ABSTRACT IMPACT: This work will be used to improve the design of engineered dermal replacements that can be used to treat difficult-to-heal wounds such as burns or ulcers. OBJECTIVES/GOALS: Wounds of the skin are among the most common and costly medical problems experienced. Engineered dermal replacements have been developed to improve outcomes, but the optimal design features are unknown. Here we describe a hypothesis-driven study of scaffold parameters using a computational model of wound healing to simulate a variety of treatments. METHODS/STUDY POPULATION: The computational model, which was informed by animal data, was used to simulate cell, cytokine, and collagen density fields. There are reciprocal mechanobiological interactions between the cells and collagen that guide the wound healing process. We analyzed initial wound properties such as scaffold stiffness, microstructure, degradation, and wound geometry by running a one-at-a-time order-of-magnitude parameter change. We then conducted a derivative-based local sensitivity analysis for simulated experimental conditions and constructed a surrogate model of wound contraction using Gaussian process regression. RESULTS/ANTICIPATED RESULTS: We conducted finite element model simulations of scaffolds that varied in physical properties. A sensitivity analysis demonstrated that wound contraction was highly sensitive to collagen fiber stiffness and density. Wound contraction rate was also dependent on initial wound size and surface area. Collagen fiber orientation in the scaffolds affected contraction directionality and the orientation of the final wound area. A Gaussian process regression model was fit to the simulation results for use in rapid prototyping of scaffolds for design optimization. The Gaussian process model was able to reproduce the wound contracture for training and test cases. DISCUSSION/SIGNIFICANCE OF FINDINGS: This work further analyzes a computational model of wounds treated with collagen scaffold dermal replacements. The hypothesis driven analysis of the model suggested several key design features of scaffolds. The model surrogate will further be used for the purposes of prediction and optimization of tissue regeneration outcomes.

ABSTRACT IMPACT: A one-stage Bayesian multilevel model for meta-analysis integrating different survival data is introduced to complete the information synthesis without assuming proportional hazard. OBJECTIVES/GOALS: To develop a general modeling approach to perform efficient and robust meta-analyses using aggregated data (AD) for survival type endpoint and apply to a meta-analysis to examine the association between measurable residual disease (MRD) and disease-free survival (DFS) and overall survival (OS) in patients with acute myeloid leukemia (AML). METHODS/STUDY POPULATION: A Bayesian semi-parametric hierarchical model with a time-varying HR effect was presented. Three common types of survival information, including reconstructed survival data, the hazard ratio (HR) estimates with corresponding CIs and survival rates at specific time points, are synthesized such that all literature from the systematic review can contribute properly to the estimation and uncertainty quantification of the model parameters. The time-varying effects was modeled by assuming piecewise hazard risk and piecewise constant hazard ratio. The heterogeneity across studies was expressed by an additive random study effect and a random treatment-by-study interaction. The method was applied to a systematic review of 81 publications reporting on 11,151 AML patients. RESULTS/ANTICIPATED RESULTS: In simulation studies that the proportional hazard assumption is either valid or violated, the proposed method was efficient to achieve comparable performance to IPD meta-analysis, a gold standard approach, in estimating the survival rates, the restricted mean survival time at specific time points and median survival time with the point estimates close to the true values. When HR is not proportional over time, the proposed method was robust in estimating HR and significantly outperformed the classical random-effects meta-analysis. In the application to AML study, the average HR for achieving MRD negativity was 0.36 (95% CrI, 0.33-0.39) for OS and 0.37 (95% CrI, 0.34-0.40) for DFS. The association of MRD negativity with OS and DFS was significant and consistent across all subgroups. DISCUSSION/SIGNIFICANCE OF FINDINGS: The proposed novel method provided a flexible framework for meta-analysis of survival data, to accommodate various types of survival data in one model without assuming proportional hazards assumption. The findings of AML meta-analysis suggest that achievement of MRD negativity is associated with superior DFS and OS in patients with AML.

