Objectives/Goals: Rare pediatric cancer research has been hindered by limited sample size and disease-specific databases. The training and infrastructure required to create these datasets have been cost prohibitive. Automated electronic medical record (EMR) extraction and natural language processing (NLP) hold the potential to overcome these limitations. Methods/Study Population: In a mixed population of adults and children with thyroid cancer, we implemented an automated data extraction process for multiple data types, including structured demographic, laboratory, and billing data and unstructured free text from pathology reports. Data extraction and cleaning software transformed EMR data into a research-ready database. NLP using regular expressions extracted meaningful, structured data from free-text pathology reports. Results from the automated data extraction and NLP were compared to manual extraction, and results were quantified using accuracy, precision, recall, F1-score, and Cohen’s Kappa to account for random agreement. Additional iterations using machine learning and large language models will be performed to compare accuracy and identify an optimal strategy. Results/Anticipated Results: From 2016 to 2023, 2,899 patients with thyroid cancer were identified. In a 50-patient subset, manual extraction of pathology reports was compared to NLP-derived data. Histology type was correctly identified in 90% of cases (kappa = 0.85), and thyroid procedure identification accuracy was 89% (kappa 0.81). Focality was 98% accurate (kappa=0.96). Vascular, lymphatic, and perineural invasion, along with extrathyroidal extension and pathologic tumor stage, were 100% accurate. Pathologic nodal staging was 96% accurate (kappa=0.89). Kappa greater than 0.8 is considered almost perfect agreement. Discussion/Significance of Impact: Our results indicate that automated EMR extraction and NLP can be used to develop a thyroid cancer dataset that is highly detailed, accurate, and efficient to collect. After optimizing our system of data extraction, we will replicate this process in a multi-center study with the eventual goal to expand these algorithms across rare pediatric cancers.

Objectives/Goals: Nearly 1 in 5 children with a suicide-related emergency department (ED) visit will have a suicide attempt within 18 months. Our objective was to determine demographic and clinical characteristics associated with time to ED revisit with self-injurious thoughts and behaviors (SITB) within 6 months. Methods/Study Population: We performed a retrospective cohort study of children aged 6 to 17 years with a primary mental health ED visit at 38 US children’s hospitals from January 2021 to December 2023. Data were obtained from the Pediatric Health Information System administrative database. We included the first index mental health ED visit for each child. Primary outcome was time to first ED revisit with a SITB diagnosis within 6 months of a patient’s index mental health ED visit, defined by ICD-10-CM diagnosis codes. We evaluated demographic and clinical characteristics associated with time to SITB revisit using a multivariable Cox Proportional Hazards Regression model. Results/Anticipated Results: We included 201,214 unique children, 8.7% had an ED revisit with SITB within 6 months. Among children with an SITB revisit, 74.6% had SITB diagnosed at the index visit. Multiple demographic characteristics were associated with longer time to revisit including male sex (HR 0.76, 95% CI 0.74-0.79) compared to female, private insurance (HR 0.83, 95% CI 0.80-0.86) compared to public, and Hispanic race and ethnicity (HR 0.81, 95% CI 0.77-0.85) compared to non-Hispanic White. Clinical characteristics associated with a shorter time to SITB revisit included index visit diagnoses for intentional self-harm (HR 1.32, 95% CI 1.27-1.38), suicidal ideation (Hazard Ratio (HR) 1.65, 95% CI 1.59-1.71), and mood disorder (HR 1.38, 95% CI 1.33-1.44). Discussion/Significance of Impact: Three in four children with a SITB ED revisit had SITB diagnosed at the index visit. Children with index visit suicidal ideation were at highest risk of SITB revisit. These findings highlight missed opportunities to identify and address pediatric SITB at the index ED visit and to prevent ED revisits.

