OBJECTIVES/GOALS: We compared the validity of an International Classification of Diseases, Clinical Modification (ICD) algorithm for identifying high-grade cervical intraepithelial neoplasia and adenocarcinoma in situ (together referred to as CIN2+) from ICD 9th revision (ICD-9) and 10th revision (ICD-10) codes. METHODS/STUDY POPULATION: Using Tennessee Medicaid data, we identified cervical diagnostic procedures in 2008-2017 among females aged 18-39 years in Davidson County, TN. Gold-standard cases were pathology-confirmed CIN2+ diagnoses validated by HPV-IMPACT, a population-based surveillance project in catchment areas of five US states. Procedures in the ICD transition year (2015) were excluded to account for implementation lag. We pre-grouped diagnosis and procedure codes by theme. We performed feature selection using least absolute shrinkage and selection operator (LASSO) logistic regression with 10-fold cross validation and validated models by ICD-9 era (2008-2014, N = 6594) and ICD-10 era (2016-2017, N = 1270). RESULTS/ANTICIPATED RESULTS: Of 7864 cervical diagnostic procedures, 880 (11%) were true CIN2+ cases. LASSO logistic regression selected the strongest features of case status: Having codes for a CIN2+ tissue diagnosis, non-specific CIN tissue diagnosis, high-grade squamous intraepithelial lesion, receiving a cervical treatment procedure, and receiving a cervical/vaginal biopsy. Features of non-case status were codes for a CIN1 tissue diagnosis, Pap test, and HPV DNA test. The ICD-9 vs ICD-10 algorithms predicted case status with 68% vs 63% sensitivity, 95% vs 94% specificity, 63% vs 64% positive predictive value, 96% vs 94% negative predictive value, 92% vs 89% accuracy, and C-indices of 0.95 vs 0.92, respectively. DISCUSSION/SIGNIFICANCE OF IMPACT: Overall, the algorithm’s validity for identifying CIN2+ case status was similar between coding versions. ICD-9 had slightly better discriminative ability. Results support a prior study concluding that ICD-10 implementation has not substantially improved the quality of administrative data from ICD-9.

OBJECTIVES/SPECIFIC AIMS: To examine the relationship between healthy eating, physical activity (PA), sleep problem and hours of sleep and cognitive function among elderly population and the racial/ethnic differences in this relation. METHODS/STUDY POPULATION: We analyzed data from National Health and Nutrition Examination Survey 2014-2016 for 882 population 60 years and older. Cognitive status was measured by the Digit Symbol Substitution (DSS) exercise score and the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) total score. Healthy eating index (HEI), PA, and sleep problem and hours of sleep were assessed by questionnaire. The association between cognitive function and HEI, PA, sleep problem and hours of sleep were assessed by linear regression after adjusting for age, gender, race/ethnicity, poverty level, lipid profile, fasting glucose level, alcohol, body mass index, stroke and education. Data were analyzed using Stata 14 considering design and sample weight and p<0.05 is statistically significant. RESULTS/ANTICIPATED RESULTS: CERAD total score was associated with HEI (Adjusted B = 0.07, 95% Confidence Interval (CI) = 0.01-0.13, p = 0.02) and not associated with physical activity or sleep problem or hours of sleep (p > 0.05). Animal fluency score was associated only with HEI (Adjusted B = 0.05, 95% CI = 0.01-0.09, p = 0.02). DDS score was not associated with HEI, PA, or sleep problem (p > 0.05) but associated with hours of sleep (p = 0.03). Stratified analysis by race/ethnicity showed that CERAD total score was associated with HEI only in White (Adjusted B = 0.08, 95% CI = 0.01-0.15, p = 0.02). DISCUSSION/SIGNIFICANCE OF IMPACT: CERAD total score was associated with HEI and not associated with PA or sleep problem. Promoting healthy eating is important for improving cognition in elderly population. Culturally sensitive and linguistically appropriate programs that involve community and care providers are needed to promote healthy eating for elderly population.

