Chemical structure is a vital consideration early in the drug development process. Its role in analysis of safety and efficacy is relatively diminished after drugs are approved for clinical use. This interdisciplinary study explores a strategy by which readily available clinical data may be used along with structural features of drugs to identify associations with potential utility for both clinical decision-making and drug development.

Chemical functional groups and structural groups (SGs) of 261 drugs were manually classified in tiers, and their incidence of gastrointestinal (GI) and central nervous system (CNS) adverse drug reactions (ADRs) were obtained from a clinical database. Drugs with an GI or CNS ADR incidence of at least 10% were analyzed for correlations with their functional and SGs.

Eight statistically significant associations were detected by preliminary analysis: piperazine and methylene groups were associated with higher rate of CNS ADRs; while amides, secondary alcohols, and di-substituted phenyl groups were associated with lower rates of GI or CNS ADRs or both.

Although further study is necessary to understand these associations and build upon this strategy, this exploratory analysis establishes a methodology by which chemical properties of drugs may be used to aid in clinical decision-making when choosing between otherwise equivalent drug therapy options, as the presence of specific groups on drugs may be associated with increased or decreased risks of specific ADRs.

Access to qualified biostatisticians to provide input on research design and statistical considerations is critical for high-quality clinical and translational research. At diverse health science institutions, like the University of Michigan (U-M), biostatistical collaborators are scattered across the campus. This model can isolate applied statisticians, analysts, and epidemiologists from each other, which may negatively affect their career development and job satisfaction, and inhibits access to optimal biostatistical support for researchers. Furthermore, in the era of modern, complex translational research, it is imperative to elevate biostatistical expertise by offering innovative training.

The Michigan Institute for Clinical and Health Research established an Applied Biostatistical Sciences (ABS) network that is a campus-wide community of staff and faculty statisticians, epidemiologists, data scientists, and researchers, with the intention of supporting both researchers and biostatisticians, while promoting high-quality clinical and translational research.

Since its inception in early 2018, the ABS Network has grown to several hundred faculty and staff members across a range of health and research disciplines. The ABS Network offers free trainings on innovative methods and tools in the biostatistical field, a web-based portal with resources and training lectures, and connections to U-M faculty and/or staff members for consultation and collaboration.

Although challenging, if approached strategically, the creation of a collaboration network of biostatisticians can be accomplished. Furthermore, the process can be adopted and implemented for establishing collaboration with any network of professionals with common interests across different disciplines and professional fields regardless of size.

There is growing concern about the declining physician-scientist workforce. NIH recently provided a national dashboard describing the biomedical research workforce, but local strategies are needed.

We used curated local and national data to develop a workforce dashboard.

Many trends at Oregon Health & Science University (OHSU) were similar to those nationally, such as the increasing percentage of Research Project Grant (RPG)-holding PhDs and the aging RPG population, but differences were also apparent. At OHSU, nearly ¾ of physician-scientist RPGs hold MD-only, compared with nationally, where nearly half are MD/PhD. OHSU also lags in the percentage of RPGs held by women physician-scientists.

Our analysis also permitted us to gain a more complete picture of research funding that has been done nationally. We used these data to develop a dashboard that allows our institution to develop policies to increase the numbers of physician-scientists. The data generation approaches and dashboard are likely to be useful at other institutions, as well.

Research career development awards (CDAs) facilitate development of clinician-scientists. This study compared the academic achievements of individuals in a structured institutional “pre-K” CDA program, the Mayo Clinic Kern Scholars program, with individuals who applied for but were not admitted to the Kern program (“Kern applicants”), and awardees of other unstructured internal CDAs.

This was a longitudinal cohort study of clinicians engaged in research at Mayo Clinic between 2010 and 2019. The primary outcome was time to the 15th new peer-reviewed publication after the program start, adjusted for baseline number of publications. Secondarily, we described successful awarding of federal funding by the NIH or VA.

The median (IQR) number of baseline publications was highest among Kern Scholars compared to Kern Applicants or other CDA awardees [16 (12, 29) vs 5 (1, 11) and 8 (5, 16); P < 0.001]. After adjustment for baseline publications, the time to 15th new publication was significantly shorter for Kern Scholars than for the two comparator groups (P<0.001). Similar findings were observed with total new publications within 5 years (P < 0.001), as well as number of new first-/last-author publications within 5 years (P < 0.001). The overall frequency of K-awards, R-awards (or equivalent), or any funding were similar between groups, with the exception of R03 awards, which were significantly more common among Kern Scholars (P = 0.002).

The Kern Scholars program is a successful training model for clinician-scientists that demonstrated comparatively greater acceleration of scholarly productivity than other internal CDA programs.

Participant recruitment and retention (R&R) are well-documented challenges in longitudinal studies, especially those involving populations historically underrepresented in research and vulnerable groups (e.g., pregnant people or young children and their families), as is the focus of the HEALthy Brain and Child Development (HBCD) birth cohort study. Subpar access to transportation, overnight lodging, childcare, or meals can compromise R&R; yet, guidance on how to overcome these “logistical barriers” is sparse. This study’s goal was to learn about the HBCD sites’ plans and develop best practice recommendations for the HBCD consortium for addressing these logistical barriers.

