OBJECTIVES/GOALS: Errors in patient matching could result in serious adverse safety events. Unlike publicized mix-ups by healthcare providers these errors are insidious and with increased data sharing, this is a growing concern in healthcare. The following project will examine patient matching errors and quantify their association with safety. METHODS/STUDY POPULATION: EHR systems perform matching out-of-the-box with unknown quality. Using matching processes outside the EMR, the rate at which matching errors are present was quantified and the erroneous records were flagged providing both comparative measures and data necessary to evaluate patient safety. To understand the relationship between matching and safety we will establish a percent of voluntarily reported safety events in our institution where a matching error existed during an encounter. Any safety events occurring for a flagged patient will be reviewed to determine if matching errors contributed to the safety problem. Not all safety events are reported so we will perform full chart review of a filtered list of medical records that have a higher likelihood of safety events. RESULTS/ANTICIPATED RESULTS: We were able to quantify matching errors, and the preliminary matching error rate is approximately 1%, representing over 700 patients. The work is in progress and we are beginning to determine the association between safety events and incorrect matching. Together these results will provide an incentive to identify errors, make corrections, and develop methods to achieve these objectives. The number of matching errors impacts patient care as well as business operations and is likely to have a negative financial impact on institutions with high error rates regardless of its relationship to safety. DISCUSSION/SIGNIFICANCE OF IMPACT: Patient matching is bundled with EHR software and institutions have little control over error rates, yet bear the liability for resulting clinical error. Institutions need to be able to identify undetected matching errors and any associated safety events and this project will provide that solution.

OBJECTIVES/GOALS: To facilitate the development of innovative injection products by providing translational researchers with a regulatory and manufacturing road map for producing small batch sterile products for Phase 1 research use. To leverage recent AMC investments in facility improvements and pharmacy training in the areas of sterile product production, testing, and environmental controls, that can be used to support production of phase 1 clinical trial supplies METHODS/STUDY POPULATION: Searching and organizing relevant data and information from web portals and databases in the following: areas: FDA, EMA, USP regulations, regulatory science, pharmaceutical formulation and analytics, supply vendors, analytical testing laboratories, and product testing laboratories. Present the information using a user friendly format including flow charts and development timelines, taking the perspective of the translational investigator. RESULTS/ANTICIPATED RESULTS:

• Choosing AMC resources vs outside consultants and vendors, leveraging local resources where possible

• Qualifying and monitoring suppliers, testing laboratories, in-house departments, and Contract Drug Manufacturing Organizations (CDMO)

• Bringing together the deliverables for the IND CMC section

• Where and how to leverage available products and science to simplify safe and reliable production

DISCUSSION/SIGNIFICANCE OF IMPACT: Use and utility of injectable drug products, both small molecule and biologics, is growing rapidly, and is projected to continue to escalate well into the next decade. This is due not only to advances in medicine, but also to improvements in AMC-based sterile product production, and a better understanding of small batch manufacturing methods. All three trends align in academic medical centers (AMC) and can be utilized by translational researchers, if they can understand the potential and regulatory requirements.

OBJECTIVES/GOALS: The development of early university technologies for commercialization is largely inefficient and exhibits a high rate of failure, often due to a lack of researcher time and commercialization experience. We have created the Translational Fellow role to address these needs and increase the throughput of university technology transfer. METHODS/STUDY POPULATION: Translational Fellows will first build their initial competencies to identify, evaluate, and develop new technologies through internships with intake organizations within the university ecosystem, including the Office of Technology Management, the LEAP gap-funding mechanism, and local venture capital firms. Following this training, Fellows will provide tailored support to validated projects by establishing development milestones, liaising with industry experts, navigating regulatory requirements, and drafting marketing materials such as executive summaries and financial projections. Lastly, Fellows will partner with a highly developed project to facilitate the commercialization of the technology, whether through a SBIR/STTR grant, direct licensing event, or startup creation. RESULTS/ANTICIPATED RESULTS: We anticipate that implementation of this mechanism will increase the proportion of university-generated inventions that undergo successful commercialization events, as well as increase the rate at which these projects develop after initial validation. Furthermore, we expect that the skills acquired through this program will allow Fellows to successfully transition to a variety of roles in the biotech space. We also expect that Fellows will be capable of training other scientific teams in the preparation of SBIR/STTR grants, further expanding opportunities for commercialization in the research space. DISCUSSION/SIGNIFICANCE OF IMPACT: Translational Fellows fill a unique interdisciplinary niche, allowing them to address common barriers faced by academic inventors. Improving commercialization throughput further capitalizes on the wealth of ideas generated in universities, thereby driving innovation in the biomedical space and directly contributing to improved human health. CONFLICT OF INTEREST DESCRIPTION: The authors have no conflicts of interest.

