A Mobile Health Application Integrated in the Electronic Health Record for Rheumatoid Arthritis Patient-Reported Outcomes: A Controlled Interrupted Time-Series Analysis of Impact on Visit Efficiency.

OBJECTIVE: Patient-reported outcome (PRO) collection between visits for rheumatoid arthritis (RA) could improve visit efficiency, reducing in-person visits for patients with stable symptoms while facilitating access for those with symptoms. We examined whether a mobile health PRO application integrated in the electronic health record (EHR) could reduce visit volume for those with RA. METHODS: We developed an application for RA that prompted patients every other day to complete brief PRO questionnaires. Results of the application were integrated into the EHR. We tested the application in a controlled interrupted time-series analysis between 2020 and 2023. Rheumatologists received EHR-based messages based on PRO results recommending the patient receive a visit earlier or later than scheduled. The primary outcome was monthly visit volume during the year before versus the year after initiation. RESULTS: A total of 150 patients with RA consented and used the application. The median age was 62 years, 83% were female, 7% had fewer than 2 years of disease, and 50% were seropositive; 150 controls were well matched. Among those in the application cohort, the estimated monthly median visit volume in the year before use of the application was 31.2 (95% confidence interval [95% CI] 28.0-34.3); in controls, this was 30.4 (95% CI 27.3-33.6). In the year using the application, the estimated monthly visit volume was 36.8 (95% CI 33.4-40.3) compared to 38.7 (95% CI 35.2-42.3) in controls. The difference in the differences between the cohorts was not statistically significant (-2.7 visits, 95% CI -9.3 to 4.0). No differences were noted in flare rates or visit delays. CONCLUSION: In this initial trial of a PRO application intervention to improve visit efficiency, we found no association with reduced visit volume.

Identifying shared genetic architecture between rheumatoid arthritis and other conditions: a phenome-wide association study with genetic risk scores.

BACKGROUND: Rheumatoid arthritis (RA) shares genetic variants with other autoimmune conditions, but existing studies test the association between RA variants with a pre-defined set of phenotypes. The objective of this study was to perform a large-scale, systemic screen to determine phenotypes that share genetic architecture with RA to inform our understanding of shared pathways. METHODS: In the UK Biobank (UKB), we constructed RA genetic risk scores (GRS) incorporating human leukocyte antigen (HLA) and non-HLA risk alleles. Phenotypes were defined using groupings of International Classification of Diseases (ICD) codes. Patients with an RA code were excluded to mitigate the possibility of associations being driven by the diagnosis or management of RA. We performed a phenome-wide association study, testing the association between the RA GRS with phenotypes using multivariate generalized estimating equations that adjusted for age, sex, and first five principal components. Statistical significance was defined using Bonferroni correction. Results were replicated in an independent cohort and replicated phenotypes were validated using medical record review of patients. FINDINGS: We studied n = 316,166 subjects from UKB without evidence of RA and screened for association between the RA GRS and n = 1317 phenotypes. In the UKB, 20 phenotypes were significantly associated with the RA GRS, of which 13 (65%) were immune mediated conditions including polymyalgia rheumatica, granulomatosis with polyangiitis (GPA), type 1 diabetes, and multiple sclerosis. We further identified a novel association in Celiac disease where the HLA and non-HLA alleles had strong associations in opposite directions. Strikingly, we observed that the non-HLA GRS was exclusively associated with greater risk of the validated conditions, suggesting shared underlying pathways outside the HLA region. INTERPRETATION: This study replicated and identified novel autoimmune phenotypes verified by medical record review that share immune pathways with RA and may inform opportunities for shared treatment targets, as well as risk assessment for conditions with a paucity of genomic data, such as GPA. FUNDING: This research was funded by the US National Institutes of Health (P30AR072577, R21AR078339, R35GM142879, T32AR007530) and the Harold and DuVal Bowen Fund.