The HBCD’s workgroups developed a survey asking the HBCD sites about their plans for supporting research-related transportation, lodging, childcare, and meals, and about the presence of institutional policies to guide their approach. Descriptive statistics described the quantitative survey data. Qualitative survey responses were brief, not warranting formal qualitative analysis; their content was summarized.

Twenty-eight respondents, representing unique recruitment locations across the U.S., completed the survey. The results indicated substantial heterogeneity across the respondents in their approach toward supporting research-related transportation, lodging, childcare, and meals. Three respondents were aware of institutional policies guiding research-related transportation (10.7%) or childcare (10.7%).

This study highlighted heterogeneity in approaches and scarcity of institutional policies regarding research-related transportation, lodging, childcare, and meals, underscoring the need for guidance in this area to ensure equitable support of participant R&R across different settings and populations, so that participants are representative of the larger community, and increase research result validity and generalizability.

Ideally, real-world data (RWD) collected to generate real-world evidence (RWE) should lead to impact on the care and health of real-world patients. Deriving from care in which clinicians and patients try various treatments to inform therapeutic decisions, N-of-1 trials bring scientific methods to real-world practice.

These single-patient crossover trials generate RWD and RWE by giving individual patients various treatments in a double-blinded way in sequential periods to determine the most effective treatment for a given patient.

This approach is most often used for patients with chronic, relatively stable conditions that provide the opportunity to make comparisons over multiple treatment periods, termed Type 1 N-of-1 trials. These are most helpful when there is heterogeneity of treatment effects among patients and no a priori best option. N-of-1 trials also can be done for patients with rare diseases, potentially testing only one treatment, to generate evidence for personalized treatment decisions, designated as Type 2 N-of-1 trials. With both types, in addition to informing individual’s treatments, when uniform protocols are used for multiple patients with the same condition, the data collected in the individual N-of-1 trials can be aggregated to provide RWD/RWE to inform more general use of the treatments. Thereby, N-of-1 trials can provide RWE for the care of individuals and for populations.

To fulfill this potential, we believe N-of-1 trials should be built into our current healthcare ecosystem. To this end, we are building the needed infrastructure and engaging the stakeholders who should receive value from this approach.

Recognition of faces of family members, friends, and colleagues is an important skill essential for everyday life. Individuals affected by prosopagnosia (face blindness) have difficulty recognizing familiar individuals. The prevalence of prosopagnosia has been estimated to be as high as 3%. Prosopagnosia can severely impact the quality of life of those affected, and it has been suggested to co-occur with conditions such as depression and anxiety.

To determine real-world diagnostic frequency of prosopagnosia and the spectrum of its comorbidities, we utilized a large database of more than 7.5 million de-identified electronic health records (EHRs) from patients who received care at major academic health centers and Federally Qualified Health Centers in New York City. We designed a computable phenotype to search the database for diagnosed cases of prosopagnosia, revealing a total of n = 902 cases. In addition, data from a randomly sampled matched control population (n = 100,973) were drawn from the database for comparative analyses to study the condition’s comorbidity landscape. Diagnostic frequency of prosopagnosia, epidemiological characteristics, and comorbidity landscape were assessed.

We observed prosopagnosia diagnoses at a rate of 0.012% (12 per 100,000 individuals). We discovered elevated frequency of prosopagnosia diagnosis for individuals who carried certain comorbid conditions, such as personality disorder, depression, epilepsy, and anxiety. Moreover, prosopagnosia diagnoses increased with the number of comorbid conditions.

Results from this study show a wide range of comorbidities and suggest that prosopagnosia is vastly underdiagnosed. Findings imply important clinical consequences for the diagnosis and management of prosopagnosia as well as its comorbid conditions.

Interventions to address social needs in clinical settings can improve child and family health outcomes. Electronic health record (EHR) tools are available to support these interventions but are infrequently used. This mixed-methods study sought to identify approaches for implementing social needs interventions using an existing EHR module in pediatric primary care.

We conducted focus groups and interviews with providers and staff (n = 30) and workflow assessments (n = 48) at four pediatric clinics. Providers and staff completed measures assessing the acceptability, appropriateness, and feasibility of social needs interventions. The Consolidated Framework for Implementation Research guided the study. A hybrid deductive-inductive approach was used to analyze qualitative data.

Median scores (range 1–5) for acceptability (4.9) and appropriateness (5.0) were higher than feasibility (3.9). Perceived barriers to implementation related to duplicative processes, parent disclosure, and staffing limitations. Facilitators included the relative advantage of the EHR module compared to existing documentation practices, importance of addressing social needs, and compatibility with clinic culture and workflow. Self-administered screening was seen as inappropriate for sensitive topics. Strategies identified included providing resource lists, integrating social needs assessments with existing screening questionnaires, and reducing duplicative documentation.

This study offers insight into the implementation of EHR-based social needs interventions and identifies strategies to promote intervention uptake. Findings highlight the need to design interventions that are feasible to implement in real-world settings. Future work should focus on integrating multiple stakeholder perspectives to inform the development of EHR tools and clinical workflows to support social needs interventions.