OBJECTIVES/GOALS:

1. 1. Examine the associations among BMI and markers of cardiometabolic risk, including blood pressure, lipids and blood glucose.

2. 2. Assess prevalence of kidney function deterioration, identified as hyperfiltration and moderately increased albuminuria (MIA), in obese compared to normal weight adolescents.

METHODS/STUDY POPULATION: De-identified electronic health records (EHR) data were extracted for female adolescents, aged 12-21 years, and their offspring through 24 months, who received health care services (Jan 2012 to Dec 2016) in NYC from 12 academic health centers and community health centers that are part of PCORnet NYC Clinical Data Research Network (NYC-CDRN). Data were analyzed using SAS (version 3.2.5). Patient characteristics overall and for study subgroups were examined using standard summary statistics. Trends in cardio-renal variables were examined by BMI groups coded according to NHANES as underweight, normal weight, overweight or obese. Multiple linear regression analyses will control for covariates. RESULTS/ANTICIPATED RESULTS: Data from 651,066 adolescent females ages 12-21 were retrieved. Analysis was performed on a subset of 202,214 unique patients (26% white, 15% black, 12.9% Latina) for whom there was complete data for BMI and blood pressure. Distribution of BMI was 6% underweight, 59% normal weight, 19% overweight, and 17% obese. There were significant differences in mean systolic (SBP, mean±SD mmHg: 102±12, 108±11, 112±12, 116±12) and diastolic blood pressure (DBP, mean±SD mmHg: 62±10, 66±8, 68±8.9, 70±9) across the four BMI groups with an increasing trend (p-values<0.0001). We will examine renal function trends, and whether these cardio-renal differences persist when controlling for age, race and ethnicity. DISCUSSION/SIGNIFICANCE OF IMPACT: Although SBP/DBP means were within normal limits across BMI groups, significant increasing trends suggest that women in higher BMI groups may be at increased risk for hypertension and potentially for renal dysfunction. We will examine contributions of race/ ethnicity and age to these associations.

OBJECTIVES/GOALS: To improve the implementation of FDRs in translation research. Current statistical packages are hard to use and fail to adequately convey strong assumptions. We developed a software package that allows the user to decide on assumptions and choose the hey desire. We encourage wider reporting of FDRs for observed findings. METHODS/STUDY POPULATION: We developed a user-friendly R function for computing FDRs from observed p-values. A variety of methods for FDR estimation and for FDR control are included so the user can select the approach most appropriate for their setting. Options include Efron’s Empirical Bayes FDR, Benjamini-Hochberg FDR control for multiple testing, Lindsey’s method for smoothing empirical distributions, estimation of the mixing proportion, and central matching. We illustrate the important difference between estimating the FDR for a particular finding and adjusting a hypothesis test to control the false discovery propensity. RESULTS/ANTICIPATED RESULTS: We performed a comparison of the capabilities of our new p.fdr function to the popular p.adjust function from the base stats-package. Specifically, we examined multiple examples of data coming from different unknown mixture distributions to highlight the null estimation methods p.fdr includes. The base package does not provide the optimal FDR usage nor sufficient estimation options. We also compared the step-up/step-down procedure used in adjusted p-value hypothesis test and discuss when this is inappropriate. The p.adjust function is not able to report raw-adjusted values and this will be shown in the graphical results. DISCUSSION/SIGNIFICANCE OF IMPACT: FDRs reveal the propensity for an observed result to be incorrect. FDRs should accompany observed results to help contextualize the relevance and potential impact of research findings. Our results show that previous methods are not sufficient rich or precise in their calculations. Our new package allows the user to be in control of the null estimation and step-up implementation when reporting FDRs.

