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Abstract Significant progress has been made in preventing severe COVID-19 disease through the development of vaccines. However, we still lack a validated baseline predictive biologic signature for the development of more severe disease in both outpatients and inpatients infected with SARS-CoV-2. The objective of this study was to develop and externally validate, via 5 international outpatient and inpatient trials and/or prospective cohort studies, a novel baseline proteomic signature, which predicts the development of moderate or severe (vs mild) disease in patients with COVID-19 from a proteomic analysis of 7000 + proteins. The secondary objective was exploratory, to identify (1) individual baseline protein levels and/or (2) protein level changes within the first 2 weeks of acute infection that are associated with the development of moderate/severe (vs mild) disease. For model development, samples collected from 2 randomized controlled trials were used. Plasma was isolated and the SomaLogic SomaScan platform was used to characterize protein levels for 7301 proteins of interest for all studies. We dichotomized 113 patients as having mild or moderate/severe COVID-19 disease. An elastic net approach was used to develop a predictive proteomic signature. For validation, we applied our signature to data from three independent prospective biomarker studies. We found 4110 proteins measured at baseline that significantly differed between patients with mild COVID-19 and those with moderate/severe COVID-19 after adjusting for multiple hypothesis testing. Baseline protein expression was associated with predicted disease severity with an error rate of 4.7% (AUC = 0.964). We also found that five proteins (Afamin, I-309, NKG2A, PRS57, LIPK) and patient age serve as a signature that separates patients with mild COVID-19 and patients with moderate/severe COVID-19 with an error rate of 1.77% (AUC = 0.9804). This panel was validated using data from 3 external studies with AUCs of 0.764 (Harvard University), 0.696 (University of Colorado), and 0.893 (Karolinska Institutet). In this study we developed and externally validated a baseline COVID-19 proteomic signature associated with disease severity for potential use in both outpatients and inpatients with COVID-19.

OBJECTIVES/GOALS: Chronic or new symptoms after infection with severe-acute-respiratory-coronavirus-2 (SARS-CoV-2) has been termed post-acute sequelae of Covid-19 (PASC) or Long Covid. Our objective is to present results from COVID-OUT, a phase 3 double-blind, randomized controlled trial of early outpatient treatment of Covid-19 with repurposed medications. METHODS/STUDY POPULATION: COVID-OUT enrolled adults age 30 to 85 with overweight or obesity who had proof of SARS-CoV-2 infection and fewer than 7 days of symptoms. In this 2 by 3 factorial design trial of metformin, ivermectin, fluvoxamine, or exact-matching placebo of each medication, participants were randomized 1:1:1:1:1:1 to the 6 treatment allocations. This abstract focuses on whether early treatment with metformin prevented Long Covid. Immediate release metformin was titrated to 1500mg daily over the first 6 days. We assessed the incidence of clinician-diagnosed Long Covid with follow up through 10 months after enrollment. We also assessed where participants were diagnosed with Long Covid, and where they received Long Covid treatment. RESULTS/ANTICIPATED RESULTS: Of 1124 participants, 98 (8.7%) report having a healthcare provider make a diagnosis of long covid. By arm, 6.9% (39/564) of metformin participants report having a diagnosis for long covid as compared with 10.5% (59/560) of matched placebo controls. The absolute reduction attributable to metformin was 3.6% (95%CI, 0.3% to 7.0%; P=0.031) with a relative risk reduction of 34% (95%CI, 3% to 55%). The metformin cost per long covid case averted was $28 (95%CI, $15 to $306). 10-month follow-up data will be available at the time of presentation as well as an analysis of baseline factors associated with the development of Long-Covid, independent of treatment allocation in the trial. DISCUSSION/SIGNIFICANCE: Metformin reduced the incidence of clinician-diagnosed long covid by 34% in a double-blind randomized placebo-controlled trial, and previous research published in-vitro activity by metformin against SARS-CoV-2 and other RNA viruses. Further investigation of metformin as early treatment for SARS-CoV-2 is warranted.

Objective To prospectively evaluate a logistic regression-based machine learning (ML) prognostic algorithm implemented in real-time as a clinical decision support (CDS) system for symptomatic persons under investigation (PUI) for Coronavirus disease 2019 (COVID-19) in the emergency department (ED). Methods We developed in a 12-hospital system a model using training and validation followed by a real-time assessment. The LASSO guided feature selection included demographics, comorbidities, home medications, vital signs. We constructed a logistic regression-based ML algorithm to predict “severe” COVID-19, defined as patients requiring intensive care unit (ICU) admission, invasive mechanical ventilation, or died in or out-of-hospital. Training data included 1,469 adult patients who tested positive for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) within 14 days of acute care. We performed: 1) temporal validation in 414 SARS-CoV-2 positive patients, 2) validation in a PUI set of 13,271 patients with symptomatic SARS-CoV-2 test during an acute care visit, and 3) real-time validation in 2,174 ED patients with PUI test or positive SARS-CoV-2 result. Subgroup analysis was conducted across race and gender to ensure equity in performance. Results The algorithm performed well on pre-implementation validations for predicting COVID-19 severity: 1) the temporal validation had an area under the receiver operating characteristic (AUROC) of 0.87 (95%-CI: 0.83, 0.91); 2) validation in the PUI population had an AUROC of 0.82 (95%-CI: 0.81, 0.83). The ED CDS system performed well in real-time with an AUROC of 0.85 (95%-CI, 0.83, 0.87). Zero patients in the lowest quintile developed “severe” COVID-19. Patients in the highest quintile developed “severe” COVID-19 in 33.2% of cases. The models performed without significant differences between genders and among race/ethnicities (all p-values > 0.05). Conclusion A logistic regression model-based ML-enabled CDS can be developed, validated, and implemented with high performance across multiple hospitals while being equitable and maintaining performance in real-time validation.

