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Abstract Background Outcome measures are crucial to support a treat-to-target approach to rheumatoid arthritis (RA) care, yet their integration into clinical practice remains inconsistent. We developed an Electronic Heath Record-integrated, patient-facing side-car application to display RA outcomes (disease activity, functional status, pain scores), medications, and lab results during clinical visits (“RA PRO Dashboard”). The study aimed to evaluate patient perceptions and attitudes towards the implementation of a novel patient-facing dashboard during clinical visits using a mixed-methods approach. Methods RA patients whose clinicians used the dashboard at least once during their clinical visit were invited to complete a survey regarding its usefulness in care. We also conducted semi-structured interviews with a subset of patients to assess their perceptions of the dashboard. The interviews were transcribed verbatim and analyzed thematically using deductive and inductive techniques. Emerging themes and subthemes were organized into four domains of the Ecological Model of Health. Results Out of 173 survey respondents, 79% were interested in seeing the dashboard again at a future visit, 71% felt it improved their understanding of their disease, and 65% believed it helped with decision-making about their RA care. Many patients reported that the dashboard helped them discuss their RA symptoms (76%) and medications (72%) with their clinician. Interviews with 29 RA patients revealed 10 key themes: the dashboard was perceived as a valuable visual tool that improved patients’ understanding of RA outcome measures, enhanced their involvement in care, and increased their trust in clinicians and the clinic. Common reported limitations included concerns about reliability of RA outcome questionnaires for some RA patients and inconsistent collection and explanation of these measures by clinicians. Conclusions In both the quantitative and qualitative components of the study, patients reported that the dashboard improved their understanding of their RA, enhanced patient-clinician communication, supported shared decision-making, and increased patient engagement in care. These findings support the use of dashboards or similar data visualization tools in RA care and can be used in future interventions to address challenges in data collection and patient education.

Abstract Objective To assess agreement between patient survey report and physician recorded arthritic conditions and medication use in order to validate population-based epidemiologic approaches to auto-immune arthritic conditions. Methods Rheumatologists in the U.S. Appalachian region recruited men 50 years or older with a confirmed rheumatoid arthritis (RA) diagnosis. For each participating patient, the treating specialist completed a brief chart abstraction that included rheumatic diagnosis and corresponding treatment. Patients participated in a telephone interview using the same questionnaire as applied in a larger random digit dial survey that queried arthritis diagnosis and treatment. We assessed patient-clinician agreement with the Prevalence Adjusted and Biased Adjusted Kappa (PABAK) statistic. Results We included 36 patient-clinician dyads in this analysis. All clinicians and patients concurred in the RA diagnosis (PABAK = 1). For concomitant systemic lupus and scleroderma, we observed generally concordant responses (PABAK 0.89 and 1, respectively). For medication use, for hydroxychloroquine or sulfasalazine was associated with the lowest PABAK (0.39), intermediate values for methotrexate and for the “other conventional synthetic DMARDs” category (0.67), and with the highest agreement PABAK value for the “biologic DMARD or JAK 2 inhibitor” category (0.89). Conclusion Survey-based self-report of RA offers a useful approach in epidemiological investigation. This is particularly relevant to population-based approaches to autoimmune arthritis related to occupational and environmental factors.

Objective Despite interest in optimizing the electronic health record (EHR) to facilitate chronic disease care for conditions like rheumatoid arthritis (RA), progress in this area has been slow. EHR sidecar applications offer one solution, but little guidance exists to facilitate their successful development, deployment, and maintenance in the healthcare setting. We aimed to provide a roadmap for how to develop and deploy an EHR sidecar application based on our experience building a new EHR-integrated, patient-facing visualization tool that displayed disease outcomes to RA patients during a clinical visit (the “RA PRO dashboard”) in a large academic health center. Methods We describe the technical design and implementation of the RA PRO dashboard; report clinic workflow adaptations to incorporate this new technology; and discuss the resources required and challenges encountered in maintaining this application. Results The RA PRO dashboard required extensive human-centered design work, regulatory approvals, software development, user testing, integration with Epic-based workflows, and maintenance. Key requirements were prioritized based on the anticipated effects on usefulness and ease of use. Implementation science strategies were used to improve use of the dashboard in clinic and included education for patients, staff, and clinicians; reports of actual use of the dashboard and data quality; and regular meetings between the research team and clinicians to discuss and address barriers to use. Conclusion Successful development and deployment of an EHR-integrated application are resource-intensive and require technical, operational, and educational innovations. The roadmap presented in this study can serve as a resource for future developers.