ABSTRACT IMPACT: This work will discuss informatics methods enabling the use of exposure health data in translational research. OBJECTIVES/GOALS: 1. Characterize gaps and formal informatics methods and approaches for enabling use of exposure health in translational research. 2. Education of informatics methods enabling use of exposure health data in translational research. METHODS/STUDY POPULATION: We performed a scoping review of selected literature from PubMed and Scopus. In addition we reviewed literature and documentation of projects using exposure health data in translation research. RESULTS/ANTICIPATED RESULTS: Primary challenges to use of exposure health data in translational research include: (1) Generation of comprehensive spatio-temporal records of exposures, (2) Integration of exposure data with other types of biomedical data, and (3) Uncertainties associated with using data as exact quantifications of exposure which are dependent on both - the proximity of measurement to subject under consideration and the capabilities of measuring devices. We identified 9 major informatics methods that enable incorporation and use of exposure health data in translational research. While there are existing and ongoing efforts in developing informatics methods for ease of incorporating exposure health in translational research, there is a need to further develop formal informatics methods and approaches. DISCUSSION/SIGNIFICANCE OF FINDINGS: Depending on the source about 50 - 75% of our health can be quantified to be a contribution of our environment and lifestyles. In this presentation, we summarize the studies and literature we identified and discuss our key findings and gaps in informatics methods and conclude by discussing how we are covering these topics in an informatics courses.

ABSTRACT IMPACT: Improve healthcare of patients with Cystic Fibrosis by reducing the time needed to generate results. OBJECTIVES/GOALS: We developed an automated framework capable of segmenting the lungs, extract the airways, and create a skeletonize map of the airways from CT scans of Cystic Fibrosis patients. As future expansion, the framework will be expanded to measure the airways diameters, detect the abnormal airways, and count the number of visible airways generations. METHODS/STUDY POPULATION: For this study, 35 CT scans from CF patients with different levels of severity were used to test the developed framework. The lungs segmentation was performed using an algorithm based on Gaussian Mixture Models for mild cases, and for severe cases a technique that uses convex hull and the recurrent addition of ‘dots’ was implemented. The airways extraction was performed using a 26-points connected components algorithm in conjunction with a curve fitting technique over the histogram of voxel values. Medial axis transform was used to perform the skeletonization of the extracted airways, and airways diameters determined via ray-casting. RESULTS/ANTICIPATED RESULTS: The framework was able to correctly obtain the segmented lungs in all 35 sample volumes regardless of disease severity. In contrast, it tends to fail to skeletonize the airways for severe cases where the framework is unable to differentiate between abnormal lungs conditions and dilated airways. Fine tuning is required to achieve better results. The expected result of the future implemented sections of the framework are focused to characterize the extracted airways by: 1) measuring the airways diameters; 2) detect and count the number of abnormal airways sizes; and 3) count the number of visible airways branching which will permit determination of stage and grade of the lungs of CF patients. DISCUSSION/SIGNIFICANCE OF FINDINGS: The proposed framework allows a fast and reproducible way to segment the lungs and create a skeletonized map of the airways that are independent of clinical training. In addition, this framework will be extended to obtain measurements of airway dilation and branching level, which could provide a deeper insight of the airways in CF patients.

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.

