Improving Prediction of Systemic Statin Exposure Using Concomitant Medications, Non-Linear Modelling and Novel SNP Discovery

Introduction: Statin drugs are a highly efficacious treatment for hypercholesterolemia and adherent treatment with statins reduces the risk of cardiovascular disease. Although statins are generally well tolerated, myalgia (muscle pain) is a common side effect and can lead to non-compliance with treatment. Increased systemic exposure may contribute to the development of myalgia. Drug-drug interactions inhibiting statin metabolism and impaired drug transporter function may lead to decreased statin clearance. Establishing accurate predictive models is an important step towards preventing adverse drug events by titrating statin dosing to limit systemic exposure. Objectives: 1) To develop an algorithm to select concomitant medications for incorporation into the existing systemic exposure model and assess their predictive impact; 2) to apply nonlinear techniques to model systemic statin exposure, and assess their effectiveness and feasibility; 3) to identify novel genes and corresponding single nucleotide polymorphisms by NGS in patients whose statin plasma concentrations were under-predicted using the original systemic exposure model to guide future biological research. Methods: Data from a previously-collected prospective cohort of 130 patients prescribed rosuvastatin and 128 patients prescribed atorvastatin were used in this analysis. Concomitant medications were selected using penalized. Stable feature selection was achieved by repeated cross validation. Generalized additive models (GAMs) and support vector regression (SVR) were assessed as candidate nonlinear models. Candidate patients were chosen for NGS sequencing based on the proportional difference between their true and predicted values from the original systemic exposure model. Variant prioritization used the Sequence Kernel Association Test. Results: Atorvastatin linear model fit was statistically significantly improved by incorporating the selected concomitant medications, but rosuvastatin model fit was not. Predictive performance was not improved using GAMs or SVR compared to linear regression, likely due to small sample size. Three candidate genes and corresponding observed variants were identified and discussed as potential predictors of systemic rosuvastatin exposure. Conclusion: Linear modelling of systemic atorvastatin exposure can be improved by incorporating concomitant medications. The feasibility of using nonlinear predictive models is limited by small sample size. Future research on newly identified interacting medication and genetic variants may provide new insights regarding underlying molecular mechanisms affecting systemic statin exposure

gLOP: A Cleaner Dirty Model for Multitask Learning

Multitask learning (MTL) was originally defined by Caruana (1997) as "an approach to inductive transfer that improves learning for one task by using the information contained in the training signals of other related tasks". In the linear model setting this is often realized as joint feature selection across tasks, where features (but not necessarily coefficient values) are shared across tasks. In later work related to MTL Jalali (2010) observed that sharing all features across all tasks is too restrictive in some cases, as commonly used composite absolute penalties (like the l(1,∞) norm) encourage not only common feature selection but also common parameter values between settings. Because of this, Jalali proposed an alternative "dirty model" that can leverage shared features even in the case where not all features are shared across settings. The dirty model decomposes the coefficient matrix Θ into a row-sparse matrix B and an elementwise sparse matrix S in order to better capture structural differences between tasks. Multitask learning problems arise in many contexts, and one of the most pertinent of these is healthcare applications in which we must use data from multiple patients to learn a common predictive model. Often it is impossible to gather enough data from any one patient to accurately train a full predictive model for that patient. Additionally, learning in this context is complicated by the presence of individual differences between patients as well as population-wide effects common to most patients, leading to the need for a dirty model. Two additional challenges for methods applied in the healthcare setting include the need for scalability so that the model can work with big data, and the need for interpretable models. While Jalali gives us a dirty model, this method does not scale as well as many other commonly used methods like the Lasso, and does not have a clean interpretation. This is particularly true in the healthcare domain, as this model does not allow us to interpret coefficients in relation to all settings. Because B coefficients in the dirty model paradigm are not required to be the same for all settings for a particular feature, departures from the global model may be captured in B or S leading to ambiguity in interpreting potential main effects. We propose a "cleaner" dirty model gLOP (global/LOcal Penalty) that is capable of representing global effects between settings as well as local setting-specific effects, much like the ANalysis Of VAriance (ANOVA) test in inferential statistics. However, the goal of the ANOVA is not to build an accurate predictive model, but to identify coefficients that are non-zero at a given level of statistical significance. By combining the dirty model's decomposed Θ matrix and the underlying concept behind the ANOVA, we get the best of both worlds: an interpretable predictive model that can accurately recover the underlying structure of a given problem. gLOP is structured as a coordinate minimization problem which decomposes Θ into a global vector of coefficients g and a matrix of local setting-specific coefficients L. At each step, g is updated using the standard Lasso paradigm applied to the composite global design matrix in which the design matrices from each setting are concatenated vertically. In contrast, L is updated at each step using the standard Lasso paradigm applied separately to each setting. Another significant advantage of our model gLOP in comparison to previous dirty models is the out-of-the-box use of standard Lasso implementations instead of less frequently implemented CAP family penalties such as the l(1,∞) norm. Additionally, gLOP has a significant advantage in lowered computational time demands as it takes larger steps towards the global optimum at each iteration. We present experimental results comparing both the runtime and structure recovered by gLOP to Jalali's dirty model

Inaccessible Justice : What Happens to Workers Who Don't Pursue Employment Claims?

