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 devel- oping 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. Team Science 10227 A Framework for Bringing Secondary Analysis of EHR Data to Geographically Dispersed Clinician Scientists James McClay, Jerrod Anzalone, Carol Geary and Ying Zhang University of Nebraska Medical Center ABSTRACT IMPACT: The described framework will enable other sites with a well-defined apparatus for enabling the secondary analy- sis 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, broad- ens 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 sci- ence 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 navi- gate 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 inves- tigators 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 edu- cation, resource sharing, and team formation to advance clinical questions from the idea stage toward national research network participation. 74123 A Learning Health Systems approach using health record data to construct patient frailty scores and predict safety events Alex Bokov, Sara Espinoza, Chandana Tripathy and Kathleen Stevens UT Health San Antonio 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 defi- cit-accumulation framework to predict patient safety events. METHODS/STUDY POPULATION: To assure that the research approach reflected perspectives of multiple stakeholders, our multidisci- plinary 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 associa- tions 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 deci- sions. The capability to harness EHR data and rapidly generate clinical knowledge can be transformative for complex care and contributes to Learning Health Systems. Translational Science, Policy, & Health Outcomes Science 21063 A Review of Novel Uses of REDCap in Clinical and Translational Science Aaryn Toles, MS 1 , Barbara Tafuto, MLS, PhD 2 and Doreen Lechner, PhD 2 1 Rutgers Robert Wood Johnson Medical School and 2 Rutgers School of Health Professions ABSTRACT IMPACT: This review will encourage further develop- ment of novel uses of REDCap for the benefit of the research 48 JCTS 2021 Abstract Supplement https://doi.org/10.1017/cts.2021.529 Downloaded from https://www.cambridge.org/core. 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