ABSTRACT IMPACT: We explored the use of machine learning to explore how multi-pollutant air quality is related to type 2 diabetes, which is more representative than the single pollutant models often employed to assess this relationship. OBJECTIVES/GOALS: Single pollutant air pollution models have correlated air pollution components with type 2 diabetes mellitus (DM). However, air pollution is a complex mixture, therefore, we explored the relationship between multi-pollutant air quality and DM incidence using machine learning. METHODS/STUDY POPULATION: Annual diabetes incidence from the CDC for each US county was downloaded for the years 2007-2016. Daily air pollution concentrations for PM2.5, PM10, CO, SO2, NO2, and O3 were downloaded from the US EPA for the years 2006-2015. K-means clustering, an unsupervised machine learning method, was employed to partition all air pollution components, for each day and county monitored, into the optimal number of clusters. Change in DM incidence was matched to air pollution clusters by county, lagged by one year. Additionally, NASA satellite-derived air pollution data will be compared to EPA data to inspect as a potential source for future clustering analysis of counties that do not have an EPA monitor. RESULTS/ANTICIPATED RESULTS: The largest increase of annual DM incidence was associated with the cluster having the highest average PM10, PM2.5, and CO, and the second greatest average NO2 concentrations. Inversely, the most significant decrease of annual DM incidence was associated with the cluster having the lowest PM10, PM2.5, and CO. While average PM10, PM2.5, SO2, NO2, and CO showed a rising tendency with elevating change of DM incidence, ozone did not show any such trend. It is anticipated that the NASA satellite-derived air pollution data will approximate the EPA air quality data and will be usable in assessing the air pollution-DM relationship for areas currently not monitored by the EPA. DISCUSSION/SIGNIFICANCE OF FINDINGS: Using an unsupervised k-means algorithm, we showed multiple ambient air components were related to increased incidence of T2DM even when average concentrations were below the National Ambient Air Quality Standards. This work could help guide policy making regarding air quality standards in the future.

ABSTRACT IMPACT: Measuring and analyzing qualitative and quantitative traits using phenomics approaches will yield previously unrecognized heart failure subphenotypes and has the potential to improve our knowledge of heart failure pathophysiology, identify novel biomarkers of disease, and guide the development of targeted therapeutics for heart failure. OBJECTIVES/GOALS: Current classification schemes fail to capture the broader pathophysiologic heterogeneity in heart failure. Phenomics offers a newer unbiased approach to identify subtypes of complex disease syndromes, like heart failure. The goal of this research is to use data-driven associations to redefine the classification of the heart failure syndrome. METHODS/STUDY POPULATION: We will identify < 10 subphenotypes of patients with heart failure using unsupervised machine learning approaches for dense multidimensional quantitative (i.e. demographics, comorbid conditions, physiologic measurements, clinical laboratory, imaging, and medication variables; disease diagnosis, procedure, and billing codes) and qualitative data extracted from an integrated health system electronic health record. The heart failure subphenotypes we identify from the integrated health system electronic health record will be replicated in other heart failure population datasets using unsupervised learning approaches. We will explore the potential to establish associations between identified subphenotypes and clinical outcomes (e.g. all-cause mortality, cardiovascular mortality). RESULTS/ANTICIPATED RESULTS: We expect to identify < 10 mutually exclusive phenogroups of patients with heart failure that have differential risk profiles and clinical trajectories. DISCUSSION/SIGNIFICANCE OF FINDINGS: We will attempt to derive and validate a data-driven unbiased approach to the categorization of novel phenogroups in heart failure. This has the potential to improve our knowledge of heart failure pathophysiology, identify novel biomarkers of disease, and guide the development of targeted therapeutics for heart failure.

ABSTRACT IMPACT: This is the first study to use QUINT analyses to examine heterogeneity of treatment effect for group medical visits among individuals with type 2 diabetes. QUINT is a data driven method that assumes no a priori assumptions regarding effect moderators - an important step in the path towards personalized medicine. OBJECTIVES/GOALS: To examine heterogeneity of treatment effect (HTE) in Jump Start, a trial that compared the effectiveness of group medical visits (GMVs) focused on medication management only versus the addition of intensive weight management (WM) on glycemic control for patients with type 2 diabetes and body mass index >=27kg/m^2. METHODS/STUDY POPULATION: Jump Start patients (n=263) were randomized to a GMV-based medication management plus low carbohydrate diet-focused WM program (WM/GMV; n = 127) or GMV-based medication management only (GMV; n = 136) for diabetes control. We used QUalitative INteraction Trees (QUINT), a tree-based clustering method, to determine if there were subgroups of patients who derived greater benefit from either WM/GMV or GMV. Subgroup predictors included 32 baseline demographic, clinical, and psychosocial factors. Outcome was hemoglobin A1c (HbA1c). We conducted internal validation via bootstrap resampling to estimate bias in the range of mean outcome differences among arms. RESULTS/ANTICIPATED RESULTS: QUINT analyses indicated that for patients who had not previously attempted weight loss, WM/GMV resulted in better glycemic control than GMV alone (mean difference in HbA1c improvement = 1.48%). For patients who had previously attempted weight loss and had lower cholesterol and blood urea nitrogen levels, GMV alone was better than WM/GMV (mean difference in HbA1c improvement = 1.51%). Internal validation resulted in moderate corrections in the mean HbA1c differences between arms; however, differences remained in the clinically significant range. DISCUSSION/SIGNIFICANCE OF FINDINGS: Among patients with diabetes and BMI>=27kg/m^2, a low-carbohydrate, weight loss focus may better improve HbA1c in those who have never attempted weight loss. A medication management focus may be better in those who have attempted weight loss and have lower cholesterol and blood urea nitrogen.

