OBJECTIVES/GOALS: Lack of a vehicle and the distant location of supermarkets influence the ease with which people can shop for healthy foods. The aim of this qualitative study is to understand how transportation impacts food access and food purchases of Baltimore residents who do not own an automobile. METHODS/STUDY POPULATION: The Lyft Grocery Access Program was piloted in Baltimore, MD from November 2019 through September 2020. Eligible households resided in Healthy Food Priority Areas, formerly known as food deserts, in south and west Baltimore and also did not own a vehicle. Enrolled households were offered discounted Lyft rides to select supermarkets. Participants for the present study will be purposively recruited via email and phone using contact information that was provided by enrolled households during the pilot program. Each in-depth interview will be conducted via Zoom and recorded, transcribed and analyzed for themes by two trained coders. Data collection and analysis will occur simultaneously. Data collection will cease once data saturation is reached and themes will be derived from the data. RESULTS/ANTICIPATED RESULTS: This study is in progress. Anticipated themes may relate to the food environment, transportation and food access. DISCUSSION/SIGNIFICANCE: Access to healthy foods is an important determinant of health, and how food access is impacted by broader aspects of daily living such as transportation will add to the food access literature. Findings may provide new insights that can help inform food policy and transportation planning in urban communities.

OBJECTIVES/GOALS: We developed institution-wide RCR training to include all required elements; support trainees to identify key ethical questions that arise in research; and to identify methods to identify a solution; and disseminate results. METHODS/STUDY POPULATION: In 2011, we participated in developing an experimental model of RCR training led by the University of Michigan. We continue to offer this training model to career development awardees in clinical and translational research at OHSU across the institution. Interactive discussion in faculty and trainee groups includes responsibilities of a researcher and a systematic process to address real world research ethics issues. Each participant identifies a key research issue they have encountered and presents a poster at the final session. We have tracked post-training assessment of participant confidence in ethical decision making and in the range of topics identified by participants. RESULTS/ANTICIPATED RESULTS: Since 2012, 227 scholars and trainees have participated in the program with 44 faculty mentors facilitating. We will describe the current curriculum as it has evolved over the past 12 years, presenting trainees with an approach to identify ethical challenges that arise in their research and identify approaches to find a practical solution. We will report on the specific challenges in research ethics identified by participants over this period and how they have evolved. We will also present pre- and post-training data about confidence in ethical decision making. DISCUSSION/SIGNIFICANCE: This approach to RCR training is well-received, has evolved over time, and has led to dissemination. Success is attributed to allowing choice in topics relevant to trainees and practicality of the stepwise approach that is transferable to any situation.

OBJECTIVES/SPECIFIC AIMS: Emergency department (ED) length of stay (LOS) is one of the important indicators of quality and efficiency of ED service delivery and is reported to be both cause and result of ED crowding. Increased ED LOS is associated with ED crowding, increases service cost and sometimes poor patient outcome. Substance abuse is one of the major determinants of morbidity, mortality and healthcare needs. Substance abuse may confound the healthcare and service needs of patients in the ED irrespective of primary purpose of their ED visit and may lengthen the ED LOS. The aim of this study was to evaluate the effect of patients’ demographic and clinical characteristics and of different patient-related activities such as screening brief intervention and referral to treatment (SBIRT) on the ED LOS of patients discharged from the ED with a diagnosis of substance abuse. METHODS/STUDY POPULATION: We conducted a retrospective data analysis of electronic health records. The study population included 26971 patients who visited our hospital ED between 2013 and 2017, had a history of substance abuse and were discharged from the ED. An accelerated failure time (AFT) model was used to analyze the influence of covariates on patient ED LOS. The predictor factors in the model included age, gender, ED arrival shift and weekday, diagnosis history of mental health and drug use, acuity triage level from 1 to 5, with 1 being worse severity, and whether any lab tests were ordered, SBIRT intervention and whether patient was homeless. The AFT model is an alternative to the Cox Proportional Hazard Ratio model, which directly models the log of ED LOS as a function of a vector of covariates. The model defines the increase or decrease in LOS with the changes in the covariate levels as an acceleration factor or time ratio (TR). RESULTS/ANTICIPATED RESULTS: The overall median ED LOS was 4 hours with IQR of 4.2 hours. The average age of the study population was 39.3 years, 58.6% of the patients were male and 57% where White; 63.4% had a history of drug use; 43% had a history of mental health issue, and 0.4% were homeless. In the analysis using the AFT model, increased age (a year increase, TR =1.01, p =0.008), female sex (TR=1.044, P<0.001), SBIRT (TR=1.525, P <0.001), history of mental health issue (TR=1.117, P<0.00), evening arrival (evening vs night, TR=1.04, p=0.006), history of drug use (drug vs alcohol only, TR=1.04, p=0.001), higher acuity (triage level 1 vs 5, TR=2.795, p <0.001) and homelessness (TR=1.073, P = 0.021) lengthened the ED LOS. In contrast, weekend arrival (TR=0.956, p=0.004) and day shift arrival (day vs night, TR=0.958, p=0.004) shortened the ED LOS. DISCUSSION/SIGNIFICANCE OF IMPACT: We identified gender, age, SBIRT, arrival shift, weekend arrival, mental health status, substance abuse, acuity level and homelessness to be significant predictor of ED LOS. The fact that SBIRT increased the LOS should be balanced with the advantages of engaging patients into substance use disorder treatment. Understanding the determinants of ED LOS in this population may provide useful information for physicians or patients to better anticipate an individual’s LOS and to help administrators plan the ED staffing and other resources mobilization.