OBJECTIVES/GOALS: Composition of demographics or image types in publicly available datasets may detract from deep learning (DL) diagnosis performance of underrepresented melanoma subtypes. We evaluate a DL model’s performance on melanoma subtypes (acral; head and neck) that have known association with poor prognosis. METHODS/STUDY POPULATION: We trained a CNN using a single InceptionV3 model for 30 epochs on dermoscopic images of pigmented lesions from the International Skin Imaging Collaboration (ISIC). The ISIC 2018 challenge training set had 10008 total images, with 1113 total nevi, 6705 total melanomas, 97 acral nevi, 10 acral melanomas, 256 head and neck (H&N) nevi, and 164 H&N melanomas. The non-acral test set had 117 melanomas and 200 nevi. The acral test set had 201 melanomas and 161 nevi. The H&N test set had 199 melanomas and 128 nevi. Area under the receiver operating curve (AUC) was calculated. The model was retrained with acral lesion oversampling (10x) and performance on the acral test set was re-evaluated. RESULTS/ANTICIPATED RESULTS: The model performed on the non-acral test with an AUC of 80.5%, on the acral test with an AUC of 76.3%, and on the head and neck test with an AUC of 83.8% After oversampling acral lesions within the training set, the model showed nearly the same performance as without oversampling on acral lesions: AUC of 75.6%. DISCUSSION/SIGNIFICANCE OF IMPACT: Diagnosis of high-risk melanoma subsets (acral; H&N) remains reliable despite underrepresentation during training, increasing validity for broad implementation of DL technology. Datasets for individual subtypes may not be warranted as findings suggest features may be learned from other skin lesions.

OBJECTIVES/GOALS: To evaluate whether the default mode network experiences age-related changes in functional connectivity and to identify these patterns of progression across seven decades of life. The overall goal is to evaluate whether quantifying these functional changes can serve as potential neurobiomarkers of aging for further quantitative genetic analyses. METHODS/STUDY POPULATION: Scans were performed at the RII on a 3T Siemens Trio scanner with an 8-channel head coil. Whole-brain, rsfMR imaging was performed using a gradient-echo EPI sequence sensitive to the BOLD effect (TE/TR = 30/3000 ms; flip angle = 90°; isotropic 1.72 mm2). Subjects were instructed to lie in dimmed light with their eyes open and try not to fall asleep. Image analysis was performed with FMRIB’s Software Library tools (www.fmrib.ox.ac.uk/fsl). Preprocessing of resting state data includes motion correction, brain extraction, spatial smoothing, and high-pass temporal filtering. Time series data was extracted from 9 DMN ROIs using FSL’s Featquery tool with 6mm radius spherical ROI masks created in Mango. After extraction, DMN connectivity was assess using structural equation modeling implemented in Amos 22.0 (IBM, Inc.). RESULTS/ANTICIPATED RESULTS: The exploratory SEM (eSEM) default mode network (DMN) model used consists of 9 regions of interest and 13 functional connectivity paths. The eSEM DMN model exhibited exceptional model fit to a primary resting state data set of 1169 subjects from the Genetics of Brain Structure project (1R01MH078111-01, David Glahn PI) with an RMSEA of 0.037. This model also had excellent model fit in 7 cohorts that were grouped by decade age (10s – RMSEA: 0.058, 20s – 0.051, 30s – 0.045, 40s – 0.048, 50s – 0.043, 60s – 0.035, 70s – 0.037). Analysis of the decade group-wise path coefficients identified 7 of the 13 paths (pC -> LMTG, pC -> PCC, PCC -> MPFG, PCC -> vACC, MPFG -> vACC, LIPL -> RIPL, LMTG -> RMTG) significantly negatively correlated with age-related changes. As early as the 1st decade of life, the functional connectivity within the DMN decreases. DISCUSSION/SIGNIFICANCE OF IMPACT: The DMN experiences progressive age-related decreases in connectivity, beginning in the first decade of life. Our results suggest that DMN path coefficients can serve as biomarkers of cognitive aging, which can then be used as quantitative traits for genetic analyses to identify genes associated with normal aging and age-related cognitive diseases.