ABSTRACT IMPACT: Our findings could potentially identify CVD at-risk persons living with HIV who might benefit from aggressive risk-reduction. OBJECTIVES/GOALS: PWH have higher rates of CVD than the general population yet CVD risk prediction models rely on traditional risk factors and fail to capture the heterogeneity of CVD risk in PWH. Here we identify protein biomarkers that are able to discriminate between CVD cases and controls in PWH, and we assess their added benefit beyond traditional risk factors. METHODS/STUDY POPULATION: We analyzed 459 baseline protein expression levels from five OLINK panels in a matched CVD (MI, coronary revascularization, stroke, CVD death) case-control study with 390 PWH from INSIGHT trials (131 cases, 259 controls). We formed 200 datasets via bootstrap. For each bootstrap set, a two-component partial least squares discriminant model (PLSDA) was fit. The importance of each variable in the discrimination of cases and controls in the PLSDA projection was assessed by the variable importance in projection (VIP) score. Proteins with average VIP scores > 1 were used in penalized logistic regression models with elastic net penalty, and proteins were ranked based on the number of times the protein had a nonzero coefficient. Proteins in the top 25th percentile were considered to have high discrimination. RESULTS/ANTICIPATED RESULTS: Participants had mean age 47 years, 13% were females, 4.9% had CVD at baseline and 69% were on ART at baseline. Eight proteins including the hepatocyte growth factor and interleukin-6 were identified as able to distinguish between CVD cases and controls within PWH. A protein score (PS) of the top-ranked proteins was developed using the bootstrap (for weights) and the entire data. The PS was found to be predictive of CVD independent of established CVD and HIV factors (Odds ratio: 2.17 CI: 1.58-2.99). A model with the PS and traditional risk factors had a 5.9% improvement in AUC over the baseline model (AUC=0.731 vs 0.69), which is an increase in model predictive power of 18%. Individuals with a PS above the median score were 3.1 (CI: 1.83- 5.41) times more likely to develop CVD than those with a protein score below the median score. DISCUSSION/SIGNIFICANCE OF FINDINGS: A protein score developed improved discrimination of PWH with CVD and those without, and helped identify PWH with high risk for developing CVD. If validated, this score and/or the individual proteins could be used in addition with established factors to identify CVD at-risk individuals who might benefit from aggressive risk-reduction.

ABSTRACT IMPACT: The described framework will enable other sites with a well-defined apparatus for enabling the secondary analysis of EHR data for research through education, team science, and resource consolidation. OBJECTIVES/GOALS: EHR’s potential to improve healthcare outcomes extends far beyond the clinic. This vast repository of clinical insights has dramatic potential for biomedical research. To enhance accessibility for busy clinicians and underserved populations, we describe a framework for interfacing with EHR locally and through national network participation. METHODS/STUDY POPULATION: The Institutional Development Award (IDeA) program, which began in 1993, broadens NIH funding’s geographic distribution for biomedical research. Included in this is the IDeA Networks for Clinical and Translational Research, which focuses on enhancing clinical and translational science across a network of IDeA-states with traditionally underserved communities and rural providers. A prior survey of the needs and capabilities of IDeA-CTR centers identified the need for improved research support. Based on our annual member survey we developed a process for supporting distributed research projects across the GP-CTR. NIH also recently made a funding announcement for the IDeA-CTR community identifying EHR research as a major priority in responding to the COVID-19 pandemic. RESULTS/ANTICIPATED RESULTS: Results from site interviews and member surveys show a clear need for dedicated resources to navigate the process of EHR-derived research. Most described a different set of requirements for increasing accessibility to EHR for research and a strong desire to participate in research networks. Local investigators cited a lack of tools, educational materials, and accessibility. Initial efforts demonstrate strong research questions but limited technical, statistical, and terminological capabilities to succeed. In response, a pipeline for team science and promotion of projects from local phenotypes to national studies. We created a facilitator training program to expand the number of facilitators (n=22), quarterly training for investigators (n=104), and ongoing efforts to advance COVID-19 research. DISCUSSION/SIGNIFICANCE OF FINDINGS: As evidenced in the expanding number of EHR-based research networks there is a need for a system to promote project development and best practices. The proposed model promotes education, resource sharing, and team formation to advance clinical questions from the idea stage toward national research network participation.