Abstract Background Improving shared decision-making using a treat-to-target approach, including the use of clinical outcome measures, is important to providing high quality care for rheumatoid arthritis (RA). We developed an Electronic Health Record (EHR) integrated, patient-facing sidecar dashboard application that displays RA outcomes, medications, and lab results for use during clinical visits (“RA PRO dashboard”). The purpose of this study was to assess clinician perceptions and experiences using the dashboard in a university rheumatology clinic. Methods We conducted focus group (FG) discussions with clinicians who had access to the dashboard as part of a randomized, stepped-wedge pragmatic trial. FGs explored clinician perceptions towards the usability, acceptability, and usefulness of the dashboard. FG data were analyzed thematically using deductive and inductive techniques; generated themes were categorized into the domains of the Technology Acceptance Model (TAM). Results 3 FG discussions were conducted with a total of 13 clinicians. Overall, clinicians were enthusiastic about the dashboard and expressed the usefulness of visualizing RA outcome trajectories in a graphical format for motivating patients, enhancing patient understanding of their RA outcomes, and improving communication about medications. Major themes that emerged from the FG analysis as barriers to using the dashboard included inconsistent collection of RA outcomes leading to sparse data in the dashboard and concerns about explaining RA outcomes, especially to patients with fibromyalgia. Other challenges included time constraints and technical difficulties refreshing the dashboard to display real-time data. Methods for integrating the dashboard into the visit varied: some clinicians used the dashboard at the beginning of the visit as they documented RA outcomes; others used it at the end to justify changes to therapy; and a few shared it only with stable patients. Conclusions The study provides valuable insights into clinicians’ perceptions and experiences with the RA PRO dashboard. The dashboard showed promise in enhancing patient-clinician communication, shared decision-making, and overall acceptance among clinicians. Addressing challenges related to data collection, education, and tailoring dashboard use to specific patient populations will be crucial for maximizing its potential impact on RA care. Further research and ongoing improvements in dashboard design and implementation are warranted to ensure its successful integration into routine clinical practice.

Objectives Contraception is crucial for safely timing pregnancies in patients with SLE. This study investigated predictors of contraception documentation in patients with SLE, and the alignment of contraception practices with the 2020 American College of Rheumatology (ACR) guidelines, within the Rheumatology Informatics System for Effectiveness (RISE) registry.Materials and methods Female patients (aged 18–44 years) with SLE were identified via International Classification of Diseases (ICD)-9/ICD-10 coding within the RISE registry, which includes data from rheumatology clinics across the USA. Eligible patients were required to have ≥1 clinical visit in 2019 (prepandemic) or between 1 April 2020 and 30 March 2021 (mid-pandemic). Adjusted multilevel logistic modelling assessed patient, provider and practice characteristics for associations with contraception documentation. Contraception patterns were identified and compared with the 2020 ACR guidelines.Results Contraception documentation rates were similar in the prepandemic and mid-pandemic groups (8.1% and 8.5%, respectively). Higher documentation rates were found in women who were younger, White, and had more visits, as well as those seen within a health system, by a female provider, and within specific regions and electronic health record (EHR) systems. Prescription of a teratogenic medication did not influence contraception documentation or type. Oestrogen-containing contraceptives were prescribed less often to women at high risk for thrombosis (26.2% with thrombotic risk vs 60.6% without, p

