Classification of Breast Implant Malposition.

BACKGROUND: Implant malposition is a well-recognized complication when using prosthetic implants in the breast for both reconstructive and aesthetic indications. However, to date, no objective classification system has been described. OBJECTIVES: This study presents a prospective trial of an objective and reproducible classification system for implant malposition formulated using retrospective data from a large cohort of patients with implant malposition. METHODS: The authors retrospectively analyzed the degree of medial/lateral and inferior/superior implant malposition relative to their optimal position within the breast footprint in a series of 189 breasts (n = 100 patients). An objective classification system for implant malposition was devised and then applied to a prospective cohort of 53 breasts in 28 patients with implant malposition. RESULTS: The degree of malposition in a single or combination of axes was categorised according to the distance from the ideal breast footprint and measured in centimeters (cms). The classification system incorporated the axis of malposition and distance to generate a treatment decision-making guide. Cases of Grade 1 malposition did not warrant surgical intervention, whilst surgical correction was warranted in all Grade 3 cases.In the combined patient cohort (n = 242 breasts, 128 patients), lateral, inferior, medial and superior displacement ranged between grades 1-3. There was no inter-observer variability in the grades assigned to nine out of ten patients in the prospective group. CONCLUSIONS: We have created a simple and reproducible classification system for implant malposition that allows surgeons to objectively record the extent of malposition, guides surgical decision-making and can be used to document the results of any intervention.

Development and pilot implementation of guidelines for culturally tailored research recruitment materials for African Americans and Latinos.

BACKGROUND: Previous studies support cultural tailoring of recruitment materials as a strategy to promote the enrollment of minoritized groups in clinical trials. However, there is a lack of guidance for research teams to create culturally tailored materials, potentially contributing to low recruitment rates of minoritized groups. We describe the development and pilot testing of recruitment material guidelines used to culturally tailor clinical trial recruitment materials targeting African Americans and Latinos. METHODS: The guideline development team consisted of investigators, research staff, and community leaders and members experienced in the recruitment and community engagement of minoritized groups. The recruitment material guidelines were developed using the literature, focus groups with African Americans and Latinos, the teams' research experience, and guidance from a community advisory board. To assess the effectiveness of the guidelines, a pilot study was conducted comparing advertisement click-through rates and enrollment outcomes between two institutions differing in use of culturally tailored versus non-tailored Facebook banner ads for the "Aspirin Dosing: A Patient-centric Trial Assessing Benefits and Long-Term Effectiveness" (ADAPTABLE) study. RESULTS: Five themes emerged from focus groups: (1) employ diversity and inclusion in recruitment efforts; (2) access multiple recruitment channels to increase reach and possible participation; (3) increase your "footwork"; (4) personalize outreach and recruitment to specific groups' beliefs and values; (5) align recruitment messaging with language preferences and motivations for study participation; and (6) specify incentives for participation. Guidelines were: 1) be inclusive; 2) use all forms of media; 3) take a personalized approach; 4) align recruitment messaging with motivations for study participation; 5) specify incentives; and 6) get out into the community. Additional guidelines were developed addressing specific considerations for images and language when targeting African American and Latino populations. Pilot study results demonstrated that clicks per impression ratio (0.47 clicks per impression vs. 0.03 clicks per impression) and the percentage of African American enrollment were significantly higher when using tailored compared to non-tailored ads (12.8% vs. 8.3%, respectively). CONCLUSION: The recruitment material guidelines offer practical recommendations to reach diverse populations for clinical trial participation more effectively. Our preliminary data supports use of these guidelines as a strategy to enhance recruitment of minoritized groups into clinical research studies.

Design and implementation of a massive open online course on enhancing the recruitment of minorities in clinical trials - Faster Together.