People with disabilities in the US are now a health disparities population. Though 25% of US adults have a disability, only 5% of medical research grants are disability related. Knowledge about researchers’ perceived barriers to including people with disabilities in research has focused on a single disability/condition and thus has limited translational science applications. Our CTSA’s Disability as Difference: Reducing Researcher Roadblocks (D2/R3) project examined such roadblocks towards inclusion of people with intellectual and developmental disabilities (I/DD). I/DDs are broad, heterogeneous conditions that originate in childhood, have varying impact and function, and persist throughout the lifespan. Strategies that mitigate their under-representation in research will likely have general applicability to all disabilities. In D2/R3’s first phase we conducted semi-structured interviews with translational science and I/DD program leaders at ten US institutions about perceived barriers and facilitators to including people with I/DD in research. Interviews were held with 25 individuals from partnering Intellectual and Developmental Disabilities Research Centers, University Centers for Excellence in Developmental Disabilities, and Clinical and Translational Science Award programs. Collaborative thematic coding identified key themes as: attitudinal barriers (e.g., assumptions about consent capacity), logistical barriers (e.g., accommodation costs), health disparities, and generalizability concerns. Findings informed development of a survey based on Prosci’s ADKAR® model of change management’s five components: Awareness, Desire, Knowledge, Ability and Reinforcement. Exclusion appears to stem from researchers’ lack of awareness, misconceptions, and knowledge gaps rather than insurmountable obstacles.

Objectives/Goals: The study’s objective was to use real-world data to compare machine learning approaches for predicting colorectal cancer (CRC) screening status: (1) a Random Forest model optimized for overall accuracy and (2) Feasible Solution Algorithm (FSA) models focused on interpretability and identifying subgroups at risk of being unscreened. Methods/Study Population: The data included 22,549 adults aged 45–75 who visited a UK King’s Daughters Medical Center (UKKD) primary care site between 12/1/2023–11/30/2024, across five Appalachian counties. Patients with total colectomy, colon cancer, advanced illness, hospice care, dementia, and/or frailty were excluded. After complete-case analysis, 18,533 records remained and were split 80/20 for training/testing. Two machine learning methods, random forests and FSA, were applied to identify key predictors of CRC screening status. Within the FSA framework, logistic regression models were fitted using the training data, and model performance metrics were subsequently evaluated using the testing dataset. Results/Anticipated Results: Overall (n=22,549), 65.6% of patients completed CRC screening. The mean age was 59 years (SD = 8.2); most were White (97%), 40% had Medicare insurance, and 68% had multiple non-primary care provider (PCP) visits. The Random Forest model demonstrated the highest overall accuracy of 69% and had notably high sensitivity, correctly identifying approximately 9 of 10 screened patients. On the other hand, the FSA model offered greater specificity (65%) and interpretability, with the 2-predictor model identifying smokers and those with 0 non-PCP visits as the vulnerable subgroups who are least likely to be screened. Discussion/Significance of Impact: FSA identified smoking status, insurance type, number of non-PCP clinic visits, and age as key factors relating to CRC screening behavior. These findings can help clinicians and public health teams identify unscreened patients, guide targeted outreach, and advance translational strategies to improve population health.

Objectives/Goals: The need for accessible medical information has never been greater. Existing research and clinical documents are often too complex for the public to understand. This study aimed to evaluate the use of AI algorithms to convert research and clinical materials into language that is more easily understood while maintaining accuracy and informativeness. Methods/Study Population: We abstracted 21 half-page texts from informed consents, clinical documents, abstracts, and health-related websites. Each original text was then rewritten using 3 AI algorithms (resulting in 63 new, rewritten texts). Participants were given a set of 3 randomly selected surveys. Each survey contained the original text and an AI-rewritten text. Participants were then asked to answer 5 questions using a Likert scale 1-5 about the readability, clarity, and content of the two texts, their overall score of the new text, and which text they preferred. The study was IRB-approved. Participants were given a $10 gift card. Results/Anticipated Results: 68 participants (69.6% non-white, 65.2% female, mean age 58.3) completed the study task. 3 AI Algorithms were tested: * ConBART [Cripwell 2023]: context-aware method for text simplification * UL+Decoder [Flores 2023]: Flesh-Kincaid readability-driven method for text simplification * UBclear [House-made]: Iteratively improves readability with self-correction and retrieval-augmented generation. ConBART and UL+Decoder scored similarly across readability, clarity, and detail-fidelity metrics, but were preferred over original texts less than 50% of the time. UBclear scored higher in readability, clarity, detail, and overall score. Importantly, UBclear text was preferred over the original text 80% of the time, showing stronger alignment with human judgments of clarity, readability, and fidelity. Discussion/Significance of Impact: Our pilot study shows that AI can simplify existing research and medical care documents, potentially contributing to inclusivity and transparency in research and clinical care. “UBclear” was superior to the other 2 AI methodologies in readability, accessibility, and clarity while also being preferred over original texts and competing AI versions.

