Contemporary clinical trials最新文献

Background

Becoming a parent is a transformative experience requiring multiple transitions, including the need to navigate several components of health care, manage any mental health issues, and develop and sustain an approach to infant feeding. Baby2Home (B2H) is a digital intervention built on the collaborative care model (CCM) designed to support families during these transitions to parenthood.

Objectives

We aim to investigate the effects of B2H on preventive healthcare utilization for the family unit and patient-reported outcomes (PROs) trajectories with a focus on mental health. We also aim to evaluate heterogeneity in treatment effects across social determinants of health including self-reported race and ethnicity and household income. We hypothesize that B2H will lead to optimized healthcare utilization, improved PROs trajectories, and reduced racial, ethnic, and income-based disparities in these outcomes as compared to usual care.

Methods

B2H is a multi-center, pragmatic, individual-level randomized controlled trial. We will enroll 640 families who will be randomized to: [1] B2H + usual care, or [2] usual care alone. Preventive healthcare utilization is self-reported and confirmed from medical records and includes attendance at the postpartum visit, contraception use, depression screening, vaccine uptake, well-baby visit attendance, and breastfeeding at 6 months. PROs trajectories will be analyzed after collection at 1 month, 2 months, 4 months, 6 months and 12 months. PROs include assessments of stress, depression, anxiety, self-efficacy and relationship health.

Implications

If B2H proves effective, it would provide a scalable digital intervention to improve care for families throughout the transition to new parenthood.

Background

Variable data quality poses a challenge to using electronic health record (EHR) data to ascertain acute clinical outcomes in multi-site clinical trials. Differing EHR platforms and data comprehensiveness across clinical trial sites, especially if patients received care outside of the clinical site's network, can also affect validity of results. Overcoming these challenges requires a structured approach.

Methods

We propose a framework and create a checklist to assess the readiness of clinical sites to contribute EHR data to a clinical trial for the purpose of outcome ascertainment, based on our experience with the Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) study, which enrolled 5451 participants in 86 primary care practices across 10 healthcare systems (sites).

Results

The site readiness checklist includes assessment of the infrastructure (i.e., size and structure of the site's healthcare system or clinical network), data procurement (i.e., quality of the data), and cost of obtaining study data. The checklist emphasizes the importance of understanding how data are captured and integrated across a site's catchment area and having a protocol in place for data procurement to ensure consistent and uniform extraction across each site.

Conclusions

We suggest rigorous, prospective vetting of the data quality and infrastructure of each clinical site before launching a multi-site trial dependent on EHR data. The proposed checklist serves as a guiding tool to help investigators ensure robust and unbiased data capture for their clinical trials.

Original trial registration number

NCT02475850

Heart failure (HF) affects six million people in the U.S., is associated with high morbidity, mortality, and healthcare utilization.^{(1, 2)} Despite a decade of innovation, the majority of interventions aimed at reducing hospitalization and readmissions in HF have not been successful.^(3–7) One reason may be that most have overlooked the role of home health aides and attendants (HHAs), who are often highly involved in HF care.^(8–13) Despite their contributions, studies have found that HHAs lack specific HF training and have difficulty reaching their nursing supervisors when they need urgent help with their patients. Here we describe the protocol for a pilot randomized control trial (pRCT) assessing a novel stakeholder-engaged intervention that provides HHAs with a) HF training (enhanced usual care arm) and b) HF training plus a mobile health application that allows them to chat with a nurse in real-time (intervention arm). In collaboration with the VNS Health of New York, NY, we will conduct a single-site parallel arm pRCT with 104 participants (HHAs) to evaluate the feasibility, acceptability, and effectiveness (primary outcomes: HF knowledge; HF caregiving self-efficacy) of the intervention among HHAs caring for HF patients. We hypothesize that educating and better integrating HHAs into the care team can improve their ability to provide support for patients and outcomes for HF patients as well (exploratory outcomes include hospitalization, emergency department visits, and readmission). This study offers a novel and potentially scalable way to leverage the HHA workforce and improve the outcomes of the patients for whom they care.

Clinical trial.gov registration: NCT04239911

Background

Adaptive trials usually require simulations to determine values for design parameters, demonstrate error rates, and establish the sample size. We designed a Bayesian adaptive trial comparing ventilation strategies for patients with acute hypoxemic respiratory failure using simulations. The complexity of the analysis would usually require computationally expensive Markov Chain Monte Carlo methods but this barrier to simulation was overcome using the Integrated Nested Laplace Approximations (INLA) algorithm to provide fast, approximate Bayesian inference.