BACKGROUND: Racial and ethnic minorities are often underrepresented in clinical trials, threatening the generalizability of trial results. Several factors may contribute to underrepresentation of minorities in clinical trials, including lack of training for researchers and staff on the importance of diversity in clinical trials and effective strategies for recruiting and retaining minority populations. METHODS: Applying community engaged research principles, we developed a massive open online course (MOOC) to help research team members develop knowledge and skills to enhance the recruitment of minorities in clinical trials. A transdisciplinary working group, consisting of clinical researchers, community engagement specialists, minority clinical trial recruitment and retention educators and specialists, and knowledge management information scientists, was formed to develop an evidence-based curriculum. Feedback from the Recruitment Innovation Center Community Advisory Board was incorporated to help finalize the curriculum. The course was implemented in Coursera, an online learning platform offering MOOCs. A bootstrap paired sample t-test was used to compare pre- and post-assessments of knowledge, attitudes, and intentions as it relates to minority recruitment. RESULTS: The final course, entitled Faster Together, was divided into eight 1-h modules. Each module included video presentations, reading assignments, and quizzes. After 10 months, 382 individuals enrolled in the course, 105 participants completed the pre-test, and 14 participants completed the post-test. Participants' knowledge scores were higher with an increase in the mean number of correct answers from 15.4 (95% CI:12.1-18.7) on the pre-test to 18.7 (95% CI:17.42-20.2) on the post-test. All post-test respondents (n = 14) indicated that the course improved their professional knowledge, and 71.4% of respondents indicated that they were very likely to make changes to their recruitment practices. CONCLUSIONS: Faster Together, a massive open online course, is an acceptable, accessible approach to educating research teams on minority recruitment in clinical trials. Preliminary evidence indicates the course increased knowledge on how to recruit minorities into clinical trials and could promote change in their recruitment practices.

Creating and implementing a COVID-19 recruitment Data Mart.

The COVID-19 pandemic has resulted in an unprecedented strain on every aspect of the healthcare system, and clinical research is no exception. Researchers are working against the clock to ramp up research studies addressing every angle of COVID-19 - gaining a better understanding of person-to-person transmission, improving methods for diagnosis, and developing therapies to treat infection and vaccines to prevent it. The impact of the virus on research efforts is not limited to investigators and their teams. Potential participants also face unparalleled opportunities and requests to participate in research, which can result in a significant amount of participant fatigue. The Vanderbilt Institute for Clinical and Translational Research recognized early in the pandemic that a solution to assist researchers in the rapid identification of potential participants was critical, and thus developed the COVID-19 Recruitment Data Mart. This solution does not rest solely on technology; the addition of experienced project managers to support researchers and facilitate collaboration was essential. Since the platform and study support tools were launched on July 20, 2020, four studies have been onboarded and a total of 1693 potential participant matches have been shared. Each of these patients had agreed in advance to direct contact for COVID-19 research and had been matched to study-specific inclusion/exclusion criteria. Our innovative Data Mart system is scalable and looks promising as a generalizable solution for simultaneously recommending individuals from a pool of patients against a pool of time-sensitive trial opportunities.

The REDCap consortium: Building an international community of software platform partners.

The Research Electronic Data Capture (REDCap) data management platform was developed in 2004 to address an institutional need at Vanderbilt University, then shared with a limited number of adopting sites beginning in 2006. Given bi-directional benefit in early sharing experiments, we created a broader consortium sharing and support model for any academic, non-profit, or government partner wishing to adopt the software. Our sharing framework and consortium-based support model have evolved over time along with the size of the consortium (currently more than 3200 REDCap partners across 128 countries). While the "REDCap Consortium" model represents only one example of how to build and disseminate a software platform, lessons learned from our approach may assist other research institutions seeking to build and disseminate innovative technologies.

Monitoring Anesthesia Care Delivery and Perioperative Mortality in Kenya Utilizing a Provider-driven Novel Data Collection Tool.