Objective Accurate identification of lupus nephritis (LN) cases is essential for patient management, research and public health initiatives. However, LN diagnosis codes in electronic health records (EHRs) are underused, hindering efficient identification. We investigated the current performance of International Classification of Diseases (ICD) codes, 9th and 10th editions (ICD9/10), for identifying prevalent LN, and developed scoring systems to increase identification of LN that are adaptable to settings with and without LN ICD codes.Methods Training and test sets derived from EHR data from a large health system. An external set comprised data from the EHR of a second large health system. Adults with ICD9/10 codes for SLE were included. LN cases were ascertained through manual chart reviews conducted by rheumatologists. Two definitions of LN were used: strict (definite LN) and inclusive (definite, potential or diagnostic uncertainty). Gradient boosting models including structured EHR fields were used for predictor selection. Two logistic regression-based scoring systems were developed (‘LN-Code’ included LN ICD codes and ‘LN-No Code’ did not), calibrated and validated using standard performance metrics.Results A total of 4152 patients from University of California San Francisco Medical Center and 370 patients from Zuckerberg San Francisco General Hospital and Trauma Center met the eligibility criteria. Mean age was 50 years, 87% were female. LN diagnosis codes demonstrated low sensitivity (43–73%) but high specificity (92–97%). LN-Code achieved an area under the curve (AUC) of 0.93 and a sensitivity of 0.88 for identifying LN using the inclusive definition. LN-No Code reached an AUC of 0.91 and a sensitivity of 0.95 (0.97 for the strict definition). Both scoring systems had good external validity, calibration and performance across racial and ethnic groups.Conclusions This study quantified the underutilisation of LN diagnosis codes in EHRs and introduced two adaptable scoring systems to enhance LN identification. Further validation in diverse healthcare settings is essential to ensure their broader applicability.

Objective Some patients with rheumatic diseases might be at higher risk for coronavirus disease 2019 (COVID‐19) acute respiratory distress syndrome (ARDS). We aimed to develop a prediction model for COVID‐19 ARDS in this population and to create a simple risk score calculator for use in clinical settings. Methods Data were derived from the COVID‐19 Global Rheumatology Alliance Registry from March 24, 2020, to May 12, 2021. Seven machine learning classifiers were trained on ARDS outcomes using 83 variables obtained at COVID‐19 diagnosis. Predictive performance was assessed in a US test set and was validated in patients from four countries with independent registries using area under the curve (AUC), accuracy, sensitivity, and specificity. A simple risk score calculator was developed using a regression model incorporating the most influential predictors from the best performing classifier. Results The study included 8633 patients from 74 countries, of whom 523 (6%) had ARDS. Gradient boosting had the highest mean AUC (0.78; 95% confidence interval [CI]: 0.67‐0.88) and was considered the top performing classifier. Ten predictors were identified as key risk factors and were included in a regression model. The regression model that predicted ARDS with 71% (95% CI: 61%‐83%) sensitivity in the test set, and with sensitivities ranging from 61% to 80% in countries with independent registries, was used to develop the risk score calculator. Conclusion We were able to predict ARDS with good sensitivity using information readily available at COVID‐19 diagnosis. The proposed risk score calculator has the potential to guide risk stratification for treatments, such as monoclonal antibodies, that have potential to reduce COVID‐19 disease progression.

Objective Glucocorticoids are part of standard therapy for systemic lupus erythematosus (SLE), despite adverse effects associated with long‐term treatment. Belimumab improved clinical manifestations of SLE and reduced glucocorticoid doses in clinical trials and clinical practice; however, associations have not been examined using multi‐institutional electronic health record (EHR) data. Using the Rheumatology Informatics System for Effectiveness registry, we examined glucocorticoid use patterns among belimumab‐treated adults with SLE. Methods This retrospective analysis (GSK Study 209267) used EHR prescription records of patients with SLE managed by rheumatologists. Eligible patients had an index date (first belimumab prescription) between January 2014 and June 2018. The primary analysis compared patients' mean daily oral glucocorticoid (prednisone equivalent) dose over the 6 months preindex versus 6 months post index. An exploratory analysis assessed glucocorticoid doses at 12 and 24 months post index for patients with extended follow‐up. Results Of the 1987 patients receiving their first belimumab prescription, 767 had available glucocorticoid prescribing data, whereas 204 (primary analysis population) had glucocorticoids prescribed in the 6 months preindex and received belimumab according to the prescribing information for the first 8 weeks post index. The mean (SD) glucocorticoid dose was 12.5 (13.5) mg/day 3 months preindex, reducing to 10.3 (10.6) mg/day over the 6 months post index, and 8.7 (9.4) and 9.0 (9.3) mg/day at 12 and 24 months post index. Conclusion This study showed reductions in mean daily glucocorticoid dose after belimumab initiation. Several limitations of EHRs for real‐world effectiveness research were identified, which limited interpretation of results and may inform future study designs.