OBJECTIVES/GOALS: The Clinical Research Professionals (CRP) group was founded in 2000 by research managers at Cincinnati Children’s Hospital Medical Center (CCHMC) as an avenue to share research processes, ideas, successes, and community. The group has developed and evolved at CCHMC to meet the needs of CRP members. METHODS/STUDY POPULATION: CRP has evolved to meet the needs of the clinical research community at CCHMC. In 2008, monthly education meetings and a Research Educator supported a standardized onboarding process. CRP hosted the inaugural CRP Appreciation Day in 2010 to recognize researchers. The group established the CRP Leadership Committee (CRPLC) in 2011 and by 2016 expanded to include subcommittees. Career development opportunities include onsite clinical research certification exams and a centralized process for advancement. CRP updated processes and onboarding materials to electronic formats during the pandemic and now includes nursing, data management, and University of Cincinnati representation on the CRPLC. RESULTS/ANTICIPATED RESULTS: The culmination of 20+ years of CRP has led to the current state of CRP at CCHMC. CRP operates an internal website to collate all activities and resources, including educational opportunities and helpful links related to CCHMC research processes. CRP currently has leadership, membership, education, and regulatory committees providing opportunities for all clinical research professionals to join, collaborate, and grow within CCHMC and beyond. Established career pathways and centralized CRP advancement guidelines support career development. The centralized REDCap onboarding tool is accessible at any time for initial onboarding and then continuing education. DISCUSSION/SIGNIFICANCE: The formation and evolution of CRP, developed by work of past CRPs, has fostered an innovative community to meet clinical research needs through education, career development, and process standardization. We aim to continue to disseminate knowledge and lessons learned beyond our institutional walls.

OBJECTIVES/SPECIFIC AIMS: The purpose of the present secondary data analysis was to examine the effect of moderate-severe disturbed sleep before the start of radiation therapy (RT) on subsequent RT-induced pain. METHODS/STUDY POPULATION: Analyses were performed on 676 RT-naïve breast cancer patients (mean age 58, 100% female) scheduled to receive RT from a previously completed nationwide, multicenter, phase II randomized controlled trial examining the efficacy of oral curcumin on radiation dermatitis severity. The trial was conducted at 21 community oncology practices throughout the US affiliated with the University of Rochester Cancer Center NCI’s Community Oncology Research Program (URCC NCORP) Research Base. Sleep disturbance was assessed using a single item question from the modified MD Anderson Symptom Inventory (SI) on a 0–10 scale, with higher scores indicating greater sleep disturbance. Total subjective pain as well as the subdomains of pain (sensory, affective, and perceived) were assessed by the short-form McGill Pain Questionnaire. Pain at treatment site (pain-Tx) was also assessed using a single item question from the SI. These assessments were included for pre-RT (baseline) and post-RT. For the present analyses, patients were dichotomized into 2 groups: those who had moderate-severe disturbed sleep at baseline (score≥4 on the SI; n=101) Versus those who had mild or no disturbed sleep (control group; score=0–3 on the SI; n=575). RESULTS/ANTICIPATED RESULTS: Prior to the start of RT, breast cancer patients with moderate-severe disturbed sleep at baseline were younger, less likely to have had lumpectomy or partial mastectomy while more likely to have had total mastectomy and chemotherapy, more likely to be on sleep, anti-anxiety/depression, and prescription pain medications, and more likely to suffer from depression or anxiety disorder than the control group (all p’s≤0.02). Spearman rank correlations showed that changes in sleep disturbance from baseline to post-RT were significantly correlated with concurrent changes in total pain (r=0.38; p<0.001), sensory pain (r=0.35; p<0.001), affective pain (r=0.21; p<0.001), perceived pain intensity (r=0.37; p<0.001), and pain-Tx (r=0.35; p<0.001). In total, 92% of patients with moderate-severe disturbed sleep at baseline reported post-RT total pain compared with 79% of patients in the control group (p=0.006). Generalized linear estimating equations, after controlling for baseline pain and other covariates (baseline fatigue and distress, age, sleep medications, anti-anxiety/depression medications, prescription pain medications, and depression or anxiety disorder), showed that patients with moderate-severe disturbed sleep at baseline had significantly higher mean values of post-RT total pain (by 39%; p=0.033), post-RT sensory pain (by 41%; p=0.046), and post-RT affective pain (by 55%; p=0.035) than the control group. Perceived pain intensity (p=0.066) and pain-Tx (p=0.086) at post-RT were not significantly different between the 2 groups. DISCUSSION/SIGNIFICANCE OF IMPACT: These findings suggest that moderate-severe disturbed sleep prior to RT is an important predictor for worsening of pain at post-RT in breast cancer patients. There could be several plausible reasons for this. Sleep disturbance, such as sleep loss and sleep continuity disturbance, could result in impaired sleep related recovery and repair of tissue damage associated with cancer and its treatment; thus, resulting in the amplification of pain. Sleep disturbance may also reduce pain tolerance threshold through increased sensitization of the central nervous system. In addition, pain and sleep disturbance may share common neuroimmunological pathways. Sleep disturbance may modulate inflammation, which in turn may contribute to increased pain. Further research is needed to confirm these findings and whether interventions targeting sleep disturbance in early phase could be potential alternate approaches to reduce pain after RT.