Metformin is the first-line medication for type 2 diabetes, but it also has a long history of improved outcomes in infectious diseases, such as influenza, hepatitis C, and in-vitro assays of zika. In the current Covid-19 pandemic, which has rapidly spread throughout the world, 4 observational studies have been published showing reduced mortality among individuals with home metformin use. There are several potential overlapping mechanisms by which metformin may reduce mortality from Covid-19. Metformin’s past anti-infectious benefits have been both against the infectious agent directly, as well as by improving the underlying health of the human host. It is unknown if the lower mortality suggested by observational studies in patients infected with Covid-19 who are on home metformin is due to direct activity against the virus itself, improved host substrate, or both.

OBJECTIVES/GOALS: Identification of COVID-19 patients at risk for deterioration following discharge from the emergency department (ED) remains a clinical challenge. Our objective was to develop a prediction model that identifies COVID-19 patients at risk for return and hospital admission within 30 days of ED discharge. METHODS/STUDY POPULATION: We performed a retrospective cohort study of discharged adult ED patients (n = 7,529) with SARS-CoV-2 infection from 116 unique hospitals contributing to the national REgistry of suspected COVID-19 in EmeRgency care (RECOVER). The primary outcome was return hospital admission within 30 days. Models were developed using Classification and Regression Tree (CART), Gradient Boosted Machine (GBM), Random Forest (RF), and least absolute shrinkage and selection (LASSO) approaches. RESULTS/ANTICIPATED RESULTS: Among COVID-19 patients discharged from the ED on their index encounter, 571 (7.6%) returned for hospital admission within 30 days. The machine learning (ML) models (GBM, RF,: and LASSO) performed similarly. The RF model yielded a test AUC of 0.74 (95% confidence interval [CI] 0.71–0.78) with a sensitivity of 0.46 (0.39-0.54) and specificity of 0.84 (0.82-0.85). Predictive variables including: lowest oxygen saturation, temperature; or history of hypertension,: diabetes, hyperlipidemia, or obesity, were common to all ML models. DISCUSSION/SIGNIFICANCE: A predictive model identifying adult ED patients with COVID-19 at risk for return hospital admission within 30 days is feasible. Ensemble/boot-strapped classification methods outperform the single tree CART method. Future efforts may focus on the application of ML models in the hospital setting to optimize allocation of follow up resources.

ABSTRACT IMPACT: Identifying racial disparities in septic shock mortality, a common and lethal condition, can inform future research and policy efforts aimed at understanding the drivers these disparities and addressing the underlying factors in order to reduce disparities and improve health. OBJECTIVES/GOALS: Septic shock is a major public health problem with significant mortality. Existing data indicate racial disparities in sepsis incidence, but evidence is limited on differences in septic shock outcomes. Our objective was to determine the association between race and septic shock mortality in a statewide cohort while controlling for clinical factors. METHODS/STUDY POPULATION: This was a retrospective analysis of septic shock patients in the One Florida Data Trust between 2012-18. Data was collected regarding age, sex, race, insurance status, and selected comorbid conditions [liver disease, hypertension, chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF), end-stage renal disease (ESRD), and human immunodeficiency virus infection (HIV)]. To account for severity of illness, we assigned Sequential Organ Failure Assessment scores for components based on laboratory values (labSOFA), and collected data on mechanical ventilation use and initial lactate.

Introduction: Formative evaluations of clinical teaching for emergency medicine (EM) faculty are limited. The goal of this study was to develop a behaviorally-based tool for evaluating and providing feedback to EM faculty based on their clinical teaching skills during a shift. Methods: We used a three-phase structured development process. Phase 1 used the nominal group technique with a group of faculty first and then with residents to generate potential evaluation items. Phase 2 included separate focus groups and used a modified Delphi technique with faculty and residents, as well as a group of experts to evaluate the items generated in Phase 1. Following this, residents classified the items into novice, intermediate, and advanced educator skills. Once items were determined for inclusion and subsequently ranked they were built into the tool by the investigators (Phase 3). Results: The final instrument, the “Faculty Shift Card,” is a behaviorally-anchored evaluation and feedback tool used to facilitate feedback to EM faculty about their teaching skills during a shift. The tool has four domains: teaching clinical decision-making; teaching interpersonal skills; teaching procedural skills; and general teaching strategies. Each domain contains novice, intermediate, and advanced sections with 2–5 concrete examples for each level of performance. Conclusion: This structured process resulted in a well-grounded and systematically developed evaluation tool for EM faculty that can provide real-time actionable feedback to faculty and support improved clinical teaching.