Humoral and cellular immune responses in persons with rheumatoid arthritis after a third dose of mRNA COVID-19 vaccine.

OBJECTIVE: Disease-modifying anti-rheumatic drugs (DMARDs) that treat rheumatoid arthritis (RA) may reduce immune responses to COVID-19 vaccination. We compared humoral and cell-mediated immunity before and after a 3rd dose of mRNA COVID vaccine in RA subjects. METHODS: RA patients that received 2 doses of mRNA vaccine enrolled in an observational study in 2021 before receiving a 3rd dose. Subjects self-reported holding or continuing DMARDs. Blood samples were collected pre- and 4 weeks after the 3rd dose. 50 healthy controls provided blood samples. Humoral response was measured with in-house ELISA assays for anti-Spike IgG (anti-S) and anti-receptor binding domain IgG (anti-RBD). T cell activation was measured after stimulation with SARS-CoV-2 peptide. Spearman's correlations assessed the relationship between anti-S, anti-RBD, and frequencies of activated T cells. RESULTS: Among 60 subjects, mean age was 63 years and 88% were female. 57% of subjects held at least 1 DMARD around the 3rd dose. 43% (anti-S) and 62% (anti-RBD) had a normal humoral response at week 4, defined as ELISA within 1 standard deviation of the healthy control mean. No differences in antibody levels were observed based on holding DMARDs. Median frequency of activated CD4 T cells was significantly greater post- vs. pre-3rd dose. Changes in antibody levels did not correlate with change in frequency of activated CD4 T cells. CONCLUSION: Virus-specific IgG levels significantly increased in RA subjects using DMARDs after completing the primary vaccine series, though fewer than two-thirds achieved a humoral response like healthy controls. Humoral and cellular changes were not correlated.

Satisfaction With a Virtual Learning Collaborative Aimed at Implementing Treat-to-Target in Rheumatoid Arthritis.

OBJECTIVE: Limited information is available concerning experiences of participants in a virtual learning collaborative (LC), and little qualitative data or participant feedback on how this format can be improved. One prior in-person LC in rheumatology successfully improved adherence with treat-to-target (TTT) for RA. We conducted a virtual LC on TTT and herein report on participant satisfaction. METHODS: We conducted a virtual LC with 18 rheumatology practices from across the United States during 2020 to 2021. The LC included a virtual kickoff meeting and monthly videoconferences, accompanied by data submission and feedback. At the conclusion of the LC, we surveyed the 45 LC participants concerning individual experience and satisfaction. RESULTS: All sites and 78% of participants responded to the surveys. The LC included small and large practices, 14 academic and 4 nonacademic, and respondents ranged in their roles: 24 physicians, 5 nurses or nurse practitioners, 3 administrators, and 3 other roles. Overall, 94% of respondents indicated they were either somewhat or very satisfied with the LC, and 94% said they would recommend a similar LC to a colleague. Aspects of the LC described as "very useful" included a kickoff meeting, intersite discussion, and monthly speakers; however, digital tools such as the Web site and meeting recordings were not found useful. CONCLUSIONS: Virtual LCs are feasible, and participants reported strong satisfaction. Virtual LCs were highly valued by rheumatologists, trainees, and their practice staffs. Potential topics were identified for future LCs that could improve the quality of care delivered to rheumatology patients.

Implementing a Treat-to-Target Approach for Rheumatoid Arthritis During the COVID-19 Pandemic: Results of a Virtual Learning Collaborative Program.