BACKGROUND: Perioperative mortality rate is regarded as a credible quality and safety indicator of perioperative care, but its documentation in low- and middle-income countries is poor. We developed and tested an electronic, provider report-driven method in an East African country. METHODS: We deployed a data collection tool in a Kenyan tertiary referral hospital that collects case-specific perioperative data, with asynchronous automatic transmission to central servers. Cases not captured by the tool (nonobserved) were collected manually for the last two quarters of the data collection period. We created logistic regression models to analyze the impact of procedure type on mortality. RESULTS: Between January 2014 and September 2015, 8,419 cases out of 11,875 were captured. Quarterly data capture rates ranged from 423 (26%) to 1,663 (93%) in the last quarter. There were 93 deaths (1.53%) reported at 7 days. Compared with four deaths (0.53%) in cesarean delivery, general surgery (n = 42 [3.65%]; odds ratio = 15.80 [95% CI, 5.20 to 48.10]; P < 0.001), neurosurgery (n = 19 [2.41%]; odds ratio = 14.08 [95% CI, 4.12 to 48.10]; P < 0.001), and emergency surgery (n = 25 [3.63%]; odds ratio = 4.40 [95% CI, 2.46 to 7.86]; P < 0.001) carried higher risks of mortality. The nonobserved group did not differ from electronically captured cases in 7-day mortality (n = 1 [0.23%] vs. n = 16 [0.58%]; odds ratio =3.95 [95% CI, 0.41 to 38.20]; P = 0.24). CONCLUSIONS: We created a simple solution for high-volume, prospective electronic collection of perioperative data in a lower- to middle-income setting. We successfully used the tool to collect a large repository of cases from a single center in Kenya and observed mortality rate differences between surgery types.

Secondary use of clinical data: the Vanderbilt approach.

The last decade has seen an exponential growth in the quantity of clinical data collected nationwide, triggering an increase in opportunities to reuse the data for biomedical research. The Vanderbilt research data warehouse framework consists of identified and de-identified clinical data repositories, fee-for-service custom services, and tools built atop the data layer to assist researchers across the enterprise. Providing resources dedicated to research initiatives benefits not only the research community, but also clinicians, patients and institutional leadership. This work provides a summary of our approach in the secondary use of clinical data for research domain, including a description of key components and a list of lessons learned, designed to assist others assembling similar services and infrastructure.

Illuminating the landscape of high-level clinical trial opportunities in the All of Us Research Program.

OBJECTIVE: With its size and diversity, the All of Us Research Program has the potential to power and improve representation in clinical trials through ancillary studies like Nutrition for Precision Health. We sought to characterize high-level trial opportunities for the diverse participants and sponsors of future trial investment. MATERIALS AND METHODS: We matched All of Us participants with available trials on ClinicalTrials.gov based on medical conditions, age, sex, and geographic location. Based on the number of matched trials, we (1) developed the Trial Opportunities Compass (TOC) to help sponsors assess trial investment portfolios, (2) characterized the landscape of trial opportunities in a phenome-wide association study (PheWAS), and (3) assessed the relationship between trial opportunities and social determinants of health (SDoH) to identify potential barriers to trial participation. RESULTS: Our study included 181 529 All of Us participants and 18 634 trials. The TOC identified opportunities for portfolio investment and gaps in currently available trials across federal, industrial, and academic sponsors. PheWAS results revealed an emphasis on mental disorder-related trials, with anxiety disorder having the highest adjusted increase in the number of matched trials (59% [95% CI, 57-62]; P < 1e-300). Participants from certain communities underrepresented in biomedical research, including self-reported racial and ethnic minorities, had more matched trials after adjusting for other factors. Living in a nonmetropolitan area was associated with up to 13.1 times fewer matched trials. DISCUSSION AND CONCLUSION: All of Us data are a valuable resource for identifying trial opportunities to inform trial portfolio planning. Characterizing these opportunities with consideration for SDoH can provide guidance on prioritizing the most pressing barriers to trial participation.

Physical Activity and Incident Obesity Across the Spectrum of Genetic Risk for Obesity.

