Background The effect of the COVID19 pandemic on community‐based rheumatology care and use of telehealth is unclear. Methods Using a community practice‐based rheumatologist network, we examined trends in in‐person vs. telehealth visits vs. canceled visits in three time periods: pre‐COVID19, COVID19‐Transition (6‐weeks beginning 3/23/20), and post‐COVID19‐Transition (May‐August). In the Transition period, we compared patients who received in‐person care vs. telehealth visits vs. cancelled all visits. We used multivariable logistic regression to identify factors associated with canceled or telehealth visits. Results Pre‐COVID19, there were 7,075 visits/week among 60,002 unique rheumatology patients cared for by approximately 300 providers practicing in 92 offices. This decreased substantially (24.6% reduction) during COVID19‐Transition period for in‐person but rebounded to pre‐COVID19 levels during post‐COVID19 transition. There were almost no telehealth visits pre‐COVID19, but telehealth increased substantially during the COVID19‐Transition (41.4% of all follow‐up visits) and slightly decreased during post‐COVID19‐Transition (27.7% of visits). Older age, female sex, Black or Hispanic race/ethnicity, lower socioeconomic status, and rural residence were associated with greater likelihood of cancelling visits. Most factors were also associated with a lower likelihood of having telehealth vs. in‐office visits. Patients living further from the rheumatologists’ office were more likely to use telehealth. Conclusion COVID19 led to large disruptions in rheumatology care; these disruptions were only partially offset by increases in telehealth use and disproportionately affected racial/ethnic minorities and patients with lower socioeconomic status. During the COVID‐19 era, telehealth continues to be an important part of rheumatology practice, but disparities in access to care exist for some vulnerable groups.

Background:Clear characterization of how different types of patient-generated data reflect patient experience is needed to guide integration of electronic patient-reported outcome (ePRO) measures and biometrics in generating real-word evidence (RWE) related to rheumatoid arthritis (RA).Objectives:To characterize the level of participant (pt) engagement/adherence and data completeness in an ongoing study of 250 RA pts enrolled in the Digital Tracking of Arthritis Longitudinally (DIGITAL) study1of the ArthritisPower real-world registry.Methods:ArthritisPower pts with RA were invited to join a digital RWE study with 14-day lead-in and 12-week main study period. In the lead-in, pts were required to electronically complete: a) two daily single-item Pain and Fatigue numeric rating scales and b) longer weekly sets of ePROs. Successful completers of the lead-in were mailed a smartwatch (Fitbit Versa) and study materials. The smartwatch collected activity, heart rate, and sleep duration/quality biosensor data; a study-specific customization of the ArthritisPower mobile application collected ePROs. The main study period included automated and manual reminders/prompts about completing ePROs, wearing the smartwatch and regularly syncing it. Study coordinators monitored pt data and contacted pts via email, text and/or phone to resolve adherence issues during the conduct of the study based on pre-determined rules triggering pt contact. Rules were based chiefly on consecutive spans of missing data. Pts were considered adherent in giving complete data for each week if providing (1) daily ePROs for ≥5 of 7 days/week, (2) weekly ePROs and (3) ≥80% of synced activity data for ≥5 of 7 days/week. Composite adherence for the first month of the main study period required meeting >70% weekly adherence parameters during the first 30 days, ie completing daily ePROs for ≥5 of 7 days/week, weekly ePROs ≥3 of 4 weeks and ≥80% of synced activity data for ≥5 of 7 days/week.Results:As of December 2019, 170 ArthritisPower members enrolled and completed at least 30 days of the main study period; 92.9% female with mean (SD) age 52.5 (10.7) and 10.5 (10.4) years since diagnosis. The overall conversion rate from initial interest to successful completion of the lead-in period was 49.0%. Pts who advanced to the main study were significantly more likely than those who did not to be currently employed (52.9% vs. 41.8%, p=0.038) and be on biologic DMARD monotherapy (64.7% vs. 47.5%, p=0.001). Overall, daily ePRO data had the lowest adherence with 70.0% of pts providing >70% of the requested data consistently across the first 30 days of the main study period (Figure 1). Composite adherence was met by 66.5% of pts. The most common time of day to provide ePRO data was morning, in the hours around scheduled app and email notifications at 10 a.m. in pt’s local time zone. Activity data had the highest adherence and persistence, with 92.9% of pts providing 80% or more of activity data for each 24-hour period in the first 30 days (Figures 1 & 2). Observed weekly adherence did not decline over time. Of 5100 possible person days in the study at day 30, we observed 643 days (91.0% of actual to maximum possible total patient days) where activity data was provided for at least 80% of the 24-hour period.Conclusion:RWE studies involving passive data collection in RA require pt-centric implementation and design to minimize pt burden, promote longitudinal engagement and maximize adherence. Passive data capture via activity trackers such as smartwatches, along with regular contact such as automated reminders, may facilitate greater pt adherence in providing longitudinal data for clinical trials.References:[1]Nowell WB, et al. JMIR Res Protoc. 2019;8(9):e14665.Disclosure of Interests:W. Benjamin Nowell: None declared, Jeffrey Curtis Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Fenglong Xie: None declared, Hong Zhao: None declared, David Curtis: None declared, Kelly Gavigan: None declared, Shilpa Venkatachalam: None declared, Laura Stradford: None declared, Jessica Boles: None declared, Justin Owensby: None declared, Cassie Clinton: None declared, Ilya Lipkovich Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Amy Calvin Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Virginia S. Haynes Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company

Introduction:In patients with rheumatoid arthritis (RA), attaining remission or low disease activity (LDA), as recommended by the treat-totarget approach, has shown to yield improvement in symptoms and quality of life. However, limited evidence from real-world settings is available to support the premise that better disease control is associated with lower healthcare costs. This study fills in evidence gaps regarding the cost of care by RA disease activity (DA) states and by therapy. Methods: This retrospective cohort study linked medical and prescription claims from Optum Clinformatics Data Mart to electronic health record data from Illumination Health over 1/1/ 2010-3/31/2020. Mean annual costs for payers and patients were examined, stratifying on DA state and baseline use of conventional synthetic disease-modifying antirheumatic drugs

Background/purpose Interstitial lung disease (ILD) is an important problem for patients with rheumatoid arthritis (RA). However, current approaches to ILD case finding in real-world data have been evaluated only in limited settings and identify only prevalent ILD and not new-onset disease. Our objective was to develop, refine, and validate a claims-based algorithm to identify both prevalent and incident ILD in RA patients compared to the gold standard of medical record review. Methods We used administrative claims data 2006–2015 from Medicare to derive a cohort of RA patients. We then identified suspected ILD using variations of ILD algorithms to classify both prevalent and incident ILD based on features of the data that included hospitalization vs. outpatient setting, physician specialty, pulmonary-related diagnosis codes, and exclusions for potentially mimicking pulmonary conditions. Positive predictive values (PPV) of several ILD algorithm variants for both prevalent and incident ILD were evaluated. Results We identified 234 linkable RA patients with sufficient data to evaluate for ILD. Overall, 108 (46.2%) of suspected cases were confirmed as ILD. Most cases (64%) were diagnosed in the outpatient setting. The best performing algorithm for prevalent ILD had a PPV of 77% (95% CI 67–84%) and for incident ILD was 96% (95% CI 85–100%). Conclusion Case finding in administrative data for both prevalent and incident interstitial lung disease in RA patients is feasible and has reasonable accuracy to support population-based research and real-world evidence generation.

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