Journal of Clinical and Translational Science最新文献

Accelerating COVID-19 Treatment Interventions and Vaccines (ACTIV) was initiated by the US government to rapidly develop and test vaccines and therapeutics against COVID-19 in 2020. The ACTIV Therapeutics-Clinical Working Group selected ACTIV trial teams and clinical networks to expeditiously develop and launch master protocols based on therapeutic targets and patient populations. The suite of clinical trials was designed to collectively inform therapeutic care for COVID-19 outpatient, inpatient, and intensive care populations globally. In this report, we highlight challenges, strategies, and solutions around clinical protocol development and regulatory approval to document our experience and propose plans for future similar healthcare emergencies.

The Accelerating COVID-19 Therapeutic Interventions and Vaccines Therapeutic-Clinical Working Group members gathered critical recommendations in follow-up to lessons learned manuscripts released earlier in the COVID-19 pandemic. Lessons around agent prioritization, preclinical therapeutics testing, master protocol design and implementation, drug manufacturing and supply, data sharing, and public-private partnership value are shared to inform responses to future pandemics.

Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) was an extraordinary example of a public-private partnership (PPP) that brought together over thirty organizations and hundreds of individuals to address one of the most pressing global health needs in recent decades. In particular, ACTIV provided a key avenue for testing numerous therapeutics for their potential benefit in treating the SARS-CoV-2 virus or the resulting symptoms of acute COVID-19 infection. Given the speed and scale at which ACTIV designed and implemented master protocols across global networks that it was simultaneously working to create, the PPP can provide valuable lessons for best practices and avoiding pitfalls the next time the world is faced with a global pandemic of a novel pathogen. This report provides a general overview of the ACTIV partnership to set the stage and context for the subsequent articles in this issue that will relay these lessons learned.

Background: Social determinants of health (SDoH), such as socioeconomics and neighborhoods, strongly influence health outcomes. However, the current state of standardized SDoH data in electronic health records (EHRs) is lacking, a significant barrier to research and care quality.

Methods: We conducted a PubMed search using "SDOH" and "EHR" Medical Subject Headings terms, analyzing included articles across five domains: 1) SDoH screening and assessment approaches, 2) SDoH data collection and documentation, 3) Use of natural language processing (NLP) for extracting SDoH, 4) SDoH data and health outcomes, and 5) SDoH-driven interventions.

Results: Of 685 articles identified, 324 underwent full review. Key findings include implementation of tailored screening instruments, census and claims data linkage for contextual SDoH profiles, NLP systems extracting SDoH from notes, associations between SDoH and healthcare utilization and chronic disease control, and integrated care management programs. However, variability across data sources, tools, and outcomes underscores the need for standardization.

Discussion: Despite progress in identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical for SDoH-EHR integration. Additional database searches could strengthen this scoping review. Ultimately, widespread capture, analysis, and translation of multidimensional SDoH data into clinical care is essential for promoting health equity.

Underrepresentation of people from racial and ethnic minoritized groups in clinical trials threatens external validity of clinical and translational science, diminishes uptake of innovations into practice, and restricts access to the potential benefits of participation. Despite efforts to increase diversity in clinical trials, children and adults from Latino backgrounds remain underrepresented. Quality improvement concepts, strategies, and tools demonstrate promise in enhancing recruitment and enrollment in clinical trials. To demonstrate this promise, we draw upon our team's experience conducting a randomized clinical trial that tests three behavioral interventions designed to promote equity in language and social-emotional skill acquisition among Latino parent-infant dyads from under-resourced communities. The recruitment activities took place during the COVID-19 pandemic, which intensified the need for responsive strategies and procedures. We used the Model for Improvement to achieve our recruitment goals. Across study stages, we engaged strategies such as (1) intentional team formation, (2) participatory approaches to setting goals, monitoring achievement, selecting change strategies, and (3) small iterative tests that informed additional efforts. These strategies helped our team overcome several barriers. These strategies may help other researchers apply quality improvement tools to increase participation in clinical and translational research among people from minoritized groups.

Enrollment into a prospective cohort study of mother-preterm infant dyads during the COVID-19 pandemic progressed slower than anticipated. Enrollment occurred during the first week after preterm birth, while infants were still hospitalized. We hypothesized that slower enrollment was attributable to mothers testing positive for COVID-19 as hospital policies restricted them from entering the neonatal intensive care unit, thus reducing interactions with research staff. However, only 4.5% of 245 screened mothers tested COVID-19 positive. Only 24.9% of those screened, far fewer than anticipated, were eligible for enrollment. Assumptions about pandemic-related enrollment barriers were not substantiated in this pediatric cohort.

Purpose: Community inclusion in research may increase the quality and relevance of research, but doing so in an equitable way is complex. Novel approaches used to build engagement with historically marginalized communities in other sectors may have relevance in the clinical research sector.

Method: To address long-standing gaps and challenges, a stakeholder group was convened to develop a theory of change (ToC), a structured method for obtaining input from stakeholders to enhance the design, conduct, and dissemination of research. The stakeholder group, comprised of Black residents within a metropolitan area, followed a structured monthly meeting schedule for 12 months to produce an outcome map, a model that formally defines aspects of research and engagement for this community.

Results: Stakeholders reported significant improvements in trust in and engagement with research over the 12-month period, but not changes in health empowerment (individual, organizational, or community level). Through this convening process, a ToC and outcome map were developed with the focus of building bidirectional relationships between groups identifying as Black, Indigenous, and People of Color (BIPOC) and researchers in Boston, MA. Additionally, the group developed a community ownership model and guidelines for researchers to adhere to when utilizing the ToC and outcome map with BIPOC communities.

Conclusion: Co-ownership of models to develop bidirectional relationships between researchers and community members, such as the ToC and outcome map, may advance and further the value and reach of community-based participatory research while increasing levels of trust and engagement in research.

Introduction: The expansion of electronic health record (EHR) data networks over the last two decades has significantly improved the accessibility and processes around data sharing. However, there lies a gap in meeting the needs of Clinical and Translational Science Award (CTSA) hubs, particularly related to real-world data (RWD) and real-world evidence (RWE).

Methods: We adopted a mixed-methods approach to construct a comprehensive needs assessment that included: (1) A Landscape Context analysis to understand the competitive environment; and (2) Customer Discovery to identify stakeholders and the value proposition related to EHR data networks. Methods included surveys, interviews, and a focus group.

Results: Thirty-two CTSA institutions contributed data for analysis. Fifty-four interviews and one focus group were conducted. The synthesis of our findings pivots around five emergent themes: (1) CTSA segmentation needs vary according to resources; (2) Team science is key for success; (3) Quality of data generates trust in the network; (4) Capacity building is defined differently by researcher career stage and CTSA existing resources; and (5) Researchers' unmet needs.

Conclusions: Based on the results, EHR data networks like ENACT that would like to meet the expectations of academic research centers within the CTSA consortium need to consider filling the gaps identified by our study: foster team science, improve workforce capacity, achieve data governance trust and efficiency of operation, and aid Learning Health Systems with validating, applying, and scaling the evidence to support quality improvement and high-value care. These findings align with the NIH NCATS Strategic Plan for Data Science.