Objectives/Goals: Skilled clinical research professionals are essential to efficient and effective research teams, but many undergraduate students are not aware of staff-level careers in the field. To address this, the Michigan Institute for Clinical and Health Research launched the Clinical and Health Research Professional (CHRP) Pathways Program. Methods/Study Population: The 10-week hybrid CHRP Pathways Program was piloted in Summer 2024. Undergraduate students interested in research careers were recruited from local universities and community colleges. The program consisted of 8 hours/week of didactic curriculum guided by ECRPTQ competencies and 32 hours/week of mentored work experience on research teams. Classroom activities were completed with participants from a partner program within the university. Surveys were administered at 3 weeks and 10 weeks along with an end-of-program focus group to assess acceptability and self-reported learning outcomes. Results/Anticipated Results: The pilot CHRP Pathways Program enrolled 3 students and was well-received by participating students and mentor research teams. Students agreed that they would recommend the program to their classmates. Students indicated that the program helped them understand the role of a study coordinator, provided insight into research career paths, and helped them form professional relationships. Self-reported confidence levels in a range of research competencies increased. Two students who completed the 10-week program chose to continue working part-time with their research teams while continuing their undergraduate studies. Discussion/Significance of Impact: Experiences like the CHRP Pathways Program provide valuable exposure to staff-level research career opportunities for students engaged in health science studies. They can address an existing workforce gap by equipping college graduates with relevant work experience and basic research competencies.

OBJECTIVES/GOALS: The objectives of this study are to: 1) Examine the feasibility of using concurrent multi-methodology to quantify the affective, decisional, physical symptom domains of Family Intensive Care Unit Syndrome. 2) Describe the associations between the Family Intensive Care Unit Syndrome symptom domains (affective, decisional, physical). METHODS/STUDY POPULATION: Using a repeated-measures, correlational design, we recruited surrogate decision makers of incapacitated, mechanically ventilated patients within four adult intensive care units at a tertiary medical center in northeast Ohio. We collected baseline data (T1) after obtaining informed consent and follow-up data three (T2) and seven (T3) days post-baseline. We used self-report instruments, behavioral tasks, and accelerometry to measure affective (anxiety & depression), decisional (working memory ability & decision fatigue), and physical (sleep quality & sleep disturbance), symptom domains. For objective 1, we computed completion percentages of each time-point and overall compliance with wearing the accelerometer device. For objective 2, we inspected Spearman correlations. RESULTS/ANTICIPATED RESULTS: We recruited and collected baseline data (T1) for 33 participants. Nineteen participants completed the T2 interview and twenty participants completed T3. Eight participants wore the accelerometer device for less than 72 hours and 15 wore the accelerometer for the full study period. At baseline, the physical symptoms (sleep quality & disturbance) were strongly correlated with the affective (anxiety & depression) symptoms, all of which were moderately correlated with one decisional symptom (decision fatigue). One cognitive symptom (working memory dysfunction), was weakly associated with the affective and physical symptom domains, and moderately correlated with the other cognitive symptom (decision fatigue) These associative trends were maintained within and between each time point. DISCUSSION/SIGNIFICANCE: This study identifies potential opportunities for examining the various symptom domains of Family Intensive Care Unit Syndrome. The associations between the domains are consistent with the extant literature, and highlights the need for supportive interventions that mitigate affective and physical symptoms that can compromise decision-making.