ABSTRACT IMPACT: Laying the groundwork for better predictive algorithms to inform clinical decisions and planning. OBJECTIVES/GOALS: Frailty scores predict poor patient outcomes. Validated against highly relevant outcomes, such scores can be used to inform clinical and resource utilization decisions. We generated and validated an electronic Frailty Index (EFI) from real-world EHR data using the Rockwood deficit-accumulation framework to predict patient safety events. METHODS/STUDY POPULATION: To assure that the research approach reflected perspectives of multiple stakeholders, our multidisciplinary group included an implementation scientist, a geriatrician, an internist, and an informatician. From our large academic health center, we accessed EHR data for 14,844 patients randomly sampled from the data warehouse underlying our ACT/SHRINE node. The per-visit EFI scores were calculated using EHR codes in a rolling 2-year time window. EFI was used as the predictor variable in the analytic design. The primary outcomes were preventable patient-safety events derived from ICD-10 codes including hospital-acquired infections, non-operative hospital-acquired trauma, and cardiac complications. Cox proportional hazard models were used to estimate risk for each outcome. RESULTS/ANTICIPATED RESULTS: We found statistically significant associations of EFI with clinically meaningful outcomes from EHR data. For most outcomes, we found significant correlation with EFI and c-statistics indicating good calibration of the models. The EFI was a strong predictor of clinically relevant outcomes without relying on any data other than diagnoses, vital signs, and laboratory results from the EHR. In contrast to previous studies, we treated EFI as a time-varying predictor with multiple follow-ups per patient, which is more realistic than relying on one static time-point. We used a representative sample of the adult patient population rather than limiting it to older individuals and found EFI to be a useful metric even at relatively young ages. DISCUSSION/SIGNIFICANCE OF FINDINGS: The EFI predicted safety events in adult patients using only routine, structured EHR data and can offer a low-effort, scalable method of risk assessment, valuable to clinical decisions. The capability to harness EHR data and rapidly generate clinical knowledge can be transformative for complex care and contributes to Learning Health Systems.

ABSTRACT IMPACT: This review will encourage further development of novel uses of REDCap for the benefit of the research community. OBJECTIVES/GOALS: REDCap is a clinical research data collection platform that is primarily used as intended. However, little is known about its more novel uses, specifically in clinical decision support in patient care and in clinical research management. Thus, the purpose of this review is to examine peer reviewed literature identifying and describing such novel uses. METHODS/STUDY POPULATION: A systematic search was conducted in both PubMed and Google Scholar using the equation ((REDCap) OR ('Research Electronic Data Capture')) AND ((Clinical Trial Management) OR (Clinical Research)).’ Articles were screened by title, then abstract, and then were reviewed in full if they met inclusion criteria. Articles were included if they had potential relevance to the topic of REDCap or if they mentioned activities related to fields of clinical and translational science including operational support in areas such as clinical research management. Articles were excluded if they focused on common clinical research activities relating to data collection software such as survey administration, database building or data collection for clinical trials, registries, and cohort studies. RESULTS/ANTICIPATED RESULTS: The initial search yielded 390 results, of which 40 underwent an abstract review; only 8 of these underwent full text review. Of these, 5 discussed uses of REDCap in the context of operational support in clinical research management; 3 were related to clinical decision support in patient care. For the 5 articles focused on operational support in clinical research management, topics include e-consenting procedures, collection and storage of protected health information (PHI), patient recruitment and tracking stakeholder engagement. The 3 articles about clinical decision support discuss REDCap tools for generating risk predictions for post-surgical clinical outcomes, generating recommendations and STI test orders, and increasing efficiency in hand-offs to enhance care of surgical oncology patients. DISCUSSION/SIGNIFICANCE OF FINDINGS: Considering that only a small percentage of peer reviewed research reports out on novel uses of REDCap, there is a need for the REDCap consortium to do further work to fulfill its mission to adopt, innovate, and suggest novel uses of REDCap, thus expanding the understanding of its functionalities and therefore its utility in the research community.