This report details findings from a pilot study that considered the experiences of workers who faced a workplace dispute that constituted a potential claim in the Employment Tribunal (ET) but, for whatever reason, did not pursue that claim. Two specific questions were examined. Firstly, why workers with employment disputes do not pursue potential claims against their employers. Secondly, what the costs are (financial, emotional and otherwise) for these workers of not formally accessing justice. The impetus for this work is the recent policy changes that have been introduced by the Coalition and Conservative governments to deter workers from taking claims to the ET. These include the requirement that workers must now be employed for a period of two years, instead of one, before they have the right to make a claim for unfair dismissal and the introduction of fees to take a claim to the ET. The government has largely justified these changes on the basis of the high level of costs arising from workers pursuing claims in the ET. It is claimed that such expenses are disproportionately borne by employers, in terms of time and money, and society, in terms of public expenditure and economic growth

Girls’ and boys’ problem talk: Implications for emotional closeness in friendships.

This research highlights the critical role of gender in the context of problem talk and social support in adolescents’ friendships. Early- and middle-adolescents’ (N = 314 friend dyads; Ms = 13.01 and 16.03 years) conversations about problems were studied using observation and a short-term longitudinal design. Mean-level gender differences emerged in that girls participated in problem talk more than boys and responded in a more positive and engaged manner to friends’ statements about problems (e.g., by saying something supportive, asking a question) than did boys. Interestingly, boys used humor during problem talk more than girls. Despite mean-level differences, there were not gender differences in the functional significance of participating in problem talk and positive engaged responses in that these behaviors predicted increased friendship closeness for both boys and girls. In contrast, humor during problem talk predicted increased closeness only for boys, highlighting an understudied pathway to closeness in boys’ friendships

Intergroup Contact, Social Dominance and Environmental Concern: A Test of the Cognitive-Liberalization Hypothesis

Intergroup contact is among the most effective ways to improve intergroup attitudes. While it is now beyond any doubt that contact can reduce prejudice, in this paper we provide evidence that its benefits can extend beyond intergroup relations – a process referred to as cognitive liberalization (Hodson, Crisp, Meleady & Earle, 2018). We focus specifically on the impact of intergroup contact on environmentally-relevant attitudes and behavior. Recent studies suggest that support for an inequality-based ideology (Social Dominance Orientation) can predict both intergroup attitudes and broader environmental conduct. Individuals higher in SDO are more willing to exploit the environment in unsustainable ways because doing so aids the production and maintenance of hierarchical social structures. In four studies conducted with British adults we show that by promoting less hierarchical and more egalitarian viewpoints (reduced SDO), intergroup contact encourages more environmentally responsible attitudes and behavior. Both cross-sectional and longitudinal data support this model. Effects are more strongly explained by reductions in an anti-egalitarian motive (SDO-E) than a dominance motive (SDO-D). We discuss how these findings help define an expanded vision for intergroup contact theory that moves beyond traditional conflict-related outcomes

Earlier cancer diagnosis in primary care: a feasibility economic analysis of ThinkCancer!

BackgroundUK cancer survival rates are much lower compared with other high-income countries. In primary care, there are opportunities for GPs and other healthcare professionals to act more quickly in response to presented symptoms that might represent cancer. ThinkCancer! is a complex behaviour change intervention aimed at primary care practice teams to improve the timely diagnosis of cancer.AimTo explore the costs of delivering the ThinkCancer! intervention to expedite cancer diagnosis in primary care.Design & settingFeasibility economic analysis using a micro-costing approach, which was undertaken in 19 general practices in Wales, UK.MethodFrom an NHS perspective, micro-costing methodology was used to determine whether it was feasible to gather sufficient economic data to cost the ThinkCancer!InterventionOwing to the COVID-19 pandemic, ThinkCancer! was mainly delivered remotely online in a digital format. Budget impact analysis (BIA) and sensitivity analysis were conducted to explore the costs of face-to-face delivery of the ThinkCancer! intervention as intended pre-COVID-19.ResultsThe total costs of delivering the ThinkCancer! intervention across 19 general practices in Wales was £25 030, with an average cost per practice of £1317 (standard deviation [SD]: 578.2). Findings from the BIA indicated a total cost of £34 630 for face-to-face delivery.ConclusionData collection methods were successful in gathering sufficient health economics data to cost the ThinkCancer!InterventionResults of this feasibility study will be used to inform a future definitive economic evaluation alongside a pragmatic randomised controlled trial (RCT)

Feminizing care pathways: Mixed‐methods study of reproductive options, decision making, pregnancy, post‐natal care and parenting amongst women with kidney disease