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/GOALS: Recent studies have assessed the association between surgery with general anesthesia and cognitive decline in longitudinal cohorts of older adults. Patients diagnosed with dementia more frequently drop out of these longitudinal studies or are unable to complete the test battery. We revisit this aim with focus on methods for informative dropout. METHODS/STUDY POPULATION: We use data from the Mayo Clinic Study of Aging (MCSA), a longitudinal epidemiological study of the prevalence, incidence, and risk factors for mild cognitive impairment (MCI) and dementia. Our primary outcome of interest was global cognitive z-score, assessed at study visits every 15 months. We implement linear mixed effects models to assess the association between post-enrollment exposure to surgery/anesthesia and subsequent cognitive decline trajectories. Demented patients more frequently drop out of MCSA, so, subjects with the worst cognitive outcomes are unobserved and missing data may be informative. Since this missingness may be missing not at random, we use shared parameter models to analyze continuous cognitive outcomes while jointly modeling time to dementia. RESULTS/ANTICIPATED RESULTS: A total 1948 subjects, non-demented at baseline, from the MCSA were included. Median age was 79, 51% of subjects were male, and 16% had MCI at enrollment. Among median follow-up of 4 study visits over median 5.4 years, 172 patients developed dementia and dropped out from further assessments of cognitive function. In adjusted linear mixed effects models, our data suggest post-enrollment exposure to surgery/anesthesia is associated with a decline in cognitive function over time (change in slope = −0.07 standard deviations of cognitive z-score per year, 95%CI = −0.08, −0.05, p<.001). After adjusting for informative dropout using shared parameter models, surgery/anesthesia is associated with greater cognitive decline (change in slope = −0.14 per year, 95%CI = −0.16, −0.12, p<.001). DISCUSSION/SIGNIFICANCE OF IMPACT: We revisited a prior analysis by our group with consideration of informative dropout. Subjects who dropout due to dementia may have different trajectories of cognitive decline compared to non-demented subjects. Shared parameter models estimate the association between surgery/anesthesia and cognitive decline accounting for informative dropout.

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.

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.

OBJECTIVES/GOALS: Although referrals for specialty consultations are a core clinical process, they are prone to coordination and communication breakdowns that have led to adverse clinical outcomes. This project’s objective is to improve timely documentation, transmission, access, and quality of consultation notes across healthcare systems. METHODS/STUDY POPULATION: There are two specific aims for this project. In Aim 1, we will characterize clinical workflows and information flow during cross-institutional referrals. In Aim 2, we will develop and test a prototype leveraging electronic health information exchange (HIE) to increase closing the loop for cross-institutional referrals and improve the quality of consultation notes. To accomplish these aims, we will use human factors methods, including data analytics, medical-record reviews, semi-structured interviews of consultants, rapid prototyping, and usability evaluations. RESULTS/ANTICIPATED RESULTS: Results will inform the design and integration of clinician-facing technologies into clinical workflows to close the referral loop and improve diagnostic processes. Aim 1 will provide quantitative evidence about the quality of cross-institutional referrals, inform the eventual implementation of our prototype, and identify user interface features required for successful electronic health information exchange. Based on the results from Aim 1, reports and visual representations will be generated to illustrate information flows and clinical workflows. This will prioritize design efforts for the intervention’s prototype. Aim 2 will translate clinicians’ requirements into prototype features and assess clinicians’ experience with the prototype. DISCUSSION/SIGNIFICANCE OF IMPACT: The use and usefulness of HIE has been limited due to usability and implementation issues. Cross-institutional referrals are complex and dependent on HIE due to EHRs’ lack of interoperability. This project will provide evidence-based recommendations for the use of Fast Healthcare Interoperability Resources (FHIR) to improve HIE during referrals.

