Ortizbjerregaard1717

Clinical safety signal detection is of great importance in establishing the safety profile of new drugs and biologics during drug development. Bayesian hierarchical meta-analysis has proven to be a very effective method of identifying potential safety signals by considering the hierarchical structure of clinical safety data from multiple randomized clinical trials conducted under an Investigational New Drug (IND) application or Biological License Application (BLA). This type of model can integrate information across studies, for instance by grouping related adverse events using the MedDRA system-organ-class (SOC) and preferred terms (PT). It therefore improves the precision of parameter estimates compared to models that do not consider the hierarchical structure of the safety data. We propose to extend an existing four-stage Bayesian hierarchical model and consider the exposure adjusted incidence rate, assuming the number of adverse events (AEs) follows a Poisson distribution. The proposed model is applied to a real-world example, using data from three randomized clinical trials of a neuroscience drug and examine in three simulation studies motivated by real-world examples. Comparison is made between the proposed method and other existing methods. The simulation results indicate that our proposed model outperforms other two candidate models in terms of power and false detection rate.

Programs such as the Pediatric Access Line in Washington state have shown decreases in antipsychotic medication use by youth with non-psychotic disorders. Program outcomes have been studied with observational designs. This manuscript describes the protocol for Targeted and Safer Use of Antipsychotics in Youth (SUAY), a randomized controlled trial of psychiatrist review of prescriptions and facilitated access to psychosocial care. The aim of the intervention is to reduce the number of person-days of antipsychotic use among participants.

Recruitment occurs at 4 health systems. Targeted enrollment is 800 youth aged 3-17years. Clinicians are block randomized to intervention versus usual care prior to the study. Youth are nested within the arm of the prescribing clinician. Clinicians in the intervention group receive an EHR-based best practice alert with options to expedite access to psychosocial care and all medication orders are reviewed by a child and adolescent psychiatrist with feedback provided to the prescriber. The primary outcome is person-days of antipsychotic medication use in the 6months following the initial order. All randomized individuals contribute data regardless of their level of participation (including declining all services).

The trial has been approved by the institutional review boards at each of the 4 sites. SB-743921 molecular weight The intervention has 4 novel design features including automated recruitment using a best practice alert, psychiatrist medication order review and consultation, telephone navigation to psychosocial care, and telemental health visits. Recruitment began in March of 2018 and will be completed in June 2020. Follow-up will be completed December 31, 2020.

Clinicaltrials.gov, NCT03448575.

Clinicaltrials.gov, NCT03448575.Lifestyle interventions to increase exercise and improve diet have been the focus of recent clinical trials to potentially prevent Alzheimer's disease (AD). However, despite the strong links between sleep disruptions, cognitive decline, and AD, sleep enhancement has yet to be targeted as a lifestyle intervention to prevent AD. A recent meta-analysis suggests that approximately 15% of AD may be prevented by an efficacious intervention aimed to reduce sleep disturbances and sleep disorders. Chronic insomnia is the most frequent sleep disorder occurring in at least 40% of older adults. Individuals with insomnia are more likely to be diagnosed with Alzheimer's Disease (AD) and demonstrate decline in cognitive function at long-term follow-up. AD is characterized by the accumulation of amyloid-β (Aβ) plaques and tau tangles in the brain, and growing evidence shows impaired sleep contributes to the accumulation of Aβ. An intervention aimed at improving insomnia may be a critical opportunity for primary prevention to slow cognitive decline and potentially delay the onset of AD. Cognitive behavioral therapy for insomnia (CBT-I) is an efficacious treatment for insomnia, but the use of CBT-I to improve cognitive function and potentially reduce the rate of Aβ accumulation has never been examined. Therefore, the objective of the proposed study is to examine the efficacy of CBT-I on improving cognitive function in older adults with symptoms of insomnia. An exploratory aim is to assess the effect of CBT-I on rate of Aβ accumulation.Atrial fibrillation (AF) is associated with an increased risk of stroke, enhanced stroke severity, and other comorbidities. However, AF is often asymptomatic, and frequently remains undiagnosed until complications occur. Current screening approaches for AF lack either cost-effectiveness or diagnostic sensitivity; thus, there is interest in tools that could be used for population screening. An AF risk prediction algorithm, developed using machine learning from a UK dataset of 2,994,837 patients, was found to be more effective than existing models at identifying patients at risk of AF. Therefore, the aim of the trial is to assess the effectiveness of this risk prediction algorithm combined with diagnostic testing for the identification of AF in a real-world primary care setting. Eligible participants (aged ≥30 years and without an existing AF diagnosis) registered at participating UK general practices will be randomised into intervention and control arms. Intervention arm participants identified at highest risk of developing AF (algorithm risk score ≥ 7.4%) will be invited for a 12‑lead electrocardiogram (ECG) followed by two-weeks of home-based ECG monitoring with a KardiaMobile device. Control arm participants will be used for comparison and will be managed routinely. The primary outcome is the number of AF diagnoses in the intervention arm compared with the control arm during the research window. If the trial is successful, there is potential for the risk prediction algorithm to be implemented throughout primary care for narrowing the population considered at highest risk for AF who could benefit from more intensive screening for AF. Trial Registration NCT04045639.