OBJECTIVES/SPECIFIC AIMS: Our study seeks to answer the following questions: (1) To determine whether higher numbers of gravidity and parity are associated with a decreased risk of mild cognitive impairment or dementia; (2) To determine whether higher numbers of gravidity and parity are associated with a decreased risk of amyloid deposition by PET MRI. METHODS/STUDY POPULATION: Our study population includes all female study participants in the Atherosclerosis Risk in Communities (ARIC) study who did not have a diagnosis of dementia before enrollment. Participants were also required to have been evaluated for cognitive impairment in the ARIC-NCS ancillary study, or to have received an MRI PET scan of their brain as part of the ARIC-PET ancillary study. Baseline information on the gravidity and parity of all the women was recorded at initial enrollment. We use statistical analyses and epidemiological measures to explore our study questions. For our first question, we use logistic regression to evaluate the association of gravidity and parity as two separate ordinal variables using adjudicated mild cognitive impairment (MCI) and dementia. For our second question, we use logistic regression to evaluate the association of gravidity and parity (again as ordinal variables) with amyloid positivity. We use STATA for our statistical analyses. RESULTS/ANTICIPATED RESULTS: We hypothesize that increased gravidity and parity will have either no effect or a protective effect against MCI, dementia, and amyloid deposition. Our preliminary analyses show that older age of a woman at first pregnancy and at first live birth are both positively correlated with increased incidence of cognitive impairment. No relationship was found between these surrogates of lifetime estrogen exposure and cerebral amyloid deposition. DISCUSSION/SIGNIFICANCE OF IMPACT: Multiple basic science and clinical research studies have shown that estrogen exposure has an effect on cognitive function, likely through a complex interplay of multiple physiologic systems. Our study expands the research in this area by using a large, established epidemiologic cohort to examine gravidity and parity as important factors in lifetime estrogen exposure as they relate to cognitive impairment and amyloid plaque deposition.

ABSTRACT IMPACT: A machine learning approach using electronic health records can combine descriptive, population-level factors of pressure injury outcomes. OBJECTIVES/GOALS: Pressure injuries cause 60,000 deaths and cost $26 billion annually in the US, but prevention is laborious. We used clinical data to develop a machine learning algorithm for predicting pressure injury risk and prescribe the timing of intervention to help clinicians balance competing priorities. METHODS/STUDY POPULATION: We obtained 94,745 electronic health records with 7,000 predictors to calibrate a predictive algorithm of pressure injury risk. Machine learning was used to mine features predicting changes in pressure injury risk; random forests outperformed neural networks, boosting and bagging in feature selection. These features were fit to multilevel ordered logistic regression to create an algorithm that generated empirical Bayes estimates informing a decision-rule for follow-up based on individual risk trajectories over time. We used cross-validation to verify predictive validity, and constrained optimization to select a best-fit algorithm that reduced the time required to trigger patient follow-up. RESULTS/ANTICIPATED RESULTS: The algorithm significantly improved prediction of pressure injury risk (p<0.001) with an area under the ROC curve of 0.60 compared to the Braden Scale, a traditional clinician instrument of pressure injury risk. At a specificity of 0.50, the model achieved a sensitivity of 0.63 within 2.5 patient-days. Machine learning identified categorical increases in risk when patients were prescribed vasopressors (OR=16.4, p<0.001), beta-blockers (OR=4.8, p<0.001), erythropoietin stimulating agents (OR=3.0, p<0.001), or were ordered a urinalysis screen (OR=9.1, p<0.001), lipid panel (OR=5.7, p<0.001) or pre-albumin panel (OR=2.0, p<0.001). DISCUSSION/SIGNIFICANCE OF FINDINGS: This algorithm could help hospitals conserve resources within a critical period of patient vulnerability for pressure injury not reimbursed by Medicare. Savings generated by this approach could justify investment in machine learning to develop electronic warning systems for many iatrogenic injuries.