Aims: To identify the needs, experiences and preferences of women with kidney disease in relation to their reproductive health to inform development of shared decision‐making interventions. Design: UK‐wide mixed‐methods convergent design (Sep 20–Aug 21). Methods: Online questionnaire (n = 431) with validated components. Purposively sampled semi‐structured interviews (n = 30). Patient and public input throughout. Findings: Kidney disease was associated with defeminization, negatively affecting current (sexual) relationships and perceptions of future life goals. There was little evidence that shared decision making was taking place. Unplanned pregnancies were common, sometimes influenced by poor care and support and complicated systems. Reasons for (not) wanting children varied. Complicated pregnancies and miscarriages were common. Women often felt that it was more important to be a “good mother” than to address their health needs, which were often unmet and unrecognized. Impacts of pregnancy on disease and options for alternates to pregnancy were not well understood. Conclusion: The needs and reproductive priorities of women are frequently overshadowed by their kidney disease. High‐quality shared decision‐making interventions need to be embedded as routine in a feminized care pathway that includes reproductive health. Research is needed in parallel to examine the effectiveness of interventions and address inequalities. Impact: We do not fully understand the expectations, needs, experiences and preferences of women with kidney disease for planning and starting a family or deciding not to have children. Women lack the knowledge, resources and opportunities to have high‐quality conversations with their healthcare professionals. Decisions are highly personal and related to a number of health, social and cultural factors; individualized approaches to care are essential. Healthcare services need to be redesigned to ensure that women are able to make informed choices about pregnancy and alternative routes to becoming a parent. Patient or Public Contribution: The original proposal for this research came from listening to the experiences of women in clinic who reported unmet needs and detailed experiences of their pregnancies (positive and negative). A patient group was involved in developing the funding application and helped to refine the objectives by sharing their experiences. Two women who are mothers living with kidney disease were co‐opted as core members of the research team. We hosted an interim findings event and invited patients and wider support services (adoption, fertility, surrogacy, education and maternal chronic kidney disease clinics) from across the UK to attend. We followed the UK national standards for patient and public involvement throughout

A collaboratively produced model of service design for children and young people with common mental health problems

Background: Little is known about the effectiveness of, and implementation complexities associated with, service delivery models for children and young people (CYP) experiencing ‘common’ mental health problems such as anxiety, depression, behavioural difficulties and self-harm. This paper outlines how a model for high-quality service design for this population group was developed by identifying available services, their effectiveness, cost-effectiveness and acceptability, and the barriers and enablers to access. Methods: Sequential, mixed-methods design, combining evidence syntheses (scoping and integrative reviews of the international literature) with primary research (a collective case study in England and Wales). Data from these two elements were collaboratively synthesised in a subsequent model-building phase. Results: The scoping review yielded a service model typology. The integrative review found effectiveness evidence only for four models: collaborative care (the only service model to also have cost-effectiveness evidence), outreach approaches, brief intervention services and an organisational framework called ‘Availability, Responsiveness and Continuity’. No service model seemed more acceptable than others. Three case study themes were identified: pathways to support; service engagement; and learning and understanding. The model-building phase identified rapid access, learning self-care skills, individualised support, clear information, compassionate and competent staff and aftercare planning as core characteristics of high-quality services. These characteristics were underpinned by four organisational qualities: values that respect confidentiality; engagement and involvement; collaborative relationships; and a learning culture. Conclusions: A consistent organisational evidence-base for service design and delivery in CYP’s mental health spanning many years appears to have had little impact on service provision in England and Wales. Rather than impose – often inflexible and untested – specific local or national models or frameworks, those commissioning, designing and delivering mental health services for CYP should (re)focus on already known, fundamental components necessary for high-quality services. These fundamental components have been integrated into a collaboratively produced general model of service design for CYP with common mental health problems. While this general model is primarily focused on British service provision, it is broad enough to have utility for international audiences

Protocol for a randomised controlled trial of a school based cognitive behaviour therapy (CBT) intervention to prevent depression in high risk adolescents (PROMISE)

<p>Abstract</p> <p>Background</p> <p>Depression in adolescents is a significant problem that impairs everyday functioning and increases the risk of severe mental health disorders in adulthood. Relatively few adolescents with depression are identified and referred for treatment indicating the need to investigate alternative preventive approaches.</p> <p>Study Design</p> <p>A pragmatic cluster randomised controlled trial evaluating the effectiveness of a school based prevention programme on symptoms of depression in "high risk" adolescents (aged 12-16). The unit of allocation is year groups (n = 28) which are assigned to one of three conditions: an active intervention based upon cognitive behaviour therapy, attention control or treatment as usual. Assessments will be undertaken at screening, baseline, 6 months and 12 months. The primary outcome measure is change on the Short Mood and Feeling Questionnaire at 12 months. Secondary outcome measures will assess changes in negative thoughts, self esteem, anxiety, school connectedness, peer attachment, alcohol and substance misuse, bullying and self harm.</p> <p>Discussion</p> <p>As of August 2010, all 28 year groups (n = 5023) had been recruited and the assigned interventions delivered. Final 12 month assessments are scheduled to be completed by March 2011.</p> <p>Trial Registration</p> <p>ISRCTN19083628</p