OBJECTIVES/GOALS: Utilizing clinical electronic health record (eHR) data pulled en masse from data warehouses provides unique challenges when applying it to retrospective studies. Use of this data in conjunction with metabolomic and genomic results to predict response to lisinopril or ondansetron has been completed. METHODS/STUDY POPULATION: Study population consists of >2000 subjects recruited from the Emergency Medicine Specimen Bank at University of Colorado Denver (UCD). All patients presenting to the emergency department are approached to participate which significantly increases demographic diversity of our study populations. Clinical data is pulled from Health Data Compass (data warehouse at UCD that collects all electronic health record (EHR) data to be able to deliver de-identified). Effectiveness of lisinopril and ondansetron were investigated using metabolomic data collected via ultra-high performance liquid chromatography mass spectrometry and genomic data from Illumina chip technology to find relevant correlations. RESULTS/ANTICIPATED RESULTS: Obtaining retrospective clinical data from data warehouses comes with significant challenges to be addressed. Verifying all clinical variables from patient EHRs is a crucial step that requires extensive quality control steps. As well, ensuring data validity, appropriateness of data points pulled as relate to the study criteria and identifying alternate EHR data points is needed. Chart review is a critical step necessary to surmount these challenges. Additionally, use of retrospective EHR data often necessitates the development of novel definitions of clinical effectiveness that can be abstracted from the EHR– such as how to determine decrease in nausea without a visual analogue scale. DISCUSSION/SIGNIFICANCE OF IMPACT: Utilizing data warehouses to deliver EHR data provides a valuable tool for completing retrospective precision medicine projects. The validation of definitions for clinical outcomes identifiable retrospectively are necessary and provide novel guidance for future studies.

OBJECTIVES/GOALS: Currently physicians are able to order CT Chest/Abd/Pelvis images as STAT or Routine. STAT images denote an emergency and are done immediately. We aim to determine the percentage of CT images that are inappropriately ordered as STAT, determine physician image ordering habits, and develop targeted interventions to encourage appropriate STAT image ordering. METHODS/STUDY POPULATION: A fishbone diagram helped reveal possible causes of inappropriate STAT image ordering. Based on the fishbone diagram, a survey was created to assess CT image ordering habits amongst radiology and internal medicine residents and attending physicians. All CT Chest/Abd/Pelvis images ordered over a 3 month period of time (July-Oct 2017) was obtained. The dataset included whether the image was ordered Stat vs Routine, time of image order, physician name and location, and reason for the imaging study.The STAT images were evaluated based on the explanation provided in the CT image order. Currently 2 radiology residents, 2 internal medicine residents, and 2 internal medicine hospitalists are evaluating all STAT CT images to determine appropriateness and how long they are willing to wait for the image to result in a read. RESULTS/ANTICIPATED RESULTS: Analysis of all CT Chest/Abd/Pelvis imaging orders revealed that 51% (1710/3345) of them were ordered as STAT. The preliminary analysis of 227 STAT images showed that 6% were inappropriate. We anticipate results of our survey to show differences in how long a STAT vs Routine image orders should take amongst Radiology and Internal Medicine clinicians. We also anticipate our survey to show differences in factors that warrant STAT imaging amongst the different medical fields. We anticipate that the clinician manual evaluation of all STAT CT image will reveal a large percentage of imaging orders to be inappropriate. All STAT imaging that were flagged as inappropriate will be characterized by the department who ordered the image and the reason provided for the imaging to assess for common themes. DISCUSSION/SIGNIFICANCE OF IMPACT: STAT images are the new routine with more images ordered STAT than Routine. Inappropriate STAT imaging results in truly urgent patients not getting the medical care they need. Many images ordered stat could potentially be switched to routine. By evaluating why clinicians are ordering STAT CT image inappropriately, we will be able to develop targeted interventions to decrease inappropriate STAT CT imaging.