OBJECTIVE: A treat-to-target (TTT) approach improves outcomes in rheumatoid arthritis (RA). In prior work, we found that a learning collaborative (LC) program improved implementation of TTT. We conducted a shorter virtual LC to assess the feasibility and effectiveness of this model for quality improvement and to assess TTT during virtual visits. METHODS: We tested a 6-month virtual LC in ambulatory care. The LC was conducted during the 2020-2021 COVID-19 pandemic when many patient visits were conducted virtually. All LC meetings used videoconferencing and a website to share data. The LC comprised a 6-hour kickoff session and 6 monthly webinars. The LC discussed TTT in RA, its rationale, and rapid cycle improvement as a method for implementing TTT. Practices provided de-identified patient visit data. Monthly webinars reinforced topics and demonstrated data on TTT adherence. This was measured as the percentage of TTT processes completed. We compared TTT adherence between in-person visits versus virtual visits. RESULTS: Eighteen sites participated in the LC, representing 45 rheumatology clinicians. Sites inputted data on 1,826 patient visits, 78% of which were conducted in-person and 22% of which were held in a virtual setting. Adherence with TTT improved from a mean of 51% at baseline to 84% at month 6 (P for trend < 0.001). Each aspect of TTT also improved. Adherence with TTT during virtual visits was lower (65%) than during in-person visits (79%) (P < 0.0001). CONCLUSION: Implementation of TTT for RA can be improved through a relatively low-cost virtual LC. This improvement in TTT implementation was observed despite the COVID-19 pandemic, but we did observe differences in TTT adherence between in-person visits and virtual visits.

Acceptability of Vaccines Against Preventable Infections Including Coronavirus Disease 2019 Among Patients With Rheumatic Disease.

OBJECTIVE: Vaccination against preventable infections is widely recommended for patients with systemic rheumatic disease. The coronavirus disease 2019 (COVID-19) pandemic has highlighted variability in attitudes toward vaccination, particularly with the use of novel vaccine platforms. We studied attitudes toward vaccination against COVID-19 and other preventable infections among patients with systemic rheumatic disease and compared these against the general population. METHODS: We surveyed patients treated at Brigham and Women's Hospital for systemic rheumatic disease using a secure web-based survey or paper survey in English or Spanish, from December 2020 to April 2021. We included survey questions used in the nationwide Harris Poll (October 2020 and February 2021), allowing the comparison of responses with those from the general population. Response frequencies were estimated and compared using descriptive statistics. RESULTS: Of 243 participants (25% response rate), the mean age was 56 years, 82% were women, and 33% were nonwhite. Rheumatoid arthritis (50%) and systemic lupus erythematosus (28%) were the most common diagnoses. Thirty percent had been hospitalized previously for any infection. Seventy-six percent worried a lot or somewhat about contracting COVID-19. Attitudes toward vaccination were very favorable, with 92% having received a flu shot in the past year and 84% desiring a COVID-19 vaccine as soon as possible compared with 30% to 40% of Harris Poll respondents (P < 0.001). Physician recommendation to receive a vaccine and desire to avoid infection were the most common reasons for desiring vaccinations. CONCLUSION: Vaccine acceptability, including toward COVID-19 vaccines, was high among this population of patients with systemic rheumatic disease seen at an academic medical center cohort. Physician recommendation is a key factor for vaccine uptake.

Predicting Response to Tocilizumab Monotherapy in Rheumatoid Arthritis: A Real-world Data Analysis Using Machine Learning.

OBJECTIVE: Tocilizumab (TCZ) has shown similar efficacy when used as monotherapy as in combination with other treatments for rheumatoid arthritis (RA) in randomized controlled trials (RCTs). We derived a remission prediction score for TCZ monotherapy (TCZm) using RCT data and performed an external validation of the prediction score using real-world data (RWD). METHODS: We identified patients in the Corrona RA registry who used TCZm (n = 452), and matched the design and patients from 4 RCTs used in previous work (n = 853). Patients were followed to determine remission status at 24 weeks. We compared the performance of remission prediction models in RWD, first based on variables determined in our prior work in RCTs, and then using an extended variable set, comparing logistic regression and random forest models. We included patients on other biologic disease-modifying antirheumatic drug monotherapies (bDMARDm) to improve prediction. RESULTS: The fraction of patients observed reaching remission on TCZm by their follow-up visit was 12% (n = 53) in RWD vs 15% (n = 127) in RCTs. Discrimination was good in RWD for the risk score developed in RCTs, with area under the receiver-operating characteristic curve (AUROC) of 0.69 (95% CI 0.62-0.75). Fitting the same logistic regression model to all bDMARDm patients in the RWD improved the AUROC on held-out TCZm patients to 0.72 (95% CI 0.63-0.81). Extending the variable set and adding regularization further increased it to 0.76 (95% CI 0.67-0.84). CONCLUSION: The remission prediction scores, derived in RCTs, discriminated patients in RWD about as well as in RCTs. Discrimination was further improved by retraining models on RWD.