OBJECTIVES/GOALS: We seek to develop an automated deep learning-based method for segmentation and volumetric quantification of the fetal brain on T2-weighted fetal MRIs. We will evaluate the performance of the algorithm by comparing it to gold standard manual segmentations. The method will be used to create a normative sample of brain volumes across gestational ages. METHODS/STUDY POPULATION: We will adapt a U-Net convolutional neural network architecture for fetal brain MRIs using 3D volumes. After re-sampling 2D fetal brain acquisitions to 3mm3 3D volumes using linear interpolation, the network will be trained to perform automated brain segmentation on 40 randomly-sampled, normal fetal brain MRI scans of singleton pregnancies. Training will be performed in 3 acquisition planes (axial, coronal, sagittal). Performance will be evaluated on 10 test MRIs (in 3 acquisition planes, 30 total test samples) using Dice scores, compared to radiologists’ manual segmentations. The algorithm’s performance on measuring total brain volume will also be evaluated. RESULTS/ANTICIPATED RESULTS: Based on the success of prior U-net architectures for volumetric segmentation tasks in medical imaging (e.g. Duong et al., 2019), we anticipate that the convolutional neural network will accurately provide segmentations and associated volumetry of fetal brains in fractions of a second. We anticipate median Dice scores greater than 0.8 across our test sample. Once validated, the method will retrospectively generate a normative database of over 1500 fetal brain volumes across gestational ages (18 weeks to 30 weeks) collected at our institution. DISCUSSION/SIGNIFICANCE OF IMPACT: Quantitative estimates of brain volume, and deviations from normative data, would be a major advancement in objective clinical assessments of fetal MRI. Such data can currently only be obtained through laborious manual segmentations; automated deep learning methods have the potential to reduce the time and cost of this process.

We assessed the rigor and reproducibility (R&R) activities of institutions funded by the National Center for Advancing Translational Sciences (NCTSA) through a survey and website search (N = 61). Of 50 institutional responses, 84% reported incorporating some form of R&R training, 68% reported devoted R&R training, 30% monitored R&R practices, and 10% incentivized them. Website searches revealed 9 (15%) freely available training curricula, and 7 (11%) institutional programs specifically created to enhance R&R. NCATS should formally integrate R&R principles into its translational science models and institutional requirements.

Academic statistical consulting centers collaborate and provide statistical support to faculty and graduate students for their research projects. Clients seeking statistical support may face communication and cognitive accessibility barriers that consultants may not be aware of. Many researchers do not disclose their personal circumstances, such as neurodivergent behaviors. Universal Design for Learning is a framework that aims to support individuals regardless of their abilities or learning styles, by providing multiple means of engagement, presentation, and action and expression. Adapting these principles for statistical consulting and taking time to assess specific needs of individuals helps improve the experiences of clients and consultants and leads to high quality statistical support and successful completion of research projects. We propose an organizational framework applicable to statistical consulting environments aligned with accessibility guidelines to improve access and empower clients and consultants. Providing a respectful environment where differences are normalized and welcomed creates a sense of belonging for everyone.

ABSTRACT IMPACT: Understanding the longitudinal glucose changes following SARS-CoV-2 infection can inform point-of-care guidelines and elucidate the viral hypothesis of diabetes mellitus pathogenesis. OBJECTIVES/GOALS: Hyperglycemia has emerged as an important manifestation of SARS-CoV-2 infection in both diabetic and non-diabetic patients. Whether clinically-detectable glycemic changes persist following SARS-CoV-2 infection remain to be elucidated. This work aims to characterize temporal patterns in glucose dysregulation following SARS-CoV-2 infection. METHODS/STUDY POPULATION: Electronic health records of patients with a diagnosis of COVID-19, positive laboratory test for SARS-CoV-2, and negative history of Diabetes Mellitus prior to infection were extracted from the TriNetX database. 7,502 patients with at least one blood glucose value 2 years to 2 weeks before, 2 weeks before to 2 weeks after, and 2 weeks after to 1 year after COVID-19 diagnosis were used for analysis. Temporal patterns are characterized by training state-of-the-art clustering algorithms, including fuzzy short time-series clustering, k-means for longitudinal data, and spectral clustering. Clustering performance is evaluated using internal evaluation metrics of the Silhouette coefficient, Calinski-Harabasz score, and Davies Bouldin index. RESULTS/ANTICIPATED RESULTS: Based on the success of prior clustering methods with random blood glucose measurements, we anticipate that the proposed time-series clustering algorithms will appropriately characterize temporal patterns of glycemic dysregulation. The best performing algorithm based on interval evaluation metrics will be selected for further analysis. Associations between blood glucose values and cluster membership will be evaluated using Kruskal-Wallis one-way ANOVA and effect size will be calculated using unbiased Cohen’s d. Clinical phenotypes for each cluster will be characterized in terms of current diagnoses, prior medication use, pertinent laboratory tests, and vital signs. DISCUSSION/SIGNIFICANCE OF FINDINGS: A clearer understanding of the longitudinal glucose changes following SARS-CoV-2 infection can elucidate clinically-detectable patterns of glycemic dysregulation, identify sub-phenotypes of patients who are more susceptive to glycemic dysregulation, and inform appropriate point-of-care guidelines.

