Improving metabolic parameters of antipsychotic child treatment (IMPACT) study: rationale, design, and methods

BACKGROUND: Youth with serious mental illness may experience improved psychiatric stability with second generation antipsychotic (SGA) medication treatment, but unfortunately may also experience unhealthy weight gain adverse events. Research on weight loss strategies for youth who require ongoing antipsychotic treatment is quite limited. The purpose of this paper is to present the design, methods, and rationale of the Improving Metabolic Parameters in Antipsychotic Child Treatment (IMPACT) study, a federally funded, randomized trial comparing two pharmacologic strategies against a control condition to manage SGA-related weight gain. METHODS: The design and methodology considerations of the IMPACT trial are described and embedded in a description of health risks associated with antipsychotic-related weight gain and the limitations of currently available research. RESULTS: The IMPACT study is a 4-site, six month, randomized, open-label, clinical trial of overweight/obese youth ages 8–19 years with pediatric schizophrenia-spectrum and bipolar-spectrum disorders, psychotic or non-psychotic major depressive disorder, or irritability associated with autistic disorder. Youth who have experienced clinically significant weight gain during antipsychotic treatment in the past 3 years are randomized to either (1) switch antipsychotic plus healthy lifestyle education (HLE); (2) add metformin plus HLE; or (3) HLE with no medication change. The primary aim is to compare weight change (body mass index z-scores) for each pharmacologic intervention with the control condition. Key secondary assessments include percentage body fat, insulin resistance, lipid profile, psychiatric symptom stability (monitored independently by the pharmacotherapist and a blinded evaluator), and all-cause and specific cause discontinuation. This study is ongoing, and the targeted sample size is 132 youth. CONCLUSION: Antipsychotic-related weight gain is an important public health issue for youth requiring ongoing antipsychotic treatment to maintain psychiatric stability. The IMPACT study provides a model for pediatric research on adverse event management using state-of-the art methods. The results of this study will provide needed data on risks and benefits of two pharmacologic interventions that are already being used in pediatric clinical settings but that have not yet been compared directly in randomized trials. TRIAL REGISTRATION: Clinical Trials.gov NCT0080623

AGILE: a seamless phase I/IIa platform for the rapid evaluation of candidates for COVID-19 treatment: an update to the structured summary of a study protocol for a randomised platform trial letter.

Funder: UnitaidBACKGROUND: There is an urgent unmet clinical need for the identification of novel therapeutics for the treatment of COVID-19. A number of COVID-19 late phase trial platforms have been developed to investigate (often repurposed) drugs both in the UK and globally (e.g. RECOVERY led by the University of Oxford and SOLIDARITY led by WHO). There is a pressing need to investigate novel candidates within early phase trial platforms, from which promising candidates can feed into established later phase platforms. AGILE grew from a UK-wide collaboration to undertake early stage clinical evaluation of candidates for SARS-CoV-2 infection to accelerate national and global healthcare interventions. METHODS/DESIGN: AGILE is a seamless phase I/IIa platform study to establish the optimum dose, determine the activity and safety of each candidate and recommend whether it should be evaluated further. Each candidate is evaluated in its own trial, either as an open label single arm healthy volunteer study or in patients, randomising between candidate and control usually in a 2:1 allocation in favour of the candidate. Each dose is assessed sequentially for safety usually in cohorts of 6 patients. Once a phase II dose has been identified, efficacy is assessed by seamlessly expanding into a larger cohort. AGILE is completely flexible in that the core design in the master protocol can be adapted for each candidate based on prior knowledge of the candidate (i.e. population, primary endpoint and sample size can be amended). This information is detailed in each candidate specific trial protocol of the master protocol. DISCUSSION: Few approved treatments for COVID-19 are available such as dexamethasone, remdesivir and tocilizumab in hospitalised patients. The AGILE platform aims to rapidly identify new efficacious and safe treatments to help end the current global COVID-19 pandemic. We currently have three candidate specific trials within this platform study that are open to recruitment. TRIAL REGISTRATION: EudraCT Number: 2020-001860-27 14 March 2020 ClinicalTrials.gov Identifier: NCT04746183  19 February 2021 ISRCTN reference: 27106947

COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records

BACKGROUND: Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework. METHODS: In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status. FINDINGS: Among 57 032 174 individuals included in the cohort, 13 990 423 COVID-19 events were identified in 7 244 925 individuals, equating to an infection rate of 12·7% during the study period. Of 7 244 925 individuals, 460 737 (6·4%) were admitted to hospital and 158 020 (2·2%) died. Of 460 737 individuals who were admitted to hospital, 48 847 (10·6%) were admitted to the intensive care unit (ICU), 69 090 (15·0%) received non-invasive ventilation, and 25 928 (5·6%) received invasive ventilation. Among 384 135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23 485 [30·4%] of 77 202 patients) than wave 2 (44 220 [23·1%] of 191 528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50·7%] of 5063 patients). 15 486 (9·8%) of 158 020 COVID-19-related deaths occurred within 28 days of the first COVID-19 event without a COVID-19 diagnoses on the death certificate. 10 884 (6·9%) of 158 020 deaths were identified exclusively from mortality data with no previous COVID-19 phenotype recorded. We observed longer patient trajectories in wave 2 than wave 1. INTERPRETATION: Our analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources. FUNDING: British Heart Foundation Data Science Centre, led by Health Data Research UK

Recommendations for the Use of Social Media in Pharmacovigilance: Lessons from IMI WEB-RADR

A central problem in e-commerce is determining overlapping communities among individuals or objects in the absence of external identification or tagging. We address this problem by introducing a framework that captures the notion of communities or clusters determined by the relative affinities among their members. To this end we define what we call an affinity system, which is a set of elements, each with a vector characterizing its preference for all other elements in the set. We define a natural notion of (potentially overlapping) communities in an affinity system, in which the members of a given community collectively prefer each other to anyone else outside the community. Thus these communities are endogenously formed in the affinity system and are "self-determined" or "self-certified" by its members. We provide a tight polynomial bound on the number of self-determined communities as a function of the robustness of the community. We present a polynomial-time algorithm for enumerating these communities. Moreover, we obtain a local algorithm with a strong stochastic performance guarantee that can find a community in time nearly linear in the of size the community. Social networks fit particularly naturally within the affinity system framework -- if we can appropriately extract the affinities from the relatively sparse yet rich information from social networks, our analysis then yields a set of efficient algorithms for enumerating self-determined communities in social networks. In the context of social networks we also connect our analysis with results about $(\alpha,\beta)$-clusters introduced by Mishra, Schreiber, Stanton, and Tarjan \cite{msst}. In contrast with the polynomial bound we prove on the number of communities in the affinity system model, we show that there exists a family of networks with superpolynomial number of $(\alpha,\beta)$-clusters.Comment: 22 page