Importance: Despite consistent public health recommendations, obesity rates in the US continue to increase. Physical activity recommendations do not account for individual genetic variability, increasing risk of obesity. Objective: To use activity, clinical, and genetic data from the All of Us Research Program (AoURP) to explore the association of genetic risk of higher body mass index (BMI) with the level of physical activity needed to reduce incident obesity. Design, Setting, and Participants: In this US population-based retrospective cohort study, participants were enrolled in the AoURP between May 1, 2018, and July 1, 2022. Enrollees in the AoURP who were of European ancestry, owned a personal activity tracking device, and did not have obesity up to 6 months into activity tracking were included in the analysis. Exposure: Physical activity expressed as daily step counts and a polygenic risk score (PRS) for BMI, calculated as weight in kilograms divided by height in meters squared. Main Outcome and Measures: Incident obesity (BMI ≥30). Results: A total of 3124 participants met inclusion criteria. Among 3051 participants with available data, 2216 (73%) were women, and the median age was 52.7 (IQR, 36.4-62.8) years. The total cohort of 3124 participants walked a median of 8326 (IQR, 6499-10 389) steps/d over a median of 5.4 (IQR, 3.4-7.0) years of personal activity tracking. The incidence of obesity over the study period increased from 13% (101 of 781) to 43% (335 of 781) in the lowest and highest PRS quartiles, respectively (P = 1.0 × 10-20). The BMI PRS demonstrated an 81% increase in obesity risk (P = 3.57 × 10-20) while mean step count demonstrated a 43% reduction (P = 5.30 × 10-12) when comparing the 75th and 25th percentiles, respectively. Individuals with a PRS in the 75th percentile would need to walk a mean of 2280 (95% CI, 1680-3310) more steps per day (11 020 total) than those at the 50th percentile to have a comparable risk of obesity. To have a comparable risk of obesity to individuals at the 25th percentile of PRS, those at the 75th percentile with a baseline BMI of 22 would need to walk an additional 3460 steps/d; with a baseline BMI of 24, an additional 4430 steps/d; with a baseline BMI of 26, an additional 5380 steps/d; and with a baseline BMI of 28, an additional 6350 steps/d. Conclusions and Relevance: In this cohort study, the association between daily step count and obesity risk across genetic background and baseline BMI were quantified. Population-based recommendations may underestimate physical activity needed to prevent obesity among those at high genetic risk.

Impact of financial compensation on enrollment and participation in a remote, mobile-app based research study.

Background: There is no consensus on how to determine appropriate financial compensation for research recruitment. Selecting incentive amounts that are reasonable and respectful, without undue inducement, remains challenging. Previously, we demonstrated that incentive amount significantly impacts participants' willingness to complete various hypothetical research activities. Here we further explore this relationship in a mock decentralized study. Methods: Adult ResearchMatch volunteers were invited to join a prospective study where interested individuals were given an opportunity to view details for a study along with participation requirements, then offered a randomly generated compensation amount between $0 and $50 to enroll and participate. Individuals agreeing to participate were then asked to complete tasks using a remote mobile application (MyCap), for two weeks. Tasks included a weekly survey, a daily gratitude journal and daily phone tapping task. Results: Willingness to participate was 85% across all incentive levels but not significantly impacted by amount. Task completion appeared to increase as a function of compensation until a plateau at $25. While participants described the study as low burden and reported that compensation was moderately important to their decision to join, only 31% completed all study tasks. Conclusion: While offering compensation in this study did not have a strong effect on enrollment rate, this work provides insight into participant motivation when joining and participating in studies employing mobile applications.

Selecting trial centers using a standardized, automated site assessment survey instrument (SASI).

BACKGROUND: To improve the site selection process for clinical trials, we expanded a site survey to include standardized assessments of site commitment time, team experience, feasibility of tight timelines, and local medical community equipoise as factors that might better predict performance. We also collected contact information about institutional research services ahead of site onboarding. AIM: As a first step, we wanted to confirm that an expanded survey could be feasible and generalizable-that asking site teams for more details upfront was acceptable and that the survey could be completed in a reasonable amount of time, despite the assessment length. METHODS: A standardized, two-part Site Assessment Survey Instrument (SASI), examining qualitative components and with multiple contact list sections, was developed using a publicly accessible dashboard and later transferred to a REDCap platform. After multiple rounds of internal testing, the SASI was deployed 11 times for multicenter trials. Follow-up questionnaires were sent to site teams to confirm that an expanded survey instrument is acceptable to the research community and could be completed during a brief work shift. RESULTS: Respondents thought the SASI collected useful and relevant information about their sites (100%). Sites were "comfortable" (90%) supplying detailed information early in the site selection process and 57% completed the SASI in one to two hours. CONCLUSIONS: Coordinating centers and sites found the SASI tool to be acceptable and helpful when collecting data in consideration of multicenter trial site selection.

Procurement of shared data instruments for Research Electronic Data Capture (REDCap).