OBJECTIVES/SPECIFIC AIMS: A course of oral broad-spectrum antibiotics frequently has a positive effect on morbidity and mortality in severe acute malnutrition (SAM), but the actual mechanism for this effect is unknown. This mechanism is especially important to find and quantify because of the possibility that using antibiotics prophylactically may accelerate the danger from antibiotic resistant infections. This study aims to answer (1) how antibiotic therapy improves the nutritional recovery and (2) how much it affects the prevalence of resistance genes in the microbiome. METHODS/STUDY POPULATION: Stool samples were collected from children with SAM between 6 and 60 weeks of age who received either one week of amoxicillin or placebo (n=164). The children were followed for 12 weeks with longitudinal sampling, and a subset were followed out to 2 years. All samples were frozen at −80°C and prepared for metagenome shotgun sequencing via the Illumina Nextera platform. RESULTS/ANTICIPATED RESULTS: Antibiotic treatment at the start of the nutritional program is associated with significant improvements in weight gain, mid-upper-arm circumference, and graduation from the treatment program. It is also associated with qualitative decreases in early-life fermenter Lactobacillus and known enteropathogen Campylobacter. Two years after the use of amoxicillin, the Shannon diversity index is significantly higher than that of malnourished children (effect size 0.507, 95% CI: 0.204–0.630, p=0.0007), while children who received placebo are not distinguishable from malnourished children by the same metric (effect size 0.147, 95% CI: −0.311, 0.630, p=0.5878). Sustained antibiotic resistance gene enrichment within the microbiota did not occur, as the enrichment effects disappears by week 4 of follow-up. DISCUSSION/SIGNIFICANCE OF IMPACT: The use of amoxicillin to treat uncomplicated SAM has therapeutic benefits visible by anthropometry and by content of the gut microbiota. The main concern with the use of prophylactic antibiotics for this purpose is the effect on antibiotic resistance gene enrichment in the children’s microbiota. This concern was not supported here. The benefit/cost ratio for the use of prophylactic antibiotics for individuals in this cohort is positive when weighing effects on anthropometry, microbiome, and antibiotic resistance. The results of this study impact the treatment of millions of children each year at nutritional therapy clinics around the world.

OBJECTIVES/SPECIFIC AIMS: To investigate the prognostic value of left ventricular mitral annular longitudinal displacement (LD) measured with color tissue Doppler imaging (TDI) in a large population suffering from acute coronary syndrome (ACS). METHODS/STUDY POPULATION: In total, 501 ACS patients underwent an echocardiography within 9 days after a percutaneous coronary intervention. Regional LD was obtained from the 6 mitral annular regions with TDI and GLD was calculated as an average. RESULTS/ANTICIPATED RESULTS: During a median follow-up time of 4.4 years 46 ACS patients suffered CVD. Mean value of GLD in the population was 8.11mm (±2.4). GLD and LD obtained from the inferior wall remained significant independent predictors after multivariate adjustment for clinical parameters, GLD (HR: 1.43, 95% CI: 1.12–1.82, p=0.014, per 1mm decrease), inferior LD (HR: 1.38, 95% CI: 1.14–1.66, p=0.001). Furthermore, inferior wall LD was the primary source of prognostic information in GLD since only inferior LD remained significant when both measures were included in the same model: GLD (HR: 0.95, 95% CI: 0.64–1.40, p=0.781); inferior LD (HR: 1.60, 95% CI: 1.15–2.22, p=0.005). Of all walls, only inferior wall LD remained as an independent predictor after multivariate adjustment. DISCUSSION/SIGNIFICANCE OF IMPACT: GLD provides independent prognostic information in ACS patients over and beyond all conventional echocardiographic measures. Regional inferior LD was the primary source of prognostic information gained from GLD. GLD proved to be a better predictor of cardiovascular events than conventional echocardiographic measures. This could lead to better risk stratification in the clinical setting and open up for earlier intervention in high-risk individuals.