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.

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.

ABSTRACT IMPACT: By outlining telehealth access disparities in the vision-impaired population, this scoping review has identified a set of effective and clinically appropriate implementation strategies and interventions for improving the technical, provider-level, and system-level accessibility of telehealth for vision-impaired patients. OBJECTIVES/GOALS: Evidence-based recommendations that ensure telehealth access for vision-impaired patients are critical to reducing health disparities. This review identifies, evaluates, and proposes strategies for public and private sector stakeholders to increase telehealth access for vision-impaired patients during the pandemic and beyond. METHODS/STUDY POPULATION: This scoping review included five steps: 1) the implementation of an iterative search strategy using relevant keywords to query 4 electronic databases (PubMed, Cochrane, Google Scholar, and Europe PMC) for relevant articles, 2) the application of a set of inclusion criteria to filter database results for article evaluation, 3) a quality assessment of the articles retained, 4) the extraction and summary of data from each assessed article, and 5) a narrative synthesis of the qualitative literature reviewed. RESULTS/ANTICIPATED RESULTS: To date, 21 articles that fit the inclusion criteria, published between 2006 and 2020, have been identified. To ensure the most robust collection of existing literature is aggregated, the iterative search strategy and inclusion criteria sorting process will be underway until December 20. The assessment of articles, and extraction and summary of data contained within said articles, will be finalized on January 20. The narrative synthesis will be complete on February 1. The poster and abstract will be complete by February 20. DISCUSSION/SIGNIFICANCE OF FINDINGS: Future research should examine outcomes associated with the implementation of accessible telehealth programs to identify remaining barriers. To improve outcomes for vision-impaired patients, policymakers, providers, payers, and industry must collaborate to promote accessibility in telehealth design and implementation.

ABSTRACT IMPACT: This project will aid in the optimization of treatment for those with heart failure with a reduced ejection fraction in order to both maximize health benefits and minimize financial burdens. OBJECTIVES/GOALS: To evaluate the accuracy and clinical applicability of a novel web-based application programming interface in the optimization of care for patients being treated for heart failure with reduced ejection fraction (HFrEF). The purpose of this validation is to ensure the translatability of this algorithm to a clinical setting using real-world data. METHODS/STUDY POPULATION: This study is a retrospective analysis of a previously created algorithm designed to optimize therapy for patients currently diagnosed with HFrEF. Patients that are seen for HFrEF treatment at Michigan Medicine are enrolled in a heart failure registry and were included in this study. Exceptions include those with heart transplants, LVAD, and those undergoing treatment with chronic inotropes (milrinone/dobutamine). Clinically relevant information (demographics, vital statistics, labs, and medications including dose and frequency) was taken from their respective electronic health record (EHR) and this data was used as the input for the algorithm. The therapy recommendations were collected and manually compared to the 2017 ACC/AHA/HFSA guidelines to verify the accuracy of the algorithm outputs. RESULTS/ANTICIPATED RESULTS: Data is currently being collected and analyzed. At first glance, our algorithm has been successful at detecting patients that are good candidates for therapy optimization. Based on inputs given, the treatment recommendations have been appropriate when compared to the most up-to-date HF treatment guidelines. The algorithm has also correctly identified levels of urgency for therapeutic recommendations. Finally, we have also shown the algorithm to have effectiveness for identifying areas of inappropriately adjusted therapy. Preliminary results have led to changes to the functionality of the algorithm, including how medications are retrieved from the EHR’s and how medication doses are identified. Previous iterations created discrepancies in dosing and the algorithm has since been adjusted. DISCUSSION/SIGNIFICANCE OF FINDINGS: By verifying its validity, our algorithm can accurately flag patients with HFrEF that are eligible for therapy optimization and give providers the opportunity to make appropriate changes. Given the high health and financial burdens of HFrEF, our algorithm has the ability to provide significant morbidity, mortality, and financial benefits.