OBJECTIVES/SPECIFIC AIMS: Abnormal eating behavior is a core and distinguishing diagnostic feature of behavioral variant frontotemporal dementia (bvFTD) that differentiates it from other neurodegenerative disorders and late-life psychiatric conditions. Though it has been proposed that hyperphagia in bvFTD results from cognitive dysfunction, the observation of altered sweet preferences and food foraging indicate that bvFTD is accompanied by fundamental dietary changes associated with hypothalamic and insular atrophy. In the current study, we examined how cognitive dysfunction contributes to abnormal feeding behavior in bvFTD. METHODS/STUDY POPULATION: We analyzed first-visit eating and neuropsychological data from the National Alzheimer’s Coordinating Center database (7 centers; September 2017 data freeze) in a subset of bvFTD patients with clinician-rated characterization of disturbed feeding severity. Group differences in cognitive domains of attention, processing speed, language, memory, and executive functioning were examined between patients with abnormal eating behavior (n=59) and a demographically-matched sample of patients with normal feeding behavior (n=60). Group differences in informant-reported empathy, behavioral inhibition, and depressive symptoms were also examined. RESULTS/ANTICIPATED RESULTS: Cognitive profiles in bvFTD patients did not vary as a function of disturbed feeding behavior. In a subset of cases pathologically-confirmed at autopsy, processing speed was better in cases with abnormal feeding behavior. No significant group differences were found for behavioral indices. DISCUSSION/SIGNIFICANCE OF IMPACT: These findings suggest that cognitive dysfunction is not the sole driver of abnormal eating behavior in bvFTD. Future studies with comprehensive characterization of feeding behavior, cognition and physiological/neuroimaging indices are warranted.

Objectives/Goals: Trainees in clinical and translational science (CTS) take courses in biostatistics, epidemiology, and other quantitative areas. To be most successful, trainees require competency in algebra. We developed a quantitative assessment and study guide to assess trainee’s quantitative skills and provide review material to address weaknesses. Methods/Study Population: The Tufts CTS Graduate Program is the training core of the Tufts CTSI and its associated pre- and post-doctoral T32 awards. Approximately 10 trainees with a range of backgrounds (e.g., physicians, medical students, master’s-level researchers, and basic science PhDs) and varying math education experiences matriculate each year. We wanted to address the resulting range of quantitative skills to help students succeed in our program. In Spring 2023, we met with faculty teaching quantitative courses to identify core algebra concepts needed to succeed in their classes. A graduate student in computational mathematics with extensive tutoring experience then drafted assessment questions, a comprehensive study guide, and brief cheat sheet. The material was reviewed and revised with input from quantitative faculty. Results/Anticipated Results: We developed a 20-item quantitative assessment covering properties of operators; identity elements and inverses; simplification of arithmetic and algebraic expressions; solving algebraic equations; functions; equations of a line; and exponents/logarithms. A cheat sheet provided trainees with a brief refresher for these topics. A study guide provided more detailed instruction, example exercises and solutions, and referenced publicly available, online resources (e.g., Khan Academy). During the introductory summer course for the Tufts CTS Program, trainees were allowed to use the cheat sheet and were given 1 hour to complete the assessment. Trainees who got questions incorrect were directed to relevant sections in the study guide. We anticipate collecting formal feedback to evaluate the material. Discussion/Significance of Impact: Trainees must have adequate foundational algebra skills to succeed in CTS graduate programs and as future researchers. Developing a quantitative assessment allowed us to identify areas of weakness resulting from educational disparities or reflecting other aspects of their backgrounds and to provide material to reinforce their preparation.

OBJECTIVES/GOALS: During earlier periods of the pandemic, Black and Latinx populations in Michigan have suffered higher rates of infection, hospitalization, and deaths when compared to Whites. We conducted this study to understand how Black and Latinx residents perceived this disproportionate burden. METHODS/STUDY POPULATION: In 2021, 40 semi-structured interviews were conducted virtually in English or Spanish with Black (n=24) and Latinx (n=16) residents in Michigan areas highly impacted by COVID-19: Genesee, Kent, Washtenaw, and Wayne counties. Using a Community-Based Participatory Research (CBPR) approach, we partnered with leaders from 15 community-based organizations and health and human service agencies to develop research questions, an interview protocol, and to interpret the data. We used the data analysis software Dedoose (ver 4.12) for inductive coding (IRR=0.81). This study is a part of the NIH Community Engagement Alliance (CEAL) Against COVID-19 initiative. RESULTS/ANTICIPATED RESULTS: Participants described the significant impact of the pandemic in terms of physical and mental health, job security, and the sheer number of deaths among loved ones. They attributed the impact to comorbidities and social determinants of health disparities exacerbated by the pandemic, including income, housing, access to healthcare, as well as systemic racism. They noted being overrepresented among frontline workers with higher exposure to COVID-19, limited or misinformation about the virus, language barriers, and difficulty with social distancing. Cultural norms that promote being in close proximity, such as intergenerational households, and loss of trusted community leaders were also noted. DISCUSSION/SIGNIFICANCE: Findings reflect the needs of Black and Latinx community members in Michigan and the discussions they feel are important to highlight. We must work strategically with partners and the community to provide transparency and effective leadership, and prioritize addressing systemic disparities in SDoH.