Methods

We simulated two-arm Bayesian adaptive trials with equal randomization that stratified participants into two disease severity states. The analysis used a proportional odds model, fit using INLA. Trials were stopped based on pre-specified posterior probability thresholds for superiority or futility, separately for each state. We calculated the type I error and power across 64 scenarios that varied the probability thresholds and the initial minimum sample size before commencing adaptive analyses. Two designs that maintained a type I error below 5%, a power above 80%, and a feasible mean sample size were evaluated further to determine the optimal design.

Results

Power generally increased as the initial sample size and the futility threshold increased. The chosen design had an initial recruitment of 500 and a superiority threshold of 0.9925, and futility threshold of 0.95. It maintained high power and was likely to reach a conclusion before exceeding a feasible sample size.

Conclusions

We designed a Bayesian adaptive trial to evaluate novel strategies for ventilation using the INLA algorithm to efficiently evaluate a wide range of designs through simulation.

Background

The 2023 VA/DoD Clinical Practice Guideline for the Management of PTSD recommends individual, manualized trauma-focused such as Prolonged Exposure (PE) over pharmacologic interventions for the primary treatment of PTSD. Unfortunately, clinical trials of trauma-based therapies in the military and veteran population showed that 30% to 50% of patients did not demonstrate clinically meaningful symptom change. Ketamine, an FDA-approved anesthetic with potent non-competitive glutamatergic N-methyl-d-aspartate antagonistic properties, has demonstrated to enhance the recall of extinction learning and decrease fear renewal without interference of extinction training in preclinical studies.

Methods

We plan to conduct a single site RCT comparing three ketamine treatment vs. active placebo (midazolam) adjunct to PE therapy among Veterans with PTSD. Pharmacological phase will start simultaneously with PE session 1. Infusions will be administered 24 h. prior to PE session for the first 3 weeks. After PE is completed (session 10), patients will be assessed during a 3-month follow-up period at various time points. We estimate that out of 100 veterans, 80 will reach time point for primary outcome measure and will be considered for primary analysis. Secondary outcomes include severity of depression and anxiety scores, safety and tolerability of ketamine-enhanced PE therapy, cognitive performance during treatment and early improvement during PE related to the rate of dropouts during PE therapy.

Discussion

Results of the proposed RCT could provide scientific foundation to distinguish the essential components of this approach, enhance the methodology, elucidate the mechanisms involved, and identify sub-PTSD populations that most likely benefit from this intervention.

Traditional approaches in dose-finding trials, such as the continual reassessment method, focus on identifying the maximum tolerated dose. In contemporary early-phase dose-finding trials, especially in oncology with targeted agents or immunotherapy, a more relevant aim is to identify the lowest dose level that maximises efficacy whilst remaining tolerable. Backfilling, defined as the practice of assigning patients to dose levels lower than the current highest tolerated dose, has been proposed to gather additional pharmacokinetic, pharmacodynamic and biomarker data to recommend the most appropriate dose to carry forward for subsequent studies.

The first formal framework [5] for backfilling proposed randomising backfill patients with equal probability among those doses below the dose level where the study is currently at. Here, we propose to use Bayesian response-adaptive randomisation to backfill patients. This patient-oriented approach to backfilling aims to allocate more patients to dose levels in the backfill set with higher expected efficacy based on emerging data. The backfill set constitutes of the doses below the dose the dose-finding algorithm is at. At study completion, collective patient data inform the dose-response curve, suggesting an optimal dose level balancing toxicity and efficacy.

Our simulation study across diverse clinical trial settings demonstrates that a backfilling strategy using Bayesian response-adaptive randomisation can result in a patient-oriented patient assignment procedure which simultaneously enhances the likelihood of correctly identifying the most appropriate dose level. This contribution offers a methodological framework and practical implementation for patient-oriented backfilling, encompassing design and analysis considerations in early-phase trials.

Background

Wearable technology is used to monitor and motivate physical activity (PA) and provides continuous, objective PA and sleep data outside the clinical setting. We reviewed the literature to understand how wearables are integrated into prostate cancer (PC) investigations in order to identify current practices, gaps, and research opportunities.

Methods

We conducted a literature search for articles using wearables, among PC survivors published between 2012 and 2022. We extracted study details, interventions and outcomes, participant baseline characteristics, and device characteristics and grouped them by study type: randomized control trials (RCTs) and non-randomized studies.

Results

Of 354 articles screened, 44 met eligibility criteria (23 RCTs, and 21 non-randomized). 89% used wearables to monitor PA metrics, 11%, sleep metrics, and 6.8%, both. Most studies involved exercise (70% RCTs, 9% non-randomized studies) or lifestyle interventions (30% RCTs, 9% non-randomized studies). Intervention delivery methods included personalized computer-based (48%), in-person (e.g., trainer) (20%), and education web or print-based (20%). Interventions occurred at the participant's home (48%) or at a gym (20%). 57% of the studies evaluated the feasibility and acceptability of the wearable as an activity-measuring device or as part of a remotely delivered computer-based intervention. Studies used wearables to monitor adherence to PA interventions, motivate behavior change, to assess patient outcomes (e.g., patient function, quality of life, mood), or as data collection tools.