OBJECTIVES/GOALS: Histopathological descriptions of kidney tissue provide more information about kidney disease severity and prognosis than serum measures, yet most patients with chronic kidney disease do not undergo kidney biopsy. We aim to develop a method to determine the degree of renal injury in patients with diabetes and hypertension without the need for biopsy. METHODS/STUDY POPULATION: Clinical data and renal tissue samples were collected from 864 patients undergoing tumor-associated nephrectomy in seven medical centers in the United States. Exclusion criteria included age < 18, presence of pyelonephritis or non-diabetic or hypertensive renal disease or incomplete clinical or histopathologic data. 19 histologic parameters were scored in a blinded manner by one renal pathologist. We examined the relationship between and functional variables (such as estimated glomerular filtration rate (eGFR)). Polynomial regression analysis was performed to model histopathologic variables and important clinical parameters such as eGFR RESULTS/ANTICIPATED RESULTS: 607 samples met inclusion criteria and were stratified as: control (no history of diabetes or hypertension, n = 160), hypertension alone (n = 224) and both diabetes and hypertension (n = 223). Interstitial fibrosis (IF) and glomerulosclerosis (GS) had the strongest correlations with eGFR. Regression analysis was used to model histopathologic score for a given eGFR. We found that diabetes and hypertension modified the relationship between tubulointerstitial fibrosis and eGFR. For example, while hypertensive patients without diabetes had 33% IF at an eGFR of 30 ml/min/1.73m2 (r2 = 0.64, p<0.01), hypertensive patients with diabetes had 32% IF at an eGFR of 30 ml/min/1.73m2 (r2 = .43, p<0.01) and control patients had approximately 23% IF at an eGFR of 30 ml/min/1.73m2 (r2 = 0.22, p<0.01). DISCUSSION/SIGNIFICANCE OF IMPACT: Here, we describe the relationship between renal structural changes and renal function and show that hypertension significantly modifies the relationship at a given eGFR. These data can be used to reasonably predict renal structural changes given clinical information without the need for renal biopsy and may provide prognostic value.

OBJECTIVES/GOALS: Flavorings differ between brands and tobacco products, potentially altering the sensory perceptions. This study aimed to examine discrepancies in flavor preference across various non-cigarette tobacco products among a national representative sample of US adult regular tobacco users. METHODS/STUDY POPULATION: Data from the Population Assessment of Tobacco and Health (PATH) Study Wave 3 (W3) were used. Weighted prevalence of flavor preference for various tobacco products, including electronic nicotine delivery systems (ENDS), traditional cigars, cigarillos/filtered cigars, hookah and snus/smokeless, was presented for 9,037 adult current and new former users of multiple flavored tobacco products. Within-subject flavor discrepancies were assessed using generalized estimating equations (GEE) models considering the complex sampling design of the PATH study. RESULTS/ANTICIPATED RESULTS: Most regular users of a flavored tobacco products reported using one flavor category per product. Fruit flavors, followed by tobacco, were the most common flavor categories among ENDS (32% and 25%, respectively) and hookah users (44% and 36%, respectively). Tobacco flavor was the most common among regular users of traditional cigars (80%), cigarillos/filtered cigars (55%), and smokeless tobacco (79%). Polytobacco users of ENDS and traditional cigars had the largest discrepancy, where about 68-76%% used different flavor categories when switching products. Conversely, polytobacco users of traditional cigars and cigarillos/filtered cigars had the lowest discrepancy (23-25%). DISCUSSION/SIGNIFICANCE OF IMPACT: Many consumers of multiple tobacco products had different flavor preferences when switching between products. In the event of a partial or full flavor ban for ENDS, these findings raise questions about consumer loyalty to a particular tobacco product or a particular flavor category. Conflict of Interest Description: MLG serves as a paid consultant for Johnson & Johnson and has received research grant from Pfizer, manufacturers of smoking cessation medications. The other authors have no conflicts to declare. CONFLICT OF INTEREST DESCRIPTION: MLG serves as a paid consultant for Johnson & Johnson and has received research grant from Pfizer, manufacturers of smoking cessation medications. The other authors have no conflicts to declare.