BackgroundHydroxychloroquine (HCQ) is commonly used for patients with autoimmune conditions. Long-term use of HCQ can cause retinal toxicity, but this risk can be reduced if high doses are avoided. ObjectiveWe developed and piloted an electronic health record–based dashboard to improve the safe prescribing of HCQ within the Veterans Health Administration (VHA). We observed pilot facilities over a 1-year period to determine whether they were able to improve the proportion of patients receiving inappropriate doses of HCQ. MethodsPatients receiving HCQ were identified from the VHA corporate data warehouse. Using PowerBI (Microsoft Corp), we constructed a dashboard to display patient identifiers and the most recent HCQ dose and weight (flagged if ≥5.2 mg/kg/day). Six VHA pilot facilities were enlisted to test the dashboard and invited to participate in monthly webinars. We performed an interrupted time series analysis using synthetic controls to assess changes in the proportion of patients receiving HCQ ≥5.2 mg/kg/day between October 2020 and November 2021. ResultsAt the start of the study period, we identified 18,525 total users of HCQ nationwide at 128 facilities in the VHA, including 1365 patients at the 6 pilot facilities. Nationwide, at baseline, 19.8% (3671/18,525) of patients were receiving high doses of HCQ. We observed significant improvements in the proportion of HCQ prescribed at doses ≥5.2 mg/kg/day among pilot facilities after the dashboard was deployed (–0.06; 95% CI –0.08 to –0.04). The difference in the postintervention linear trend for pilot versus synthetic controls was also significant (–0.06; 95% CI –0.08 to –0.05). ConclusionsThe use of an electronic health record–based dashboard reduced the proportion of patients receiving higher than recommended doses of HCQ and significantly improved performance at 6 VHA facilities. National roll-out of the dashboard will enable further improvements in the safe prescribing of HCQ.

Experts have noted a concerning gap between clinical natural language processing (NLP) research and real-world applications, such as clinical decision support. To help address this gap, in this viewpoint, we enumerate a set of practical considerations for developing an NLP system to support real-world clinical needs and improve health outcomes. They include determining (1) the readiness of the data and compute resources for NLP, (2) the organizational incentives to use and maintain the NLP systems, and (3) the feasibility of implementation and continued monitoring. These considerations are intended to benefit the design of future clinical NLP projects and can be applied across a variety of settings, including large health systems or smaller clinical practices that have adopted electronic medical records in the United States and globally.

Objective Individuals with autoimmune rheumatic disease (RD) are considered to be at increased risk for infection. However, few US population‐based studies have assessed whether these patients are at increased risk of hospitalization or death due to COVID‐19 compared with those without RD. Methods We performed a retrospective cohort study using national Veterans Affairs Health Care System data for individuals who tested positive for SARS‐CoV‐2. Outcomes of interest were hospitalization or death due to any cause within 30 days of COVID‐19 diagnosis. Outcomes were compared among veterans with RD and those without RD by using propensity score matching (PSM) and mixed‐effects multivariate logistic regression. Results Of 26,116 veterans with COVID‐19, 501 (1.9%) had an underlying RD. Prior to matching, patients with RD were more likely to have poor outcomes compared with controls (37.7% vs. 28.5% hospitalized; 6.4% vs. 4.5% died). In the PSM analysis, RD was not a significant predictor for poor outcomes; however, patients with prescriptions for glucocorticoids had increased odds of poor outcomes in a dose‐dependent manner (odds ratio [95% confidence interval] for hospitalization or death: 1.33 [1.20‐1.48] for doses >0 and ≤10 mg/day; 1.29 [1.09‐1.52] for doses >10 mg/day). Conclusion Among US veterans with COVID‐19, we did not find a significant association between RD and hospitalization or death. Poor outcomes appear to be mostly driven by age and other comorbidities, similar to the general veteran population. However, we observed an increased risk for poor outcomes among patients who received glucocorticoids, even at daily doses less than or equal to 10 mg.