Low enrollment of racial/ethnic minorities in clinical trials is a persistent problem. This study explores community health workers’ (CHWs) potential to increase research participation by Black people. We interviewed 12 CHWs and 12 Black community members, and after multidisciplinary analysis, held a CHW focus group to refine themes and make recommendations. Most participants mistrusted research, but many valued its potential for generativity. CHW involvement in research was seen as an opportunity to bring community relationships and context to all aspects of research, including recruitment. Participants proposed steps to build trustworthy research experiences and develop CHWs as full research team members.

Objectives/Goals: Blood pressure (BP) control remains suboptimal among US patients with hypertension. Single-pill combination (SPC) therapies are commonly used to improve adherence; however, their effectiveness for achieving early and sustained intensive BP control is unclear. Methods/Study Population: We performed a post hoc analysis of SPRINT including 2,736 participants propensity matched in 1:2 ratio to compare effects of SPCs with equivalent multi-pill therapy. The estimated marginal odds of achieving optimal BP control were derived using generalized linear mixed models with repeated measures (LMMRM). The association between time-updated SPC use and BP change in short- (≤6 months) and long-term (>6 months) follow-up was assessed with LMMRM and SPC*time interaction term. Multivariable Cox models evaluated association of SPC use with CV events and serious adverse events (SAEs). Results/Anticipated Results: Among SPRINT participants (N=8623), 9.3% (N=803) were prescribed SPC at baseline with greater use in the intensive vs. usual care group (5.79 vs. 3.90 per 100 person-months; p-diff<0.001). Among matched pairs (SPC[n=912); multi-pill therapy[n=1824]), SPC use was associated with 22% increased likelihood of achieving target BP by 6 months [OR(95% CI): 1.22(1.05, 1.42)]. Participants receiving SPCs (vs multi-pills) experienced more rapid BP reduction in the first 6 months (-2.0 vs. -1.2 mmHg monthly change; p-diff<0.001). Over long-term follow-up, participants using SPCs achieved significantly lower SBP at each timepoint. The risk of the primary CV composite endpoint and SAEs was not significantly different between groups. Discussion/Significance of Impact: SPC therapy resulted in more rapid and sustained BP reduction and a greater likelihood of achieving BP control compared with multi-pill therapy without increase in SAEs. Future research is needed to identify optimal strategies for implementing SPC-based approaches at the population-level and optimize public health benefits of intensive BP control.

Objectives/Goals: We evaluated the associations of urine uranium with incident chronic kidney disease (estimated glomerular filtration rate <60 mL/min/1.73m2), incident albuminuria (albumin-to-creatinine ratio >30 mg/g), and baseline and follow-up plasma metabolite levels in participants without diabetes across the Great Plains and Southwest. Methods/Study Population: We evaluated participants with available urine uranium estimates, eGFR, uACR, and other variables of interest. In incident case analyses, participants were free of CKD (N=1,845) and albuminuria (1,693) at baseline (2001-2003). We used generalized estimating equations, clustering on family identifier, to estimate risk ratios (RRs) of incident CKD and albuminuria per doubling of urine uranium adjusted for sex, age, eGFR or uACR, study center, educational level, smoking status, BMI, and arsenic exposure. Nontargeted metabolomic profiling was analyzed by gas chromatography-mass spectrometry. In metabolome-wide association analyses, we used adjusted linear mixed models to estimate the association of urine uranium (N=1,223) with relative changes in metabolite concentrations over time. Results/Anticipated Results: Overall, the median (IQR) age was 35.9 (23.7, 46.8) years and 60% participants were female. The number of incident CKD cases was 28 (mean follow-up 5.6 years), and the number of albuminuria cases was 140. The adjusted RR (95% CI) per doubling of urine uranium was 0.90 (0.71, 1.15) for incident CKD and 1.00 (0.92, 1.08) for incident albuminuria. In metabolome-wide analyses, urine uranium was associated with a relative change in 8 unique metabolites and 31 unknown metabolomic features (p-value <0.05), with 5 unique metabolites (threonic acid, N-acetylornithine, isocitric acid, cellobiose, and 6-deoxyglucose) and 7 unknown metabolomic features reaching false discovery rate significance (BH-adjusted p-value <0.05). Discussion/Significance of Impact: Urine uranium was not associated with incident CKD nor with albuminuria. We identified 5 unique metabolites associated with urine uranium. Our findings are relevant for uranium-related kidney disease in people without diabetes, but require additional confirmation due to potential reverse causality and lack of drinking water uranium exposure.