OBJECTIVES/GOALS: Inflammation following traumatic injury to the spinal cord persists long after the primary insult and is known to increase complication rates and prolong recovery time. We investigated the effects of Erythropoietin (EPO) in combination with Glucocorticoids on the levels of erythrocyte sedimentation rate (ESR), an overall measure of inflammation. METHODS/STUDY POPULATION: Electronic medical records from approximately 38 million patients in 27 Healthcare Organizations were analyzed using the TriNetX Analytics platform. Patients with spinal cord injuries (SCI) were defined with the ICD-10 code, G95 and two unique cohorts were defined for patients treated with injectable EPO in combination with injectable Glucocorticoids within 6 months of SCI or only injectable Glucocorticoids with no injectable EPO. ESR rates were queried from patient cohorts to evaluate the potential effects of the two treatment pathways on the ESR. Most recent lab results within 6 months before initiating treatment and 1-year post-treatment were defined as “before” and “after” treatment, respectively. Changes in ESR lab results were evaluated using unpaired t-test with Welch’s Correction. RESULTS/ANTICIPATED RESULTS: A total of 14,370 patients satisfied the inclusion criteria. 89 patients were treated with injectable EPO in combination with Glucocorticoids within 6 months of SCI. The ESR lab results were available for 33 patients before treatment with a mean of 63±33 mm/h. The ESR lab results were available for 22 patients after treatment with a mean of 51.7±34.1 mm/h. 14,281 patients were treated with Glucocorticoids (no injectable EPO) within 6 months of SCI. The ESR lab results were available for 2,042 patients before treatment with a mean of 29.2±30.5 mm/h. The ESR lab results were available for 2,184 patients after treatment with a mean of 32.6±30 mm/h. Patients treated with combinational therapy showed a reduction in ESR of 11.3 mm/h, while those treated with only Glucocorticoids showed an increase in ESR of 3.4 mm/h. DISCUSSION/SIGNIFICANCE OF IMPACT: The present results demonstrated that combinational therapy with injectable, EPO and glucocorticoids exhibited a significant reduction in ESR level. The study suggests that EPO and glucocorticoid might have a synergistic effect on reducing the inflammation following SCI. This approach might help reduce the therapeutic dose of glucocorticoids. Conflict of Interest Description: The authors declare that they have no competing interests. CONFLICT OF INTEREST DESCRIPTION: The authors declare that they have no competing interests.

OBJECTIVES/GOALS: The Dixon up-and-down method (U/D), original developed for testing explosives, is especially common in anesthesia research studies. The objective of this research is to compare the performance of the U/D method for obtaining and analyzing sensitivity data with that of the Bayesian Optimal Interval (BOIN) method. METHODS/STUDY POPULATION: A simulation study will compare the performance of the U/D method with the BOIN design. The two study designs offer alternative decision-making algorithms with respect to the dose at which the next experimental unit is treated. These alternative decisions may impact the precision of point estimates of the mean and standard deviation of the effective dose to elicit a response. Transition probability matrices are developed, and maximum likelihood estimates of the unknown parameters assessed for accuracy. For simulation, the effective dose is assumed to be randomly distributed with a known mean and standard deviation. Fixed dose levels are defined, and decisions for what level the next experimental unit should be treated at are defined by the Dixon up-and-down method and the BOIN design. For the U/D method, the stimulus is increased by one level in the absence of a response or decreased if a response occurs from an initial stimulus. A target toxicity probability of 0.50 is used to define the dose escalation or de-escalation rules for the application of the BOIN design. RESULTS/ANTICIPATED RESULTS: A feature of both methods is that the consecutive observations are concentrated about the mean value of the effective dose. However, the BOIN design tends to be more concentrated between these two dose levels. In the presence of severe adverse events, the BOIN design can choose to eliminate doses that are too toxic whereas the U/D design cannot eliminate any dose levels. Transition probability matrices are defined and parameters for the distribution of the effective dose are estimated using maximum likelihood estimation. Mean squared errors for the estimated mean and standard deviations compare the two study designs. DISCUSSION/SIGNIFICANCE OF IMPACT: The BOIN design offers an alternative method for decision-making compared with the U/D method. The BOIN design tends to concentrate dose levels about the mean more than the U/D. This may provide better estimates of the mean and standard deviation of the effective dose for eliciting a response in some circumstances.