OBJECTIVES/GOALS: Chronic stress has vast implications for health. Pathophysiological dysregulation, as evidenced by allostatic load, is associated with increased morbidity and mortality. Health disparities exist in both the incidence and outcomes of chronic stress. This study investigates the intersection of identity, allostatic load, and depression. METHODS/STUDY POPULATION: A nationally representative sample of pre-pandemic health data from the 2017-2020 cycle of the National Health and Nutrition Examination Survey (NHANES) was used to assess variation in depression and allostatic load by identity status. Datasets containing biomarker, mental health, and identity data were merged using Stata. An index of allostatic load was created by generating quartiles for nine biomarkers of cardiovascular (cholesterol, triglycerides, systolic and diastolic blood pressure), metabolic (glucose, body mass index, albumin, creatinine), and immune (c-reactive protein) functioning, and summing the total high risk biomarkers per person. Depression scores were averaged across nine items from the patient health questionnaire, and dichotomous identity variables (e.g., race) were generated. RESULTS/ANTICIPATED RESULTS: People identifying as female (t = 8.25, p < .001) or Black (t = 7.18, p < .001) have higher allostatic load scores, whereas people identifying as White (t = -2.64, p < .01) or Asian (t = -3.80, p < .001) have lower allostatic load scores. People identifying as female (t = 10.76, p < .001), White (t = 2.66, p < .01), or Another/Mixed race (t = 6.23, p < .001) have higher levels of depression, whereas people identifying as Asian (t = 9.17, p < .001) have lower levels of depression. Multiple regression analyses indicate a significant effect of depression on allostatic load when controlling for sociodemographic variables (B = 0.33, SE = 0.05, t = 7.02, p < .001). The identity*depression interaction increases allostatic load for females (B = 0.43, SE = 0.10, t = 4.21, p < .001) and racial/ethnic minority males (B = 0.25, SE = 0.10, t = 2.62, p < .01). DISCUSSION/SIGNIFICANCE: This study highlights differences in the experience of allostatic load and depression based on identity. Depression exerts an independent and moderating effect on allostatic load. Findings have implications for health disparity research, and highlight the dynamic intersection of identity, mental, and physical health in the face of chronic stress.

OBJECTIVES/GOALS: Penn State CTSI supports KL2 career development awards for faculty seeking to become leaders in clinical and translational research. CTSAs can benefit from a better understanding of KL2 applicant profiles and trainee outcomes. Predictive modeling of KL2 records provides insights into institutional processes and continuous improvement goals. METHODS/STUDY POPULATION: Collecting KL2 application records at Penn State CTSI from 2017 to 2023, comprising both accepted and not accepted candidate profiles, this study used a generalized logistic mixed model with binomial distribution to understand the factors predictive of KL2 trainee acceptance, (n=47). The following factors were modeled as potentially predictive of scholars’ acceptance: Institution-specific Processes—Campus; Terminal Degree Type; College of Residency, Applicant Demographics and Portfolio—Minoritized or Protected Groups; Mean Application Score; Rurality Focus; Gender, and Outcomes—Post-Program h-index. RESULTS/ANTICIPATED RESULTS: Only Campus and Degree were significant factors predictive of trainee acceptance (r<.0001), with a particular campus and the MD degree-designation both exerting selectional pressures on acceptance rates. Applicant demographics were not significant historical factors in selection despite the most recent trainee cohort comprised of all women. Similarly, while our CTSA focuses on rural inequality and accessibility, a research proposal focused on rurality was not a significant factor for acceptance. Notably, NIH-scaled application scores and post-program h-indices were not significant for accepted and non-accepted applicants. DISCUSSION/SIGNIFICANCE: The absence of applicant-focused selectional pressure is striking—Penn State CTSI does not significantly select for gender, URM, or URP status. Administration is now empowered to intentionally engage, recruit, and retain from our other affiliated campuses and colleges.