This commentary discusses research workforce programs designed to enhance the representation and engagement of Latinos in HIV research, highlighting key challenges and proposing actionable strategies for improvement. Mentorship education and cultural inclusivity are identified as the most salient issues because the former leads to stronger health-related outcomes and is linked to cognitive-and career-related factors while the latter offers the potential to directly dismantle structures of inequity. This commentary suggests recasting of Diversity, Equity, and Inclusion (DEI) initiatives from eligibility as Latino self-identification, to all individuals’ lived experiences and/or prior experience in service/research activities. Some issues and constructs (i.e., heterogeneity, perseverance, acculturation, cultural values), typically important for certain underrepresented minoritized groups in diversity workforce programs, are reinterpreted for their relevance to all potential participants. This commentary proposes a holistic approach to trainee eligibility, creating a more inclusive environment that respects both individuality and diversity, and, importantly, contributing to DEI does not require being a member of an underrepresented minoritized population group.

Objectives/Goals: Randomized evaluation of interruptive EHR alerts in improving service delivery or patient health outcomes is usually carried out within the EHR system and in routine hospital operations. However, this study setting leads to increased risk of contamination, where factors outside the original design affect estimation and testing of treatment effect. Methods/Study Population: We focused on the effect of contamination on the design and analysis of individually randomized controlled trials (iRCTs) for the study of the effectiveness of EHR alerts. More specifically, we looked at post-randomization contamination due to clinician-induced correlations. To account for this kind of contamination, we extended the iRCT design to include clinicians’ random effects under the linear mixed effects model (LMEM) framework. We then proceeded to derive sample size and power formulas under different LMEM correlation structures. Using extensive simulation studies, we evaluated the derived formulas and assessed the effect of using a naive two-sample t-test that ignores post-randomization contamination. Results/Anticipated Results: We observed that the level of contamination on sample size, power, and type I error due to clinician-induced correlations was driven by three parameters: variances of the clinicians’ random intercepts and slopes, and the correlation between them. These parameters reflect the way and extent to which clinicians influence participant-level outcomes. For scenarios with common correlation structures from the literature, a test of the treatment effect based on a naive two-sample t-test led to inflated type I error. Using the naive test in the presence of contamination increased the risk of incorrectly concluding that the treatment effect is significant. We did observe valid naive tests with well controlled type I error but under extreme and unusual values of inter-cluster correlations. Discussion/Significance of Impact: Using iRCT as the baseline design for the interruptive EHR alert trials, a naive two-sample t-test may appear reasonable. However, our analytical and simulation findings indicate that a careful consideration of clinicians’ impact on patient-level outcomes in routine hospital operations is necessary for valid hypothesis tests and inference.

Goals: Asthma is a common condition affecting over 25 million Americans. Despite high prevalence, asthma is often undertreated, resulting in significant preventable morbidity and mortality. Our goal was to identify and characterize individuals with undertreated asthma to inform tailored interventions designed to improve care access and outcomes. Methods/Study Population: We used electronic health records (EHR) to identify patients with suspected undertreated asthma receiving care in a large, multi-site state-wide healthcare system serving >2 million people. Undertreated asthma was defined by 1) history of emergency department visit or hospitalization for asthma exacerbation or asthma-related symptoms and 2) no prior history of treatment with inhaled corticosteroids, the standard of care preventative treatment for patients with asthma. Asthma symptoms were defined as wheezing, cough, and dyspnea. Patients were required to have been treated with systemic corticosteroids and inhaled beta agonists (standard treatment for asthma exacerbation) and have history of eosinophilia to increase the specificity of asthma diagnosis. Results/Anticipated Results: We identified 3,318 people with suspected undertreated asthma evaluated between 2019 and 2024. These patients had high rates of healthcare utilization with a mean of 7.3 any-cause emergency department visits and 1.4 hospitalizations over the 5-year study period. Black and Hispanic patients were less likely to have a documented primary care physician or prior pulmonologist referral order when compared to white and non-Hispanic patients, respectively. Insurance coverage rates did not differ significantly between these racial/ethnic groups. Discussion/Significance of Impact: Individuals identified as having suspected undertreated asthma were found to have high healthcare utilization. Significant racial and ethnic disparities were observed, with associated gaps in primary and specialty care not explained by insurance status, highlighting potential opportunities for improving care and addressing disparities.

