116 Determining factors associated with treatment outcomes in patients with shoulder arthritis

OBJECTIVES/GOALS: For shoulder osteoarthritis (OA), the understanding of the patient-specific factors that determine success of both non-operative and operative treatment options is limited. This study aims to identify key factors associated with the response and the heterogeneity of outcomes for both types of treatment. METHODS/STUDY POPULATION: Patients diagnosed with shoulder OA and treated with either reverse/anatomic total shoulder arthroplasty (rTSA/TSA) or non-operative management at the University of California, San Francisco were enrolled in this study. They were followed for a year to ascertain phenotypic traits and patient-reported outcomes (PROs). Magnetic Resonance Imaging (MRI) was used to calculate the Shoulder Osteoarthritis Severity (SOAS) score, a semi-quantitative global assessment of shoulder OA, and to measure fat fractions of rotator cuff muscles. A Microsoft Kinect camera was used to determine the Reachable Workspace (RWS). Linear regression models were used to assess the associations between baseline demographic and radiographic factors on outcomes related to shoulder function. RESULTS/ANTICIPATED RESULTS: It is anticipated that the pre-operative MRI-based SOAS score will be inversely correlated with the magnitude of improvement in PROs 1 year after rTSA/TSA and non-operative management of shoulder OA for the surgical replacement and non-operative cohorts, respectively. Additionally, the non-operative patients who convert to rTSA/TSA within 1 year of observation will have higher SOAS scores compared to patients who continue with non-operative management. The surgical replacement patients with an infraspinatus fat fraction of more than 5% will have worse shoulder function, as measured by RWS, compared to patients with an infraspinatus fraction less than 5%. DISCUSSION/SIGNIFICANCE: MRI may be a novel technique to better predict prognosis of shoulder OA management. This will allow for the development of appropriate algorithms in the prescription of treatments and may be used to counsel patients regarding their expected outcomes or to recommend alternative treatments.