REDCap (Research Electronic Data Capture) is a web-based software solution and tool set that allows biomedical researchers to create secure online forms for data capture, management and analysis with minimal effort and training. The Shared Data Instrument Library (SDIL) is a relatively new component of REDCap that allows sharing of commonly used data collection instruments for immediate study use by research teams. Objectives of the SDIL project include: (1) facilitating reuse of data dictionaries and reducing duplication of effort; (2) promoting the use of validated data collection instruments, data standards and best practices; and (3) promoting research collaboration and data sharing. Instruments submitted to the library are reviewed by a library oversight committee, with rotating membership from multiple institutions, which ensures quality, relevance and legality of shared instruments. The design allows researchers to download the instruments in a consumable electronic format in the REDCap environment. At the time of this writing, the SDIL contains over 128 data collection instruments. Over 2500 instances of instruments have been downloaded by researchers at multiple institutions. In this paper we describe the library platform, provide detail about experience gained during the first 25months of sharing public domain instruments and provide evidence of impact for the SDIL across the REDCap consortium research community. We postulate that the shared library of instruments reduces the burden of adhering to sound data collection principles while promoting best practices.

Targeted gene delivery by free-tissue transfer in oncoplastic reconstruction.

Surgery is the most effective curative treatment for various tumour types. Despite a current preference for conservative surgery, radical excision retains a clearly defined role in modern management of locoregional disease. Extirpative defects are reconstructed routinely using free-tissue transfer from a distant donor site. Although these free flaps currently provide no direct therapeutic benefit, advances in gene-delivery techniques offer the possibility to genetically modify flaps to produce potent targeted treatments with greater anatomical control. Several promising therapeutic strategies, including virus-directed enzyme prodrug therapy, genetic radionuclide therapy, and free-flap radioprotection, have the potential to extend the role of the free flap beyond its immediate goal of restoring form and function to patients, but challenges exist. Work to translate therapeutic free-tissue transfer from preclinical study to clinical use is in progress.

Using a multicultural and multilingual awareness-raising strategy to enhance enrollment of racially underrepresented minoritized communities - the PassITON trial.

Racially and ethnically minoritized populations have been historically excluded and underrepresented in research. This paper will describe best practices in multicultural and multilingual awareness-raising strategies used by the Recruitment Innovation Center to increase minoritized enrollment into clinical trials. The Passive Immunity Trial for Our Nation will be used as a primary example to highlight real-world application of these methods to raise awareness, engage community partners, and recruit diverse study participants.

Association of Longitudinal Activity Measures and Diabetes Risk: An Analysis From the National Institutes of Health All of Us Research Program.

CONTEXT: Prior studies of the relationship between physical activity and incident type 2 diabetes mellitus (T2DM) relied primarily on questionnaires at a single time point. OBJECTIVE: We sought to investigate the relationship between physical activity and incident T2DM with an innovative approach using data from commercial wearable devices linked to electronic health records in a real-world population. METHODS: Using All of Us participants' accelerometer data from their personal Fitbit devices, we used a time-varying Cox proportional hazards models with repeated measures of physical activity for the outcome of incident T2DM. We evaluated for effect modification with age, sex, body mass index (BMI), and sedentary time using multiplicative interaction terms. RESULTS: From 5677 participants in the All of Us Research Program (median age 51 years; 74% female; 89% White), there were 97 (2%) cases of incident T2DM over a median follow-up period of 3.8 years between 2010 to 2021. In models adjusted for age, sex, and race, the hazard of incident diabetes was reduced by 44% (95% CI, 15%-63%; P = 0.01) when comparing those with an average daily step count of 10 700 to those with 6000. Similar benefits were seen comparing groups based on average duration of various intensities of activity (eg, lightly active, fairly active, very active). There was no evidence for effect modification by age, sex, BMI, or sedentary time. CONCLUSION: Greater time in any type of physical activity intensity was associated with lower risk of T2DM irrespective of age, sex, BMI, or sedentary time.

A community-informed recruitment plan template to increase recruitment of racial and ethnic groups historically excluded and underrepresented in clinical research.