Cancer clinical research networks (CRNs) play a vital role in medical research globally by generating investigator-initiated research, pooling expertise, and enabling recruitment across multiple sites. Completing clinical trials is challenging. Delays can slow the generation of evidence needed to refine the best patient treatments. The aim of this review was to identify factors that have been either proposed or shown by research to influence the performance of cancer CRNs to improve trial success, outcomes and impact. A scoping review was conducted using a systematic search across five databases [PROSPERO CRD42023414241]. Records were screened for eligibility. For included articles, data on factors and research methods were extracted independently by up to three reviewers, and disagreements resolved by discussion. 1928 articles were returned, 13 were included. Articles reported on 11 membership-based cancer CRNs with headquarters in four countries (eight in the USA). Factors influencing CRN performance broadly fell into six categories: site, CRN, patient, regulatory, policy and industry factors, with subcategories in each case. These findings may help to inform future research to prioritize and improve the day-to-day performance of membership-based cancer CRNs and other trial sponsors to optimize clinical trial success. Further research is warranted.

Objectives/Goals: Prenatal exposure to phthalates, parabens, and phenols (EDCs) may be risk factors for adverse birth outcomes, but the biological mechanisms driving these relationships are not well understood. This study aims to explore the associations between prenatal EDC exposure – individually and as a mixture – and the neonatal metabolome. Methods/Study Population: This study included 180 pregnant people from a Columbia Center for Children’s Environmental Health cohort in New York City (1998-2002). Eleven phthalates, 1 phthalate replacement, 5 phenols, and 3 parabens were quantified in umbilical cord plasma samples using gas chromatography-tandem mass spectrometry. Metabolomic features in cord plasma were characterized via liquid chromatography coupled with high-resolution mass spectrometry. Metabolome-wide association studies using linear regression and quantile g-computation were used to identify features associated with exposure to the individual and mixture of EDCs, respectively. Correlated metabolites were grouped into modules using weighted gene co-expression network analysis (WGCNA) to identify associations with EDC exposure at a systemic resolution. Results/Anticipated Results: We observed metabolome-wide associations between 18 individual EDCs and 64 unique features in the umbilical cord plasma (p-value <0.05). For example, 9 different phthalates were significantly negatively associated with methionine, creatinine, and 2-methylcitrate. The overall EDC mixture was negatively associated with 2-methylcitrate, arginine, and creatinine (p-value <0.05). In the WGCNA analyses, 13 individual EDCs and the overall EDC mixture were significantly associated with at least 1 of the modules. Pathway enrichment analyses revealed that these metabolites were related to energy, amino acid, and lipid metabolism. Discussion/Significance of Impact: Prenatal EDC exposure, individually and as a mixture, is associated with levels of metabolites measured in umbilical cord plasma at delivery. The identified EDC-responsive metabolites and associated pathways offer insight into the biological processes that could underpin the relationship between prenatal EDC exposure and adverse birth outcomes.

Objectives/Goals: The human coronavirus OC43 is a Betacoronavirus, like SARS-CoV-2, but causes limited disease. Current clinical OC43 strains replicate in airway epithelial cultures while the lab-adapted strains only replicate in immortalized cells. We will identify the host and viral factors causing this difference. Methods/Study Population: To observe the effects of specific mutations present in clinical isolates of OC43, we will construct infectious clones of clinical and lab-adapted strains. Through targeted mutagenesis, we will characterize structural differences present in the Spike protein of clinical strains and identify the tropism-determining genome regions. By manipulating expression levels of enzymes involved in sialic acid production including SIAE and CasD1 and using a carbohydrate microarray to further evaluate carbohydrate binding, we will evaluate whether clinical OC43 isolates use the same 9-O-acetylated sialic acid identified as a receptor for the lab-adapted OC43 strain (VR-1558). Results/Anticipated Results: In primary human lung epithelial cell cultures, clinical strains of OC43 persistently replicate through 30 days post-infection. In contrast, the lab-adapted strain did not replicate. Growth on standard immortalized cells demonstrated the reverse phenotype with extensive replication of VR-1558 but no replication by the clinical isolates. Using AlphaFold to predict the 3D structure of Spike from a clinical isolate of OC43, we identified differences at a region previously identified as the binding site for its 9-O-acetylated sialic acid receptor. Further testing will determine whether these structural differences in Spike are responsible for the observed differences in viral tropism. Discussion/Significance of Impact: This project will deepen knowledge of coronavirus biology and the drivers of respiratory cell tropism. It will also develop novel tools for the study of OC43 and particularly of clinically relevant circulating strains which are better models for developing broad-spectrum therapies.