Impact of ICD10 and secular changes on electronic medical record rheumatoid arthritis algorithms.

OBJECTIVE: The objective of this study was to compare the performance of an RA algorithm developed and trained in 2010 utilizing natural language processing and machine learning, using updated data containing ICD10, new RA treatments, and a new electronic medical records (EMR) system. METHODS: We extracted data from subjects with ≥1 RA International Classification of Diseases (ICD) codes from the EMR of two large academic centres to create a data mart. Gold standard RA cases were identified from reviewing a random 200 subjects from the data mart, and a random 100 subjects who only have RA ICD10 codes. We compared the performance of the following algorithms using the original 2010 data with updated data: (i) a published 2010 RA algorithm; (ii) updated algorithm, incorporating ICD10 RA codes and new DMARDs; and (iii) published algorithm using ICD codes only, ICD RA code ≥3. RESULTS: The gold standard RA cases had mean age 65.5 years, 78.7% female, 74.1% RF or antibodies to cyclic citrullinated peptide (anti-CCP) positive. The positive predictive value (PPV) for ≥3 RA ICD was 54%, compared with 56% in 2010. At a specificity of 95%, the PPV of the 2010 algorithm and the updated version were both 91%, compared with 94% (95% CI: 91, 96%) in 2010. In subjects with ICD10 data only, the PPV for the updated 2010 RA algorithm was 93%. CONCLUSION: The 2010 RA algorithm validated with the updated data with similar performance characteristics as the 2010 data. While the 2010 algorithm continued to perform better than the rule-based approach, the PPV of the latter also remained stable over time.

Predicting Remission Among Patients With Rheumatoid Arthritis Starting Tocilizumab Monotherapy: Model Derivation and Remission Score Development.

OBJECTIVE: Most patients with rheumatoid arthritis (RA) strive to consolidate their treatment from methotrexate combinations. The objective of this analysis was to identify patients with RA most likely to achieve remission with tocilizumab (TCZ) monotherapy by developing and validating a prediction model and associated remission score. METHODS: We identified four TCZ monotherapy randomized controlled trials in RA and chose two for derivation and two for internal validation. Remission was defined as a Clinical Disease Activity Index score less than 2.8 at 24 weeks post randomization. We used logistic regression to assess the association between each predictor and remission. After selecting variables and assessing model performance in the derivation data set, we assessed model performance in the validation data set. The cohorts were combined to calculate a remission prediction score. RESULTS: The variables selected included younger age, male sex, lower baseline Clinical Disease Activity Index score, shorter RA disease duration, region of the world (Europe and South America [increased odds of remission] versus Asia and North America), no previous exposure to disease-modifying antirheumatic drugs and/or methotrexate, lower baseline Health Assessment Questionnaire Disability Index score, and baseline hematocrit. The area under the receiver operating characteristic curve was 0.739 in the derivation data set and 0.756 in the validation data set. Patients were categorized into three remission prediction categories based on the remission prediction score: 40% in the low (less than 10% probability of remission), 45% in the intermediate (10%-25% probability), and 15% in the moderate remission prediction category (greater than 25% probability). CONCLUSION: We used easily accessible factors to develop a remission prediction score to predict RA remission at 24 weeks after initializing TCZ monotherapy. These results may provide guidance to clinicians tailoring treatment options based on clinical characteristics.