ABSTRACT IMPACT: Mobile app may help improve the depression symptoms among underserved patients OBJECTIVES/GOALS: Depression is one of most common mental health conditions and the leading cause of disability worldwide, affecting about one in 10 adults in the US. The aim of this study was to explore the factors that affect feasibility of incorporating mobile app self-management tools for depression in integrated primary care settings. METHODS/STUDY POPULATION: This was a cross-sectional questionnaire study of depressed patients at two primary care clinics in a Midwest academic medical center. Adult patients (≥19 years) who had an active or previous diagnosis of depression were included in the study. A self-administered survey collected information pertaining to demographics, smartphone ownership, data plan type, smartphone application usage, mobile app self-management interest, health literacy, and patient activation. Chi-square analysis was conducted to compare the patient demographic characteristics, the smartphone ownership, phone plan, smartphone use for health information between two clinics. Multinominal logistic regression analysis was conducted to examine the association between the patient activation and patient characteristics. RESULTS/ANTICIPATED RESULTS: Over 80% of patients owned a smartphone, 80.5% were willing to use data for depression management, and 68.9% believe an app can help in depression management. A higher literacy level was significantly associated with higher level of patient activation (Chi-square=8.5453; p=0.0360). These results suggest that planning interventions that use mobile apps within this patient population is likely feasible and the intended underserved patients at these clinics have an interest in using depression related apps which is similar to findings found by other studies exploring app interest. DISCUSSION/SIGNIFICANCE OF FINDINGS: Understanding patient activation levels within a given population can help to shape corresponding needs. The use of depression related self-management mobile apps will likely require the development of educational materials to facilitate patient use and engagement which means understanding the literacy needs of this population as well.

ABSTRACT IMPACT: The perspectives and guidance from adolescents and young adults (AYA) reported in this study could inform the evidence-based development and delivery of mobile health (mHealth) interventions to improve the health of AYA with chronic diseases. OBJECTIVES/GOALS: To elicit advice from AYA with chronic healthcare needs regarding if and how mHealth interventions could effectively promote illness self-management skills. We selected this goal because including the user perspective from the beginning of the design process could lead to greater future adoption. METHODS/STUDY POPULATION: We purposively recruited AYA patients from a pediatric hospital with heterogeneous chronic illnesses to identify universal chronic disease views rather than condition-specific perspectives. We conducted qualitative face-to-face semi-structured interviews with (N = 19) AYA between 16 and 20 years old (63.2% Latinx; 21.1% Black; 10.5% White; 5.3% Multiracial). Using ATLAS.ti, three coders completed thematic analysis to inform a conceptual framework on how AYA believe mHealth interventions could promote the development and maintenance of self-management skills. Member checking was conducted over the phone to obtain participant feedback on themes to enhance the validity of qualitative results. RESULTS/ANTICIPATED RESULTS: Results suggest that AYA develop self-management skills through several strategies, including 1) getting organized, 2) ‘making it work for me’ and 3) keeping the ‘right’ mentality. AYA described developing these strategies through: 1) receiving social support, 2) accessing helpful tools and technologies, and 3) going through a maturation process. They provided recommendations for how mHealth interventions could improve this process, including: 1) ‘what’ recommendations, describing the content or active ingredients that should be included in mHealth interventions, and 2) ‘how’ recommendations, describing the technological aspects or style in which the interventions should be delivered. DISCUSSION/SIGNIFICANCE OF FINDINGS: The results suggest that an appealing mHealth intervention could increase the support for AYA patients to proactively acquire self-management skills, avoiding trial-and-error or uneven access to guidance. Improving self-management could prevent poor health outcomes and increase quality of life.