Objective Patient‐reported outcomes (PROs) are an integral part of treat‐to‐target approaches in managing rheumatoid arthritis (RA). In clinical practice, however, routine collection, documentation, and discussion of PROs with patients are highly variable. The RISE LC (Rheumatology Informatics System for Effectiveness Learning Collaborative) was established to develop and share best practices in PRO collection and use across adult rheumatology practices in the United States Methods The goals of the RISE LC were developed through site surveys and in‐person meetings. Participants completed a baseline survey on PRO collection and use in their practices. RISE LC learning sessions focused on improving communication around PROs with patients and enhancing shared decision‐making in treatment plans. During the coronavirus disease 2019 (COVID‐19) pandemic, the RISE LC pivoted to adapt PRO tools for telehealth. Results At baseline, all responding sites (n = 15) had established workflows for collecting PROs. Most sites used paper forms alone. PRO documentation in electronic health records was variable, with only half of the sites using structured data fields. To standardize and improve the use of PROs, participants iteratively developed a Clinical Disease Activity Index–based RA Disease Activity Communication Tool to solicit treatment goals and improve shared decision‐making across sites. The COVID‐19 pandemic necessitated developing a tool to gauge PROs via telehealth. Conclusion The RISE LC is a continuous, structured method for implementing strategies to improve PRO collection and use in rheumatological care, initially adapting from the Learning Collaborative model and extending to include features of a learning network. Future directions include measuring the impact of standardized PRO collection and discussion on shared decision‐making and RA outcomes.

Abstract Background Behçet’s disease (BD), a chronic systemic vasculitis, has distinct geographical and ethnic variation. Data regarding the epidemiology of patients with BD in the U.S. are limited; therefore, we sought to describe BD patient characteristics and medication use in the U.S., and compared them with data from patients from endemic regions. Methods We conducted a cross-sectional study using data from the RISE registry (2014–2018). Patients aged ≥ 18 years with BD were included. Sociodemographic and treatment information was extracted. We compared patients from the RISE registry to data from other published studies of patients with BD from endemic areas. Results One thousand three hundred twenty-three subjects with BD from the RISE registry were included. Mean age was 48.7 ± 16.3 years, female to male ratio was 3.8:1, and 66.7% were White. The most frequently used medications included glucocorticoids (67.6%) and colchicine (55.0%). Infliximab and adalimumab were the most used biologics (14.5% and 14.1%, respectively); 3.2% of patients used apremilast. The RISE registry had more women (79.3%), and patients were older compared to previously published BD studies from endemic areas. Methotrexate and TNFi were more commonly reported in RISE (21.8% and 29.4%) compared to studies from Egypt and Turkey. Colchicine, cyclosporine, and cyclophosphamide were more commonly used in cohorts from Egypt, Turkey, and Iran. Conclusions Findings from the largest BD dataset in the U.S. suggest that BD patients are predominantly female. Further research is needed to explore the reasons for the higher prevalence of BD among women in the U.S. and its possible impact on disease severity and management.

