to the classification system after each round. Results Expert input from 23 clinicians and researchers from the RECAP Preterm project aided development of the classification system’s topic content, refining it from 10 modules, 48 themes and 197 domains to 14 modules, 93 themes and 345 domains. Supplementary classifications for target, source, mode and instrument were also developed to capture additional variable-level information. Over 22,000 individual data variables relating to VPT birth outcomes have been mapped to the classification system to date to facilitate data harmonisation. This will continue to increase as retrospective data items are mapped and harmonised variables are created. Conclusions This bespoke preterm birth classification system is a fundamental component of the RECAP Preterm project’s web-based interactive platform. It is freely available for use worldwide by those interested in research into the long term impact of VPT birth. It can also be used to inform the development of future cohort studies.

Examining the Neural Correlates of Error Awareness in a Large fMRI Study

Goal-directed behaviour is dependent upon the ability to detect errors and implement appropriate post-error adjustments. Accordingly, several studies have explored the neural activity underlying error-monitoring processes, identifying the insula cortex as crucial for error awareness and reporting mixed findings with respect to the anterior cingulate cortex. Variable patterns of activation have previously been attributed to insufficient statistical power. We therefore sought to clarify the neural correlates of error awareness in a large event-related functional magnetic resonance imaging (MRI) study. Four hundred and two healthy participants undertook the Error Awareness Task, a motor Go/No-Go response inhibition paradigm in which participants were required to indicate their awareness of commission errors. Compared to unaware errors, aware errors were accompanied by significantly greater activity in a network of regions including the insula cortex, supramarginal gyrus, and midline structures such as the anterior cingulate cortex and supplementary motor area. Error awareness activity was related to indices of task performance and dimensional measures of psychopathology in select regions including the insula, supramarginal gyrus and supplementary motor area. Taken together, we identified a robust and reliable neural network associated with error awareness.

dsSurvival: Privacy preserving survival models for federated individual patient meta-analysis in DataSHIELD

Achieving sufficient statistical power in a survival analysis usually requires large amounts of data from different sites. Sensitivity of individual-level data, ethical and practical considerations regarding data sharing across institutions could be a potential challenge for achieving this added power. Hence we implemented a federated meta-analysis approach of survival models in DataSHIELD, where only anonymous aggregated data are shared across institutions, while simultaneously allowing for exploratory, interactive modelling. In this case, meta-analysis techniques to combine analysis results from each site are a solution, but a manual analysis workflow hinders exploration. Thus, the aim is to provide a framework for performing meta-analysis of Cox regression models across institutions without manual analysis steps for the data providers. We introduce a package (dsSurvival) which allows privacy preserving meta-analysis of survival models, including the calculation of hazard ratios. Our tool can be of great use in biomedical research where there is a need for building survival models and there are privacy concerns about sharing data. A tutorial in bookdown format with code, diagnostics, plots and synthetic data is available here: https://neelsoumya.github.io/dsSurvivalbookdown/ All code is available from the following repositories: https://github.com/neelsoumya/dsSurvivalClient/ https://github.com/neelsoumya/dsSurvival/