Conclusions

Wearables are primarily being used to assess daily activity and monitor adherence to exercise interventions in clinical studies involving PC survivors. Findings suggest that they are feasible for use in this population. More research is needed to understand how to integrate wearables into routine clinical care, expand their use to predict clinical outcomes, or to deliver tailored interventions for PC survivors.

Platform trials are generally regarded as an innovative approach to address clinical valuation of early stage candidates, regardless of modality as the evidence evolves. As a type of randomized clinical trial (RCT) design construct in which multiple interventions are evaluated concurrently against a common control group allowing new interventions to be added and the control group to be updated throughout the trial, they provide a dynamic and efficient mechanism to compare and potentially discriminate new treatment candidates. Their recent use in the evaluation of new therapies for COVID-19 has spurred new interest in the approach. The paucity of platform trials is less influenced by the novelty and operational requirements as opposed to concerns regarding the sharing of intellectual property (IP) and the lack of infrastructure to operationalize the conduct in the context of IP and data sharing. We provide a mechanism how this can be accomplished through the use of a digital research environment (DRE) providing a safe and secure platform for clinical researchers, quantitative and physician scientists to analyze and develop tools (e.g., models) on sensitive data with the confidence that the data and models developed are protected. A DRE, in this context, expands on the concept of a trusted research environment (TRE) by providing remote access to data alongside tools for analysis in a securely controlled workspace, while allowing data and tools to be findable, accessible, interoperable, and reusable (FAIR), version-controlled, and dynamically grow in size or quality as a result of each treatment evaluated in the trial.

Family-based behavioral treatment (FBT) is one of the most effective treatments for childhood obesity. These programs include behavior change strategies and basic parenting training to help parents make healthy diet and physical activity changes for their children. While effective, not all families respond to this program. Additional training on how to effectively deliver these behavior change strategies may improve outcomes. The authoritative parenting style is associated with many positive academic and socio-emotional outcomes in children, and is characterized by displays of warmth and support while also being consistent with setting limits and boundaries. This parenting style has also been associated with normal weight status. Furthermore, parenting training programs that promote this parenting style for children with behavioral issues have shown unintended effects on decreasing child weight status. Therefore, our goal was to examine the effect of adding more intensive parenting training to FBT on child weight status. We randomized 140 children and their parent to either FBT or FBT + Parenting Training (FBT + PT). Assessments were conducted at baseline, mid-treatment (month 3), post-treatment (month 6), 6-month follow-up (month 12), and 12-month follow-up (month 18). Primary outcome was change in child weight status. Secondary outcomes were rates of drop-out, treatment adherence, and acceptability. If effective, this program may provide another alternative for families to help improve outcomes in childhood obesity management.

Purpose

Chronic neck pain (CNP) is prevalent and challenging to treat. Despite evidence of massage's effectiveness for CNP, multiple accessibility barriers exist. The Trial Outcomes for Massage: Care Ally-Assisted vs. Therapist Treated (TOMCATT) study examined a care ally-assisted massage (CA-M) approach compared to a waitlist control prior to a study design modification (WL-C₀).

Methods

CA-M consisted of in-person training for veteran/care-ally dyads to learn a standardized 30-minue massage routine, instructional DVD, and printed treatment manual. Participants were to complete three care ally-assisted massage sessions weekly for 12-weeks. Outcomes collected at baseline, 1-, 3-, and 6-months included validated measures of neck pain severity and associated disability. Linear mixed-model approaches were used for analysis with 3-months as the primary outcome timepoint.

Results

Participants (N = 203) were 56.7 ± 14 years old, 75% White, 15% female, and 75% married/partnered. Among 102 CA-M participants, 45% did not attend the in-person training and subsequently withdrew from the study and were more likely to be younger (p = .016) and employed (p = .004). Compared to WL-C₀, CA-M participants had statistically significant reductions in pain-related disability at 3-months (−3.4, 95%CI = [−5.8, −1.0]; p = .006) and 6-months (−4.6, 95%CI = [−7.0, −2.1]; p < .001) and pain severity at 3-months (−1.3, 95%CI = [−1.9, −0.8]; p < .001) and 6-months (−1.0, 95%CI = [−1.6, −0.4]; p = .007), respectively.

Conclusion

In this analysis, CA-M led to greater reductions in CNP with disability and pain severity compared to WL-C₀, despite treatment engagement and retention challenges. Future work is needed to determine how to better engage Veterans and their care-allies to attend CA-M training.