INTRODUCTION: Engaging communities in research planning and implementation can enhance recruitment and retention (R&R) of racial and ethnic groups historically excluded and underrepresented in clinical research; however, most studies do not use community-informed approaches. This paper describes the formative research process used to design a Community-Informed Recruitment Plan Template for racial and ethnic groups historically excluded and underrepresented in clinical research. METHODS: Using an existing R&R template as a starting point, we iteratively developed and refined the community-informed template through a 3-phase process to achieve cultural-appropriateness. Phase 1 included a literature review, 34 community engagement (CE) studios to review recommendations, community advisory board (CAB) review, and survey data from minority recruitment experts. Phase 2 involved integration of content into existing R&R template. Phase 3 was a final review and revision using input of the CAB and researchers' panel. Survey data collected in Phase 1 were analyzed using descriptives (i.e., frequencies and percentages). Open-ended survey responses were analyzed using inductive, qualitative thematic analysis. RESULTS: The final 8-section template can help develop effective grant or proposal language where study R&R plans are requested. They include: 1) Recruitment Strategy; 2) A Stakeholder Communication Plan; 3) Evidence of Recruitment Feasibility; 4) Recruitment and Retention Team; 5) Recruitment and Retention Methods; 6) Recruitment and Retention Timeline; 7) Evaluation; and 8) Budget. CONCLUSIONS: Incorporating multiple perspectives into this formative research process enhances the cultural appropriateness of this community-informed R&R template to help research teams achieve R&R goals for individuals historically excluded and underrepresented in clinical research.

The RIC Recruitment & Retention Materials Toolkit - a resource for developing community-informed study materials.

Clinical trials face many challenges with meeting projected enrollment and retention goals. A study's recruitment materials and messaging convey necessary key information and therefore serve as a critical first impression with potential participants. Yet study teams often lack the resources and skills needed to develop engaging, culturally tailored, and professional-looking recruitment materials. To address this gap, the Recruitment Innovation Center recently developed a Recruitment & Retention Materials Content and Design Toolkit, which offers research teams guidance, actionable tips, resources, and customizable templates for creating trial-specific study materials. This paper seeks to describe the creation and contents of this new toolkit.

De-black-boxing health AI: demonstrating reproducible machine learning computable phenotypes using the N3C-RECOVER Long COVID model in the All of Us data repository.

Machine learning (ML)-driven computable phenotypes are among the most challenging to share and reproduce. Despite this difficulty, the urgent public health considerations around Long COVID make it especially important to ensure the rigor and reproducibility of Long COVID phenotyping algorithms such that they can be made available to a broad audience of researchers. As part of the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative, researchers with the National COVID Cohort Collaborative (N3C) devised and trained an ML-based phenotype to identify patients highly probable to have Long COVID. Supported by RECOVER, N3C and NIH's All of Us study partnered to reproduce the output of N3C's trained model in the All of Us data enclave, demonstrating model extensibility in multiple environments. This case study in ML-based phenotype reuse illustrates how open-source software best practices and cross-site collaboration can de-black-box phenotyping algorithms, prevent unnecessary rework, and promote open science in informatics.

ResearchMatch on FHIR: Development and evaluation of a recruitment registry and electronic health record system interface for volunteer profile completion.

Background: Obtaining complete and accurate information in recruitment registries is essential for matching potential participants to research studies for which they qualify. Since electronic health record (EHR) systems are required to make patient data available to external systems, an interface between EHRs and recruitment registries may improve accuracy and completeness of volunteers' profiles. We tested this hypothesis on ResearchMatch (RM), a disease- and institution-neutral recruitment registry with 1357 studies across 255 institutions. Methods: We developed an interface where volunteers signing up for RM can authorize transfer of demographic data, medical conditions, and medications from the EHR into a registration form. We obtained feedback from a panel of community members to determine acceptability of the planned integration. We then developed the EHR interface and performed an evaluation study of 100 patients to determine whether RM profiles generated with EHR-assisted adjudication included more conditions and medications than those without the EHR connection. Results: Community member feedback revealed that members of the public were willing to authenticate into the EHR from RM with proper messaging about choice and privacy. The evaluation study showed that out of 100 participants, 75 included more conditions and 69 included more medications in RM profiles completed with the EHR connection than those without. Participants also completed the EHR-connected profiles in 16 fewer seconds than non-EHR-connected profiles. Conclusions: The EHR to RM integration could lead to more complete profiles, less participant burden, and better study matches for many of the over 148,000 volunteers who participate in ResearchMatch.