Demographic gaps and requirements for participation: A systematic review of clinical trial designs in Hidradenitis Suppurativa

Background: Hidradenitis suppurativa (HS) is a chronic inflammatory disease that disproportionally affects women, as well as Black and biracial individuals. While adalimumab remains the only therapy approved by the Food and Drug Administration for HS, many HS clinical trials for novel and re-tasked therapies are ongoing or upcoming. To optimize treatment equity, reflect the patient population, and facilitate trial participation, it is important to elucidate aspects of clinical trial protocols that may systematically exclude specific patient groups or impose hardships. Objective: The study aimed to systematically review inclusion and exclusion criteria as well as participant demographics in HS clinical trials. Methods: A literature search of PubMed, Embase, Cochrane Central, and Web of Science databases was conducted. Peer-reviewed publications of randomized controlled trials that were written in English and had at least 10 participants were included. Title and abstract screening and data extraction were completed by two independent reviewers, with disagreements resolved by a third. Results: Twenty-three studies totaling 1,496 adult participants met the inclusion criteria. Race and ethnicity were not reported in 473/1,496 (31.6%) and 1,420/1,496 (94.9%) trial participants, respectively. Trial participants were predominantly white (811/1,023, 79.3%) and female (1,057/1,457, 72.5%). The median of each study’s average age was 35.7 years (IQR 33.5–38.0), and 17/23 (73.9%) trials excluded pediatric patients. Nearly all participants had Hurley Stage II (499/958, 52.0%) or Hurley Stage III (385/958, 40.2%) disease. Many trials excluded patients who were pregnant (19/23, 82.6%) and breastfeeding (13/23, 56.5%), or who had HS that was “too severe” (8/23, 34.8%) or “too mild” (16/23, 70.0%). Frequently, trial protocols required prolonged washout periods from HS therapies, relatively long duration in the study’s placebo arm, and prohibited concurrent analgesic use. Conclusions: This systematic review of 23 HS clinical trials totaling 1,496 participants identified substantial hardships imposed by trial participation, high rates of missing race and ethnicity data, and low representation of key patient groups, including those who identify as Black. Future trials with pragmatic study designs, broader inclusion criteria, and study sites in diverse communities may alleviate burdens of trial participation and improve enrollment of diverse patient groups

Longitudinal realist evaluation of the dementia PersonAlised care team (D-PACT) intervention: protocol

BACKGROUND: Different dementia support roles exist but evidence is lacking on which aspects are best, for whom and in what circumstance, and on their associated costs and benefits. Phase 1 of the Dementia PersonAlised Care Team programme (D-PACT), developed a post-diagnostic primary care-based intervention for people with dementia and their carers and assessed the feasibility of a trial. AIM: Phase 2 of the programme aims to 1) refine our programme theory on how, when and for whom the intervention works and 2) evaluate its value and impact. DESIGN & SETTING: A realist longitudinal mixed-methods evaluation will be conducted in urban, rural, and coastal areas across Southwest and Northwest England where low-income groups or ethnic minorities (eg, South Asian) are represented. Design was informed by patient, public and professional stakeholder input and Phase one findings. METHOD: High volume qualitative and quantitative data will be collected longitudinally from people with dementia, carers and practitioners. Analyses will comprise: 1) realist longitudinal case studies; 2) conversation analysis of recorded interactions; 3) statistical analyses of outcome and experience questionnaires; 4 a) health economic analysis examining costs of delivery; 4b) realist economic analysis of high-cost events and 'near misses'. All findings will be synthesised using a joint display table, evidence appraisal tool, triangulation and stakeholder co-analysis. CONCLUSION: Our realist evaluation will describe how, why and for whom the intervention leads (or not) to change over time; it also demonstrates how a non-randomised design can be more appropriate for complex interventions with similar questions or populations

A Digital Repository and Execution Platform for Interactive Scholarly Publications in Neuroscience