OBJECTIVES/GOALS: HelpBeatCOVID19.org, a novel self-reporting symptom tracking surveillance system, is based at the University of Alabama, Birmingham. Helpbeatcovid19.org captures social determinants of health (SDOH) data. This presentation will report research in progress to understand the utility of self-reported data with communicable disease outbreaks. METHODS/STUDY POPULATION: Individuals voluntarily completed an online questionnaire at HelpBeatCOVID19.org which captured SDOH data and other disease surveillance variables including zip code, gender, age group, race, ethnicity, symptoms, underlying conditions, type of home (e.g., single-family, mobile home, etc.), and household COVID-19 diagnosis status. The data are stored on HIPAA-compliant servers. De-identified self-reported data were culled from the HelpBeatCOVID19 database, cleaned, sorted, and analyzed by zip code. Using STATA/SE 16.1, we employed regression analysis to determine if there might be any statistically significant associations that could be made based on zip codes, especially where there are health disparities in historically African American neighborhoods in Jefferson County. RESULTS/ANTICIPATED RESULTS: To date, 102,308 people have reported their symptoms in HelpBeatCOVID19. Of those, 77,903 are from Alabama. More than half of the people who completed HelpBeatCOVID19.org reported zero symptoms. However, 19.3% of Alabamians reported having underlying health conditions. Midfield, AL, a predominantly African-American neighborhood (81.1%), has 74.1% of people reporting underlying conditions where the median household income is $38,750. By comparison, Vestavia Hills, AL, a more affluent neighborhood with an 88.8% White population and median household income being $109,485, had more people participating in HelpBeatCOVID19 (3,920), yet a smaller percentage (15.2%) with underlying health conditions. Final results will be reported during the ACTS Conference. DISCUSSION/SIGNIFICANCE: Our analysis of the data reveals that in Jefferson County, AL, a greater number of people in affluent communities participated in the study. Whereas state-wide, a greater percentage of individuals indicated that they had zero symptoms. Identifying self-reported underlying conditions that impact persons with COVID-19 symptoms will be significant.

Objectives/Goals: Trainees in clinical and translational science (CTS) must learn to effectively communicate their research ideas and findings to a range of audiences. As part of our science communication curriculum, we developed ORAL and WRITTEN science communication rubrics for our trainees to use across their courses and research activities. Methods/Study Population: The Tufts CTS Graduate Program is the training core of the Tufts CTSI and its associated pre- and post-doctoral T32 awards. Approximately 10 trainees with a range of backgrounds (e.g., physicians, medical students, master’s-level researchers, and basic science PhDs) matriculate each year. Faculty members and staff with expertise in science communication and pedagogy formed a committee to develop the rubrics. Because oral and written communication require different skills, we developed separate rubrics for each. We reviewed our current science communication curriculum, reviewed existing communication rubrics, and identified common mistakes students make. Following pilot testing by students and faculty pilot for one semester, we modified the rubrics based on informal feedback. Results/Anticipated Results: Both rubrics include a section to identify the target audience and specific items organized by theme. Oral rubric themes include presentation content, slides, verbal communication, nonverbal communication, and following instructions. Written rubric themes include overall, manuscript/proposal sections, and following instructions. The rubrics serve as feedback tools for faculty and students to evaluate work others produce and as self-evaluation tools. Feedback elements include a 4-point rating for each rubric item, open text feedback for each theme, and an open text holistic assessment. We now use the rubrics in our study design course, which features student presentations of planned research, and in our writing course. We anticipate collecting formal student feedback to further evaluate the rubrics. Discussion/Significance of Impact: Our rubrics can supplement existing science communication training and can be integrated into all CTS coursework and research activities. For future clinical and translational scientists to have the greatest impact, they must learn to effectively communicate findings to multiple audiences, ranging from experts in their field to the general public.