OBJECTIVES/GOALS: A diverse workforce is an aspiration of CTSIs and embedded in goals to build D&I workforce capacity. However, little research describes the diversity of the current workforce. We assessed current assets, opportunities, and diversity of D&I efforts at Frontiers and characterized investigators and those supported by services offered. METHODS/STUDY POPULATION: In January 2021, Frontiers convened a working group to identify existing D&I assets and needs in the CTSI multi-state catchment area. The committee catalogued existing training and consultation resources and services, which the CTSI supported with infrastructure to support, track, and evaluate ongoing efforts. We obtained data from the evaluation platform and conducted descriptive analyses of the investigators and service uptake among two programs offered, contrasting the workforce with national data obtained from the American Academy of Medical Colleges (2022) and the National Institutes of Health (2018). RESULTS/ANTICIPATED RESULTS: Ninety individuals at 9 institutions across Kansas and Missouri identified as implementation researchers. Since 2022, 28 D&I consultations were provided, 92% for grant applications. Five early-stage investigators were identified for career development in an NIH-supported Health Equity and Implementation Center (ESI). The network mirrors the larger workforce regarding underrepresented racial/ethnic minorities (18%) and new investigators (60%). More women (76%) are represented in the D&I network and among ESIs (80%) than the academic workforce (44%), but significantly fewer women used consultation services (p<0.001). Lower proportions of underrepresented minority investigators (p<0.001) and investigators from disadvantaged statuses (p=0.027) accessed consultations services. DISCUSSION/SIGNIFICANCE: Investigators underrepresented in science on multiple dimensions were less likely to use consulting services, Outreach for consultation services may be necessary, if needs are not being met in other programs. Further exploration of overall D&I workforce trends is needed to ensure goals for the field and the CTSA network are achieved.

OBJECTIVES/GOALS: Autism spectrum disorder (ASD) is a developmental disorder with a high financial and personal burden. Individuals with ASD experience significant comorbid medical conditions. We identified conditions that appear prior to ASD diagnosis in order to potentially improve early screening practices. METHODS/STUDY POPULATION: We used electronic health record data from an anonymized database at Vanderbilt University Medical Center for individuals with ASD and matched controls to analyze comorbid conditions prior to an ASD diagnosis. Data were censored to include only individuals who first appeared in the databank prior to two years of age (ntotal = 1551, ncontrols = 976, nASD = 575). Comorbidities (~1800 conditions) were compared between the ASD and matched control group using a novel tool (pyPheWAS) to examine presence, count, and duration of comorbidities that occurred between 0-2 years old and before ASD diagnosis. RESULTS/ANTICIPATED RESULTS: Convulsions (p = 0.000404, ß = 0.807), constipation (p = 0.000789, ß = 0.894), and strabismus (p = 0.00243, ß = 1.155) were the most significant comorbid conditions prior to age 2 in individuals who would later be diagnosed with ASD. The group with ASD also had more visits associated with convulsions (p = 0.00511, ß = 0.195), diseases of the esophagus (p = 0.0117, ß = 1.675), and allergic reactions to food (p = 0.0119, ß = 0.540) prior to their diagnosis. The ASD group was also seen for a longer duration regarding convulsions (p = 0.000273, ß = 0.695), constipation (p = 0.00157, ß = 0.712), and malaise and fatigue (p = 0.00188, ß = 0.903) before ASD diagnosis. DISCUSSION/SIGNIFICANCE OF IMPACT: Precise comorbid condition profiles in early childhood may help uncover biomarkers leading to better prediction of a future ASD diagnosis. Medical conditions that precede the onset of measurable behavioral symptoms may enhance early screening, treatment, and intervention in ASD.