Objective Rheumatoid arthritis (RA) and other autoimmune (AI) conditions are associated with inorganic dust exposure. Many military activities are likely to entail inorganic dust exposures. We wished to identify associations between prior military dust exposure and RA and other AI conditions. Methods We studied persons from a roster of Army, Navy, Air Force, or Marine Corps personnel who had served in Operation Enduring Freedom and Operations Iraqi Freedom and New Dawn. We linked military occupational codes to a job exposure matrix assigning dust exposure likelihood. We used the Veterans Affairs Health Care System (VAHCS) electronic health care records to identify cases of RA, systemic lupus erythematosus (SLE), systemic sclerosis (SSc), vasculitis, and inflammatory myositis. Generalized estimating equations modeled risk of RA and other AI conditions associated with dust exposure, taking into account military service branch, age at first VAHCS encounter, sex, race/ethnicity, smoking status, and years of military service. Results Of 438 086 veterans (68% ever‐smokers), 44% were classified with likely or somewhat likely dust exposure. Cases included 1139 cases with RA, 467 cases with SLE, and 180 cases with other AI diseases (SSc, vasculitis, or inflammatory myositis). Military dust exposure was associated with increased odds of RA (odds ratio [OR] = 1.10; 95% confidence interval [CI] = 1.003‐1.20) and increased odds of SSc, vasculitis, or inflammatory myositis (OR = 1.23; 95% CI = 1.14‐1.34) but was protective for SLE (OR = 0.81; 95% CI = 0.76‐0.88). Conclusion Dust exposure during past military service comprises an occupational and environmental risk factor for RA and other AI diseases. This is potentially relevant for prevention activities.

Abstract Background Poor patient‐clinician communication around patient‐reported outcomes (PROs) is a barrier to the effective management of rheumatoid arthritis (RA). We aimed to develop an RA ‘dashboard’ that could facilitate conversations about PROs and that would be acceptable to a wide range of patients, including English and Spanish speakers and patients with adequate or limited health literacy. Methods A diverse group of RA patients along with clinicians from two academic rheumatology clinics joined separate focus groups. We solicited feedback and made iterative changes to mock‐ups of an RA dashboard that visualized PROs using a human‐centred design process. We used the thematic analysis method to identify and characterize themes from the focus groups and used these insights to refine the dashboard. Results We conducted six focus groups involving 25 RA patients and three groups with 11 clinicians. Patients and clinicians agreed that the dashboard could enhance communication about PROs and RA disease activity and could promote patient self‐management. Patients varied in their (a) comprehension, (b) preferences for the display and features of the dashboard, and (c) desired uses for the dashboard. Clinicians expressed significant concerns about the logistics of using the dashboard in clinical practice. Conclusion Using principles of human‐centred design, we created an RA dashboard that was well‐accepted among patients and clinicians. The ability to customize the data display is important for tailoring the dashboard to patients with diverse needs and preferences. Special attention should be given to feasibility concerns voiced by clinicians.

Abstract There is a great and growing need to ascertain what exactly is the state of a patient, in terms of disease progression, actual care practices, pathology, adverse events, and much more, beyond the paucity of data available in structured medical record data. Ascertaining these harder-to-reach data elements is now critical for the accurate phenotyping of complex traits, detection of adverse outcomes, efficacy of off-label drug use, and longitudinal patient surveillance. Clinical notes often contain the most detailed and relevant digital information about individual patients, the nuances of their diseases, the treatment strategies selected by physicians, and the resulting outcomes. However, notes remain largely unused for research because they contain Protected Health Information (PHI), which is synonymous with individually identifying data. Previous clinical note de-identification approaches have been rigid and still too inaccurate to see any substantial real-world use, primarily because they have been trained with too small medical text corpora. To build a new de-identification tool, we created the largest manually annotated clinical note corpus for PHI and develop a customizable open-source de-identification software called Philter (“Protected Health Information filter”). Here we describe the design and evaluation of Philter, and show how it offers substantial real-world improvements over prior methods.

