Mapping the disease-specific LupusQoL to the SF-6D

Purpose To derive a mapping algorithm to predict SF-6D utility scores from the non-preference-based LupusQoL and test the performance of the developed algorithm on a separate independent validation data set. Method LupusQoL and SF-6D data were collected from 320 patients with systemic lupus erythematosus (SLE) attending routine rheumatology outpatient appointments at seven centres in the UK. Ordinary least squares (OLS) regression was used to estimate models of increasing complexity in order to predict individuals’ SF-6D utility scores from their responses to the LupusQoL questionnaire. Model performance was judged on predictive ability through the size and pattern of prediction errors generated. The performance of the selected model was externally validated on an independent data set containing 113 female SLE patients who had again completed both the LupusQoL and SF-36 questionnaires. Results Four of the eight LupusQoL domains (physical health, pain, emotional health, and fatigue) were selected as dependent variables in the final model. Overall model fit was good, with R2 0.7219, MAE 0.0557, and RMSE 0.0706 when applied to the estimation data set, and R2 0.7431, MAE 0.0528, and RMSE 0.0663 when applied to the validation sample. Conclusion This study provides a method by which health state utility values can be estimated from patient responses to the non-preference-based LupusQoL, generalisable beyond the data set upon which it was estimated. Despite concerns over the use of OLS to develop mapping algorithms, we find this method to be suitable in this case due to the normality of the SF-6D data

DIA1R Is an X-Linked Gene Related to Deleted In Autism-1

Background: Autism spectrum disorders (ASDs) are frequently occurring disorders diagnosed by deficits in three core functional areas: social skills, communication, and behaviours and/or interests. Mental retardation frequently accompanies the most severe forms of ASDs, while overall ASDs are more commonly diagnosed in males. Most ASDs have a genetic origin and one gene recently implicated in the etiology of autism is the Deleted-In-Autism-1 (DIA1) gene. Methodology/Principal Findings: Using a bioinformatics-based approach, we have identified a human gene closely related to DIA1, we term DIA1R (DIA1-Related). While DIA1 is autosomal (chromosome 3, position 3q24), DIA1R localizes to the X chromosome at position Xp11.3 and is known to escape X-inactivation. The gene products are of similar size, with DIA1 encoding 430, and DIA1R 433, residues. At the amino acid level, DIA1 and DIA1R are 62 % similar overall (28 % identical), and both encode signal peptides for targeting to the secretory pathway. Both genes are ubiquitously expressed, including in fetal and adult brain tissue. Conclusions/Significance: Examination of published literature revealed point mutations in DIA1R are associated with X-linked mental retardation (XLMR) and DIA1R deletion is associated with syndromes with ASD-like traits and/or XLMR. Together, these results support a model where the DIA1 and DIA1R gene products regulate molecular traffic through the cellular secretory pathway or affect the function of secreted factors, and functional deficits cause disorders with ASD-lik

Understanding how and why de-implementation works in health and care: research protocol for a realist synthesis of evidence

Background Strategies to improve the effectiveness and quality of health and care have predominantly emphasised the implementation of new research and evidence into service organisation and delivery. A parallel, but less understood issue is how clinicians and service leaders stop existing practices and interventions that are no longer evidence based, where new evidence supersedes old evidence, or interventions are replaced with those that are more cost effective. The aim of this evidence synthesis is to produce meaningful programme theory and practical guidance for policy makers, managers and clinicians to understand how and why de-implementation processes and procedures can work. Methods and analysis The synthesis will examine the attributes or characteristics that constitute the concept of de-implementation. The research team will then draw on the principles of realist inquiry to provide an explanatory account of how, in what context and for whom to explain the successful processes and impacts of de-implementation. The review will be conducted in four phases over 18 months. Phase 1: develop a framework to map the preliminary programme theories through an initial scoping of the literature and consultation with key stakeholders. Phase 2: systematic searches of the evidence to develop the theories identified in phase 1. Phase 3: validation and refinement of programme theories through stakeholder interviews. Phase 4: formulating actionable recommendations for managers, commissioners and service leaders about what works through different approaches to de-implementation. Discussion This evidence synthesis will address gaps in knowledge about de-implementation across health and care services and ensure that guidance about strategies and approaches accounts for contextual factors, which may be operating at different organisational and decision-making levels. Through the development of the programme theory, which explains what works, how and under which circumstances, findings from the evidence synthesis will support managers and service leaders to make measured decisions about de-implementation. Systematic review registration PROSPERO CRD4201708103