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.

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.

Handling editor Josef S SmolenTwitter Kazuki Yoshida @kaz_yos and Deepak A Rao @deepakarao Contributors SKT and DHS were responsible for conceiving the study, overseeing recruitment and data interpretation. SKT drafted the first version of the manuscript. JS, JEE, and MGW were responsible for subject recruitment, conducting study visits, data entry and provided comments on the manuscript. KH performed data analysis and provided comments on the manuscript. KY provided biostatistical input and provided comments on the manuscript. LC, IA and KEM performed flow cytometry and data analyses and provided comments on the manuscript. AHJ contributed to study design and provided critical feedback on the manuscript. DAR contributed to study design, oversaw flow cytometry analyses and provided critical feedback on the manuscript.

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.

Objective Tocilizumab (TCZ) had similar efficacy when used as monotherapy or in combination with other treatments for rheumatoid arthritis (RA) in randomized controlled trials (RCT). We derived a remission prediction score for TCZ monotherapy (TCZm) using RCT data and now performed an external validation of the prediction score using “real world data” (RWD). Methods We identified patients in Corrona-RA who used TCZm (n=453), matching the design and patients from four 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 DMARD 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 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.