Objective It is unclear if biosimilars of biologics for inflammatory arthritis are realizing their promise to increase competition and improve accessibility. This study evaluates biosimilar tumor necrosis factor inhibitor (TNFi) utilization across rheumatology practices in the United States and compares whether patients initiating biosimilars remain on these treatments at least as long as new initiators of bio‐originators. Methods We identified a cohort of patients initiating a TNFi biosimilar between January 2017 and September 2018 from an electronic health record registry containing data from 218 rheumatology practices and over 1 million rheumatology patients in the United States. We also identified a cohort of patients who initiated the bio‐originator TNFi during the same period. We calculated the proportion of biosimilar prescriptions compared with other TNFi's and compared persistence on these therapies, adjusting for age, sex, diagnoses codes, and insurance type. Results We identified 909 patients prescribed the biosimilar infliximab‐dyyb, the only biosimilar prescribed, and 4413 patients with a new prescription for the bio‐originator infliximab. Biosimilar patients tended to be older, have a diagnosis code for rheumatoid arthritis, and covered by Medicare insurance. Over the study period, biosimilar prescriptions reached a maximum of 3.5% of all TNFi prescriptions. Patients persisted on the biosimilar at least as long as the bio‐originator infliximab (hazard ratio [HR] 0.83, P = 0.07). Conclusion The uptake of biosimilars in the United States remains low despite persistence on infliximab‐dyyb being similar to the infliximab bio‐originator. These results add to clinical studies that should provide greater confidence to patients and physicians regarding biosimilar use.

BackgroundRoutine collection of disease activity (DA) and patient-reported outcomes (PROs) in rheumatoid arthritis (RA) are nationally endorsed quality measures and critical components of a treat-to-target approach. However, little is known about the role electronic health record (EHR) systems play in facilitating performance on these measures. ObjectiveUsing the American College Rheumatology’s (ACR’s) RISE registry, we analyzed the relationship between EHR system and performance on DA and functional status (FS) quality measures. MethodsWe analyzed data collected in 2018 from practices enrolled in RISE. We assessed practice-level performance on quality measures that require DA and FS documentation. Multivariable linear regression and zero-inflated negative binomial models were used to examine the independent effect of EHR system on practice-level quality measure performance, adjusting for practice characteristics and patient case-mix. ResultsIn total, 220 included practices cared for 314,793 patients with RA. NextGen was the most commonly used EHR system (34.1%). We found wide variation in performance on DA and FS quality measures by EHR system (median 30.1, IQR 0-74.8, and median 9.0, IQR 0-74.2), respectively). Even after adjustment, NextGen practices performed significantly better than Allscripts on the DA measure (51.4% vs 5.0%; P

Summary: In traumatic brain injury (TBI), a diversity of brain resident and peripherally derived myeloid cells have the potential to worsen damage and/or to assist in healing. We define the heterogeneity of microglia and macrophage phenotypes during TBI in wild-type (WT) mice and Ccr2−/− mice, which lack macrophage influx following TBI and are resistant to brain damage. We use unbiased single-cell RNA sequencing methods to uncover 25 microglia, monocyte/macrophage, and dendritic cell subsets in acute TBI and normal brains. We find alterations in transcriptional profiles of microglia subsets in Ccr2−/− TBI mice compared to WT TBI mice indicating that infiltrating monocytes/macrophages influence microglia activation to promote a type I IFN response. Preclinical pharmacological blockade of hCCR2 after injury reduces expression of IFN-responsive gene, Irf7, and improves outcomes. These data extend our understanding of myeloid cell diversity and crosstalk in brain trauma and identify therapeutic targets in myeloid subsets.

ABSTRACT Objective Research using electronic health records (EHRs) may offer advantages over observational prospective cohort studies, including lower costs and a more generalizable patient population; however, EHR data may be more biased because of the high prevalence of missing data. We took advantage of a unique clinical setting in which all patients with rheumatoid arthritis (RA) were asked to participate in a longitudinal cohort study that would examine potential biases of EHR vs. prospective cohort designs in assessment of disease outcomes, but only some chose to participate. Methods For individuals both participating in the cohort (“cohort,” n = 187) and not participating (“noncohort,” n = 190), we retrieved data regarding RA disease activity and other sociodemographic and clinical factors from data recorded in the EHR between 2013 and 2017. We compared the prevalence of missing data between groups and studied differences in disease activity measures over time. Results Disease activity measures were less likely to be missing for cohort participants compared with noncohort participants (0.2%‐13% vs. 2%‐22%, respectively). No significant differences were present at baseline with respect to race/ethnicity or disease activity measures between groups. However, black, non‐Hispanic race/ethnicity was associated with worse longitudinal disease activity compared with white, non‐Hispanic individuals in noncohort participants (β = 6.47, P =0.03) but not in cohort participants (β = −0.10, P = 0.97) (P interaction = 0.09). Conclusion Findings suggest that data derived from the EHR were comparable to a cohort across some variables but captured racial/ethnic disparities in long‐term outcomes not observed in the cohort study. Research utilizing EHR data in conjunction with cohort studies may provide new opportunities for studying health disparities.