ABSTRACT IMPACT: Findings from this study have the potential to improve interventions geared toward YBW, by highlighting the potential for technology-based HIV interventions. OBJECTIVES/GOALS: A stark disparity in HIV exists for Black American women, with 61% of all new HIV infections among American women occurring in Black women. Using technology to address community-level HIV risk may be beneficial, however few studies have examined the association of tech-based communication and HIV prevention behaviors among Black women. METHODS/STUDY POPULATION: Egocentric social network data from 201 young Black women (YBW) were collected from June 2018 to December 2018 to identify how social media use (e.g., amount of time, type of social media used, health information seeking) and sexual health communication (e.g., talk about condom use via face-to-face, text or phone and talk about HIV testing via face-to-face, text or phone) were associated with condom use, HIV testing and interest in pre-exposure prophylactic (PrEP). Statistical analysis proceeded in two stages, descriptive statistics and multivariate logistic regression modeling. RESULTS/ANTICIPATED RESULTS: Instagram (82%) and Snapchat (79%) were the most used social media platforms for communication with SNMs. About 20% of YBW reported spent 4 or more hours on social media per day, and a majority of YBW spoke to at least one SNM via text (85%), face-to-face (98%), or on the phone (97%). Multivariate logistic regression results indicated that YBW who spoke to their SNMs via Instagram had 3.23 times the odds of using condom during last sex, however if they spoke to SNMs on twitter or spent more then 4 hours on social media they had a decrease in odds of using condoms. YBW had 96% decreased odds of ever being tested for HIV if they spoke to a SNM face-to-face about condom use; and notably, YBW had 3 times the increased odds in interest of using PrEP if they had sex with someone they met online or if they sought sexual health information online. DISCUSSION/SIGNIFICANCE OF FINDINGS: By assessing the modes of communication YBW are using to speak with their SNMs and their associations with HIV prevention behaviors, we can better determine the most optimal, efficient, and effective ways of utilizing technology for HIV intervention.

ABSTRACT IMPACT: Optimizing the use of endotracheal aspirate cultures (EACs) has the potential to improve the care of complex mechanically ventilated children by improving testing practices and avoiding unnecessary antibiotic treatment for false-positive results. OBJECTIVES/GOALS: An electronic survey has previously been employed to characterize the practices and attitudes around blood cultures among critically ill children. The objective of this work was to develop and pilot a new survey as a tool to understand practices and attitudes that could inform quality improvement initiatives to optimize EAC practices. METHODS/STUDY POPULATION: Informed by prior experience of diagnostic stewardship of EAC in other settings and using a similar structure to the blood culture practice survey, we developed an electronic self-administered survey sent to respiratory therapists, advanced practice providers, and physicians at the Johns Hopkins All Children’s pediatric intensive care unit. RESULTS/ANTICIPATED RESULTS: A total of 27 of 87 clinicians (37%) responded to the survey (22 respiratory therapists, 9 attending physicians and 1 advanced practice provider). Responses indicated samples are typically collected by respiratory therapists via in-line (endotracheal) or open suctioning (tracheostomy). Most respondents did not feel EACs could lead to unintended negative consequences (71%), agreed practices vary between people (89%), and felt an algorithm would help align the clinical team (79%). Most respondents agreed some clinicians may be reluctant to change practice (82%) and may not change practice due to concern for missing diagnosis of ventilator-associated pneumonia or tracheitis (78%). Surveillance cultures were not used in this unit and there were no prior EAC diagnostic stewardship efforts. DISCUSSION/SIGNIFICANCE OF FINDINGS: This survey captured practices, perceptions and barriers to changes that will inform the implementation of quality improvement initiatives to optimize EAC use in this unit. Future studies can consider utilizing an electronic survey to describe practice variation, clinician believes and attitudes about EAC testing in ventilated patients.