The CARMEN Virtual Laboratory (VL) is a cloud-based platform which allows neuroscientists to store, share, develop, execute, reproduce and publicise their work. This paper describes new functionality in the CARMEN VL: an interactive publications repository. This new facility allows users to link data and software to publications. This enables other users to examine data and software associated with the publication and execute the associated software within the VL using the same data as the authors used in the publication. The cloud-based architecture and SaaS (Software as a Service) framework allows vast data sets to be uploaded and analysed using software services. Thus, this new interactive publications facility allows others to build on research results through reuse. This aligns with recent developments by funding agencies, institutions, and publishers with a move to open access research. Open access provides reproducibility and verification of research resources and results. Publications and their associated data and software will be assured of long-term preservation and curation in the repository. Further, analysing research data and the evaluations described in publications frequently requires a number of execution stages many of which are iterative. The VL provides a scientific workflow environment to combine software services into a processing tree. These workflows can also be associated with publications and executed by users. The VL also provides a secure environment where users can decide the access rights for each resource to ensure copyright and privacy restrictions are met

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy

Development and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort.

Some patients with breast cancer treated by surgery and radiation therapy experience clinically significant toxicity, which may adversely affect cosmesis and quality of life. There is a paucity of validated clinical prediction models for radiation toxicity. We used machine learning (ML) algorithms to develop and optimise a clinical prediction model for acute breast desquamation after whole breast external beam radiation therapy in the prospective multicenter REQUITE cohort study. Using demographic and treatment-related features (m = 122) from patients (n = 2058) at 26 centers, we trained 8 ML algorithms with 10-fold cross-validation in a 50:50 random-split data set with class stratification to predict acute breast desquamation. Based on performance in the validation data set, the logistic model tree, random forest, and naïve Bayes models were taken forward to cost-sensitive learning optimisation. One hundred and ninety-two patients experienced acute desquamation. Resampling and cost-sensitive learning optimisation facilitated an improvement in classification performance. Based on maximising sensitivity (true positives), the "hero" model was the cost-sensitive random forest algorithm with a false-negative: false-positive misclassification penalty of 90:1 containing m = 114 predictive features. Model sensitivity and specificity were 0.77 and 0.66, respectively, with an area under the curve of 0.77 in the validation cohort. ML algorithms with resampling and cost-sensitive learning generated clinically valid prediction models for acute desquamation using patient demographic and treatment features. Further external validation and inclusion of genomic markers in ML prediction models are worthwhile, to identify patients at increased risk of toxicity who may benefit from supportive intervention or even a change in treatment plan. [Abstract copyright: © 2022 The Authors.

Longitudinal realist evaluation of the dementia PersonAlised care team (D-PACT) intervention: protocol

Background Different dementia support roles exist but evidence is lacking on which aspects are best, for whom and in what circumstance, and on their associated costs and benefits. Phase 1 of the Dementia PersonAlised Care Team programme (D-PACT), developed a post-diagnostic primary care-based intervention for people with dementia and their carers and assessed the feasibility of a trial. AimPhase 2 of the programme aims to 1) refine our programme theory on how, when and for whom the intervention works and 2) evaluate its value and impact. Design & setting A realist longitudinal mixed-methods evaluation will be conducted in urban, rural, and coastal areas across Southwest and Northwest England where low-income groups or ethnic minorities (eg, South Asian) are represented. Design was informed by patient, public and professional stakeholder input and Phase one findings. Method High volume qualitative and quantitative data will be collected longitudinally from people with dementia, carers and practitioners. Analyses will comprise: 1) realist longitudinal case studies; 2) conversation analysis of recorded interactions; 3) statistical analyses of outcome and experience questionnaires; 4 a) health economic analysis examining costs of delivery; 4b) realist economic analysis of high-cost events and ‘near misses’. All findings will be synthesised using a joint display table, evidence appraisal tool, triangulation and stakeholder co-analysis. Conclusion Our realist evaluation will describe how, why and for whom the intervention leads (or not) to change over time; it also demonstrates how a non-randomised design can be more appropriate for complex interventions with similar questions or populations