OBJECTIVES/GOALS: Metabolic syndrome (MetSyn) is a risk for World Trade Center-Lung Injury (WTC-LI; defined as developing FEV1<lower limit of normal [LLN]). Metabolic health is a modifiable disease risk factor. We propose to characterize how time-dependent covariates of MetSyn are longitudinally associated with WTC-LI. METHODS/STUDY POPULATION: WTC-particulate exposed firefighters, consented, with pre-9/11 FEV1 LLN (N = 5,746). Data assessed from last pre-9/11 till August 1, 2017. Longitudinal MetSyn characteristics were assessed using 3 models: i. A linear mixed effect model to assess the effect size of longitudinal MetSyn and its components on longitudinal FEV1% predicted as an outcome; ii. a time-dependent Cox regression to assess the associations of MetSyn to time of onset of WTC-LI; iii. a novel, partially linear single index regression model with repeatedly measured MetSyn to assess their joint effects and delineate their relative contribution on the longitudinal lung function in the WTC-FDNY cohort. RESULTS/ANTICIPATED RESULTS: In Model I, BMI 30 kg/m2 had the largest effect size compared to ever-smoking, with −2.524 (95%CI: −2.708,−2.340) compared to −1.681(−2.325,−1.038) respectively. Having MetSyn, defined as 3/5 risk factors, had an effect size of −2.319(−2.526,−2.112). In Model II, hazards of triglycerides 150mg/dL were highest at 1.497(1.336, 1.677), followed by BMI 30 kg/m2 at 1.406(1.256, 1.575), and HDL<40mg/dL 1.355(1.176-1.561), compared to ever-smoking (1.201, p = 0.002). Having high exposure to PM by being present in the morning of 9/11 was a significant covariate only in Model II investigating HDL<40mg/dL or triglycerides 150mg/dL. Model III The proposed methods will be applied to our cohort study. DISCUSSION/SIGNIFICANCE OF IMPACT: MetSyn is both a predictor and concurrent marker of WTC-LI. The single index model can not only reduce dimensionality of the covariates, but also provides efficient estimates of the joint MetSyn effects, allowing linear or nonlinear effects. Future studies will investigate dietary intervention as a potential disease-modifying factor. CONFLICT OF INTEREST DESCRIPTION: NA, nothing to disclose.

OBJECTIVES/SPECIFIC AIMS: The study aims to utilize event-related potentials (ERPs) coupled with observable reports of symptoms to comprehensively understand neurological and symptomatic profile of individuals at risk for developing psychosis. The study is a short-term longitudinal design which allows for examination of course as well as structure of illness. The primary outcome is to map known neuroclinical deficits among individuals with schizophrenia onto a high-risk, non-clinical sample. A secondary aim of the study is to demonstrate prediction of symptom severity over time measured by a combination of ERPs and clinical symptom scores. METHODS/STUDY POPULATION: Recruited participants are pre-screened for eligibility via telephone interview. This process includes administration of Community Assessment of Psychotic Experiences (CAPE), and the Mini International Neuropsychiatric Interview (MINI). During in-person lab assessment, participants provide written informed consent and complete a battery of ERP tasks, semi-structured clinical interviews, and self-report questionnaires that assess for presence and severity of sub-threshold psychotic-like experiences. Six months following the laboratory visit, participants will be provided a link to online questionnaires that were completed during laboratory visit in order to reassess presence and severity. RESULTS/ANTICIPATED RESULTS: The target number of participants included in this study is 60. We hope to recruit individuals who range in symptom severity as measured by CAPE. It is of interest to determine relationship among known deficits in individuals with schizophrenia and individuals exhibiting sub-clinical symptoms of psychosis. Additionally, we plan to examine ERPs and symptoms together as a “profile” of high risk psychosis, yielding more robust information about this population than any one ERP or symptom measure alone. The within subjects design of this study allows for examination of symptom progression and potential prediction of symptoms based on brain activity. Many studies examine only single ERP components thus limiting the ability to draw broader conclusions regarding general cognitive frameworks among populations. We use a combination of well-validated ERPs (i.e. P300, N400, ERN) with behavioral and symptom data in order to predict variation in symptoms over the course of 6 months. The project aims to take a novel approach at identifying high-risk profiles based on neurophysiological and behavioral data and using this as a basis for predicting symptom severity across time. DISCUSSION/SIGNIFICANCE OF IMPACT: Individuals endorsing psychotic-like experiences are at heightened risk for developing a psychotic disorder in the future, and have been linked with similar social, behavioral, and emotional risk factors similar to those of schizophrenia. Subjective data (e.g. self-report, interview) sheds light on important information regarding observable symptom manifestation; however, neural measures can detect relatively subtle deficits in information processing that precede and predict overt symptom onset, which necessitates other important methodological considerations. Specifically, extant literature has shown that quantifiable indices of cognitive deficits may represent a vulnerability to psychosis in high-risk populations, and can be measured using event-related potentials (ERPs). This study integrates a psychophysiological approach by mapping neural deficits from schizophrenia onto a high-risk sample. Many studies examine only single ERP components thus limiting the ability to draw broader conclusions regarding general cognitive frameworks among populations. We use a combination of well-validated ERPs (i.e. P300, N400, ERN) with behavioral and symptom data in order to predict variation in symptoms over the course of 6 months. The project aims to take a novel approach at identifying high-risk profiles based on neurophysiological and behavioral data and using this as a basis for predicting symptom severity across time. We will parse heterogeneity within a high-risk group in order to create innovative profiles and potentially predict variation in course of symptoms. In other words, a “fingerprint” physiologic aberration may be exhibited within high-risk individuals and can be used as biomarkers to identify those at risk even before onset of observable symptoms.