OBJECTIVE:Infection is a leading cause of morbidity and mortality in systemic lupus erythematosus (SLE). Therapeutic practices have evolved over the past 15 years, but effects on infectious complications of SLE are unknown. We evaluated trends in hospitalizations for severe and opportunistic infections in a population-based SLE study. METHODS:Data derive from the 2000 to 2011 United States National Inpatient Sample, including individuals who met a validated administrative definition of SLE. Primary outcomes were diagnoses of bacteremia, pneumonia, opportunistic fungal infection, herpes zoster, cytomegalovirus, or pneumocystis pneumonia (PCP). We used Poisson regression to determine whether infection rates were changing in SLE hospitalizations and used predictive marginals to generate annual adjusted rates of specific infections. RESULTS:We identified 361,337 SLE hospitalizations from 2000 to 2011 meeting study inclusion criteria. Compared to non-SLE hospitalizations, SLE patients were younger (51 vs. 62 years), predominantly female (89% vs. 54%), and more likely to be racial/ethnic minorities. SLE diagnosis was significantly associated with all measured severe and opportunistic infections. From 2000 to 2011, adjusted SLE hospitalization rates for herpes zoster increased more than non-SLE rates: 54 to 79 per 10,000 SLE hospitalizations compared with 24 to 29 per 10,000 non-SLE hospitalizations. Conversely, SLE hospitalizations for PCP disproportionately decreased: 5.1 to 2.5 per 10,000 SLE hospitalizations compared with 0.9 to 1.3 per 10,000 non-SLE hospitalizations. CONCLUSIONS:Among patients with SLE, herpes zoster hospitalizations are rising while PCP hospitalizations are declining. These trends likely reflect evolving SLE treatment strategies. Further research is needed to identify patients at greatest risk for infectious complications.

Co-prescription of folic acid in patients receiving low dose oral methotrexate is recommended because it reduces adverse events and prolongs the use of methotrexate (MTX). However, little is known about how often new users of methotrexate are co-prescribed folic acid, and what factors are associated with its use. We aimed to determine the prevalence, predictors of, and persistence of folic acid use in a population-based cohort of MTX users with rheumatic diseases.Using a national, administrative database of patients seen through the Veterans Health Administration (VHA) that included pharmacy and laboratory data, we performed an observational cohort study of veterans over 65 years old who were new users of MTX. We used log-binomial regression to identify independent predictors of folic acid use and Kaplan Meyer survival analysis to examine persistence of folic acid over time.We studied 2467 incident users of MTX. 27% of patients were not prescribed folic acid through the VHA pharmacy within 30 days of MTX initiation. Patients who did not see a rheumatologist were 23% less likely to receive folic acid compared to patients who did have a rheumatologist visit during the baseline period (RR (95% CI) 0.77 (0.72, 0.82). These results remained unchanged even after adjusting for demographic, clinical, and other factors (adjusted RR (95% CI) 0.78 (0.74, 0.85)). After 20 months, only 50% of patients continued to receive folic acid.In a nationwide VHA cohort of new users of oral MTX, many patients did not receive folic acid or discontinued it over time. Rheumatologists were more likely to prescribe folic acid than other providers. These data highlight the need to improve patient safety for users of methotrexate by standardizing workflows for folic acid supplementation.