OBJECTIVES/GOALS: Harnessing the EHR to support clinical research is critical for the study of rare diseases such as interstitial lung disease (ILD). However no studies have compared EHR and research-quality data in the ILD population. Our objectives were to (1) identify ILD patients and extract clinical data from an EHR system and (2) assess the performance of ILD data capture. METHODS/STUDY POPULATION: Case validated algorithms were implemented to identify patients from the University of California San Francisco EHR and extract key ILD clinical information including, demographic variables, process measures and patient outcomes. Key clinical information were defined based on consensus statements and ILD clinical trials. A subset of ILD patients, had variables recorded in both the EHR and a separate ILD longitudinal research database. The completeness of EHR data capture and level of agreement were compared between three data collection methods: (1) data manually and systematically collected for an ILD research database (gold standard), (2) data automatically extracted from structured fields in the EHR, and (3) data extracted from unstructured data sources. RESULTS/ANTICIPATED RESULTS: We identified 5857 ILD patients in the EHR, of which 2100 patients had data available in the both the EHR and research database. Baseline demographic variables, co-morbidities, use of diagnostic testing, pharmacotherapy were accurately extracted from structured fields. Outcome measures, including lung physiology, radiographic patterns, pathology results, and health related quality of life (HRQoL) were unevenly extracted from structured fields alone. With the exception of HRQoL, these measures were accurately captured in unstructured EHR sources. Notably, certain metrics were better defined in the EHR, including health care resource utilization metrics, acute exacerbations, medication side effects, supplemental oxygen use and specialty care referrals (rheumatology, lung transplant, palliative care, etc). DISCUSSION/SIGNIFICANCE OF IMPACT: A large real-world ILD cohort can be algorithmically extracted from the EHR along with key clinical variables with accuracy comparable to protocol-driven research databases. Rigorous assessment of the types of disease-specific variables that are present in EHR-derived data will inform future interventions to improve the fidelity, accessibility and use of the EHR in clinical research.

OBJECTIVES/GOALS: Identify the interactions between nasopharyngeal bacterial pathogens, commensals, and patient clinical characteristics in relation to the development of viral upper respiratory tract infections (URI) and acute otitis media (AOM) in infants. METHODS/STUDY POPULATION: The subjects were part of a prospective, longitudinal study (2008–2014) of infants to evaluate the prevalence and risks for the development of URI and AOM. Healthy infants (n = 362) were enrolled from near birth and followed to the first episode of AOM up to 12 months of age. Nasopharyngeal specimens and clinical traits were collected at monthly intervals between 1-6 months, month 9, and during viral URI episodes. Subjects were closely followed for AOM development. 16S rRNA sequencing was performed on the nasopharyngeal swabs to identify their bacterial composition. Multidimensional (2, 3, and 4 dimensional) co-presence, co-exclusion, and one-way relation patterns were identified between the microbiome compositions, health status, and other collected clinical traits. RESULTS/ANTICIPATED RESULTS: We analyzed 971 specimens collected monthly and during URI and AOM episodes from 139 infants. Of the 139 enrolled subjects, 96% had 2 or more healthy samples, 77% contracted URI/AOM during the study period, and 60% had at least 1 healthy sample before URI/AOM onset. Otopathogens (Moraxella, Haemophilus, and Streptococcus), Staphylococcus, and Pseudomonas were the most common pathogenic genera. Corynebacterium, Dolosigranulum, and Acinetobacter were 3 most abundant commensal bacterial genera. Samples from infants with AOM in the first year had a significantly higher relative abundance of Haemophilus, Enterobacter, and Yersinia, and lower relative abundance of Corynebacterium, and Pseudomonas compared to samples from infants who did not develop AOM. DISCUSSION/SIGNIFICANCE OF IMPACT: Identification of complex multidimensional interaction patterns within microbial communities and environmental factors is vital to understanding disease onset risk and prevention. Prophylactic microbiome and environmental factor modulation between enterotypes could be used to reduce URI/AOM onset in infants.