OBJECTIVES/SPECIFIC AIMS: Enhancing Patient Safety for hospitalized patients is a priority for healthcare facilities, providers, and federal funding agencies. Multidisciplinary partnerships in clinical and translational research better defines the scope of complex patient-safety issues, and is part of more effectively developing interventions. The discipline represented by engineering-trained partners brings valuable perspective to patient safety problems through their training background in human factors and systems analysis. The objective of this education program was to create and implement a collaboration between engineering students and clinical providers. Through the Johns Hopkins Institute for Clinical and Translational Research, a multidisciplinary partnership was created, to identify contributing factors, and suggest novel solutions, to key patient safety problems using an ethnographic research approach. METHODS/STUDY POPULATION: The collaboration was formed between the following Johns Hopkins (JH) groups: (1) The Institute for Clinical and Translational Research (ICTR), (2) The Armstrong Institute for Patient Safety, (3) The JH Hospital Clinical Engineering Services, (4) The Homecare Group, (5) The Masters of Science in Engineering Management Program at the Whiting School of Engineering, and (6) The JH Hospital Risk Management. All 6 provided representation to contribute to the planning, structure, and implementation of the project. The initial cohort was 24 masters students enrolled in the JHU Whiting School of engineering, and included 46% men, 54% women, and 75% international students. Students were placed in teams of 2–3 to work on 9 distinct patient safety concerns, as provided by the Armstrong Institute as priority. Potential clinical hosts from the appropriate clinical departments were vetted for feasibility and scope before students were assigned to them. Students and clinical hosts were oriented to the process. The students then spent 3–6 hours a week, for 7 weeks, observing and interacting with patients and health professionals at their specific clinical sites, conducting ethnographic research under the guidance of their hosts. Ethnographic research is the systematic investigation of a culture or system; in our application, teams were looking at the environment, culture, and its contributing factors, with respect to patient safety issues. Teams made observations, then formulated hypothesis and collected data relevant to what systems factors may be contributing to the patient safety issue. Following data collection and analyses, teams made recommendations for culture and/or systems shifts that could impact change and improve patient safety. Ethnography research process training is a tenet of the training undertaken by all Masters of Science in Engineering Management Students. RESULTS/ANTICIPATED RESULTS: At the end of the 7-week project, each team generated a comprehensive report suggesting potential solutions for each problem, and gave presentations on their findings to their peers, clinical hosts, and JHU steering committee representatives. Requirements on the student side included a midterm, final presentation, and report. Both students and site leaders submitted mid- and final program evaluations. Based on follow-up survey data, 71% of students said that the course may impact their career choice, 57% said the collaboration changed the way they viewed themselves, and 28% elected to continue working or were planning to work with their site in some fashion after the course ended. Nearly 60% of students believed additional funding or resources would benefit the course and 71% thought they would benefit from more or similar experiences with their clinical partners. Furthermore, 85% wanted to see the course expanded. Of the clinical hosts, 71% said that students added value, 86% believed students changed their perspective on their problem, unveiled new areas of investigation, and improved or likely would improve patient safety in their department. Seventy-one percent of hosts were actively acting on the students’ findings, and over 86% shared findings with their colleagues. Following the 7-week program, 2 teams also presented their findings to committees within the hospital departments, 2 patient-safety projects are being continued with engineering teams, and 2 new collaborative projects have been initiated. Based on the popularity of this program with the students, hosts, and teaching faculty, this will be implemented within the engineering curriculum for a second time next year. Additional outcomes data collection are currently ongoing, and we plan to continue to monitor and analyze results. DISCUSSION/SIGNIFICANCE OF IMPACT: In its first year our engineering collaboration exceeded expectations. Engineering students and clinical providers successfully worked toward tangible solutions that were directly applicable to patient care. Furthermore, interactions were both personally and professionally beneficial for students while simultaneously adding value to clinical hosts. Beyond the collaboration, this initiative allowed for secondary connections between engineers and clinicians that are also have great potential for resulting in translational innovation. Despite the overwhelming success of this project, it highlighted the need for increased resources for sustainability. Our pilot highlighted a role for funding with regards to: (1) students in the execution of their projects (eg, transportation to sites, prototype materials); (2) clinical hosts, particularly protecting time to interact with and lead student teams; (3) the Armstrong Institute—to aid the identification and prioritization of high impact, patient safety projects; and (4) the ICTR for staff to facilitate placements, student orientation to the hospital setting, and program execution and maintenance. Ultimately, this collaboration addressed an unmet need for the clinical providers as well as the engineering students: thus, all partners agree that (1) the impact of this pilot would be greatly magnified by more time, longer duration, and additional resources; and (2) this collaboration could provide a useful model for approaching other complex health care problems. In terms of larger and longer-term impact, engaging engineers at the training level together with clinicians provides early exposure, and could potentially prime them to continue collaborations with clinical and translational science, across their careers.

Student Research Assistant Acknowledgements: The authors thank Manik Arora, Alexandra Morani, and Thomas Cornish -- Johns Hopkins University.

ABSTRACT IMPACT: E-values can help quantify the amount of unmeasured confounded necessary to fully explain away a relationship between treatment and outcomes in observational data. OBJECTIVES/GOALS: Older patients with HL have worse outcomes than younger patients, which may reflect treatment choice (e.g., fewer chemotherapy cycles). We studied the relationship between treatment intensity and 3-year overall survival (OS) in SEER-Medicare. We calculated an E-value to quantify the unmeasured confounding needed to explain away any relationship. METHODS/STUDY POPULATION: This retrospective cohort study of SEER-Medicare data from 1999-2016 included 1131 patients diagnosed with advanced stage HL at age ≥65 years. Treatment was categorized as: (1) full chemotherapy regimens (‘full regimen’, n=689); (2) partial chemotherapy regimen (‘partial regimen’, n=175); (3) single chemotherapy agent or radiotherapy (‘single agent/RT’, n=102), or (4) no treatment (n=165). A multivariable Cox regression model estimated the relationship between treatment and 3-year OS, adjusting for disease and patient factors. An E-value was computed to quantify the minimum strength of association that an unmeasured confounder would need to have with both the treatment and OS to completely explain away a significant association between treatment and OS based on the multivariable model. RESULTS/ANTICIPATED RESULTS: Results from the multivariable model found higher hazards of death for partial regimens (HR=1.81, 95% CI=1.43, 2.29), single agent/RT (HR=1.74, 95% CI=1.30, 2.34), or no treatment (HR=1.98, 95% CI=1.56, 2.552) compared to full regimens. We calculated an E-value for single agent/RT because it has the smallest HR of the treatment levels. The observed HR of 1.74 could be explained away by an unmeasured confounder that was associated with both treatment and OS with a HR of 2.29, above and beyond the measured confounders; the 95% CI could be moved to include the null by an unmeasured confounder that was associated with both the treatment and OS with a HR of 1.69. Of the measured confounders, B symptoms had the strongest relationship with treatment (HR=2.08) and OS (HR=1.38), which was below the E-value. DISCUSSION/SIGNIFICANCE OF FINDINGS: Patients with advanced stage HL who did not receive full chemotherapy regimens had worse 3-year OS, even after adjusting for potential confounders related to the patient and disease. The E-value analysis made explicit the amount of unmeasured confounding necessary to fully explain away the relationship between treatment and OS.

Translation of critical and broadly impactful health advancements is stymied by insufficient scientific scrutiny of barriers and roadblocks in the process. The Clinical & Translational Science Award (CTSA) funding opportunity announcement released in July 2021 makes clear the distinction between translational research and translational science (TS) and urges a shift from the former to the latter. This represents a significant shift in the overall scientific direction of the CTSA program and necessitates corresponding shifts in CTSA hub operations. To better support TS, the Team Science Core of the Duke CTSA hub designed and facilitated a virtual retreat for hub personnel that (1) enabled organizational learning about TS and (2) identified anticipated challenges and opportunities. A post-retreat survey was utilized to assess the degree to which the retreat met its stated goals. Our survey received a 62% response rate; 100% of respondents would recommend the session to others. Respondents also reported gains in all areas assessed, with evidence for greater understanding of TS and increased perspective of the value and relevance of TS. In this paper, we provide a roadmap for designing and implementing facilitated TS retreats, which we argue is a key step in TS capacity building through workforce development.

The Physician-Scientist Trainee Diversity Summit, hosted by the American Physician Scientists Association and the Burroughs Wellcome Fund, was conceived in 2019 with the mission of developing strategic plans to diversify the physician-scientist community using human-centered design thinking. In June 2024, the second iteration of this conference was held in Raleigh, North Carolina, and brought together a network of scientific and medical organizations to discuss issues of justice, equity, diversity, and inclusion facing physician-scientist trainees. This article summarizes the progress made from the first meeting, the proceedings of the 2024 Summit, and a thematic analysis of the recent meeting, offering tangible solutions to the physician-scientist community for supporting diversity and accessibility.