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ObjectiveTo investigate the independent relationship of rheumatoid arthritis (RA) to the type and severity of pulmonary patterns on spirometry compared to the pulmonary patterns in general population controls.MethodsIn this cross‐sectional study, we investigated the association of RA with pulmonary function measures on spirometry among subjects in the UK Biobank who underwent spirometry for research purposes. RA cases were identified based on self‐report and current disease‐modifying antirheumatic drug/glucocorticoid use. Controls were subjects without RA from the general population. Outcome measures included continuous forced expiratory volume in 1 second percent predicted (FEV1%) and forced vital capacity percent predicted (FVC%), type of spirometric pattern (restrictive or obstructive), and severity of the restrictive or obstructive pattern. We used multivariable regression to estimate the effects in RA cases compared to the effects in controls, adjusting for age, sex, body mass index, and smoking status/pack‐years.ResultsAmong 350,776 analyzed subjects who underwent spirometry (mean age 56.3 years; 55.8% female; 45.5% ever smokers), we identified 2,008 cases of treated RA. In multivariable analyses, RA was associated with lower FEV1% (β = –2.93 [95% confidence interval (95% CI) –3.63, –2.24]), FVC% (β = –2.08 [95% CI –2.72, –1.45]), and FEV1/FVC (β = –0.008 [95% CI –0.010, –0.005]) compared to controls. RA was additionally associated with restrictive patterns (odds ratio [OR] 1.36 [95% CI 1.21, 1.52]) and obstructive patterns (OR 1.21 [95% CI 1.07, 1.37]) independent of confounders, and was most strongly associated with severe restrictive and obstructive patterns.ConclusionRA is associated with increased odds of restrictive and obstructive patterns, and this relationship is not explained by confounders, including smoking status. In addition to restrictive lung disease, clinicians should also be aware that airway obstruction may be a pulmonary manifestation of RA.

ObjectiveAntirheumatic disease therapies have been used to treat coronavirus disease 2019 (COVID‐19) and its complications. We conducted a systematic review and meta‐analysis to describe the current evidence.MethodsA search of published and preprint databases in all languages was performed. Included studies described ≥1 relevant clinical outcome for ≥5 patients who were infected with severe acute respiratory syndrome coronavirus 2 and were treated with antirheumatic disease therapy between January 1, 2019 and May 29, 2020. Pairs of reviewers screened articles, extracted data, and assessed risk of bias. A meta‐analysis of effect sizes using random‐effects models was performed when possible.ResultsThe search identified 3,935 articles, of which 45 were included (4 randomized controlled trials, 29 cohort studies, and 12 case series). All studies evaluated hospitalized patients, and 29 of the 45 studies had been published in a peer‐reviewed journal. In a meta‐analysis of 3 cohort studies with a low risk of bias, hydroxychloroquine use was not significantly associated with mortality (pooled hazard ratio [HR] 1.41 [95% confidence interval (95% CI) 0.83, 2.42]). In a meta‐analysis of 2 cohort studies with some concerns/higher risk of bias, anakinra use was associated with lower mortality (pooled HR 0.25 [95% CI 0.12, 0.52]). Evidence was inconclusive with regard to other antirheumatic disease therapies, and the majority of other studies had a high risk of bias.ConclusionIn this systematic review and meta‐analysis, hydroxychloroquine use was not associated with benefit or harm regarding COVID‐19 mortality. The evidence supporting the effect of other antirheumatic disease therapies in COVID‐19 is currently inconclusive.

Abstract Objective The study sought to determine the dependence of the Electronic Medical Records and Genomics (eMERGE) rheumatoid arthritis (RA) algorithm on both RA and electronic health record (EHR) duration. Materials and Methods Using a population-based cohort from the Mayo Clinic Biobank, we identified 497 patients with at least 1 RA diagnosis code. RA case status was manually determined using validated criteria for RA. RA duration was defined as time from first RA code to the index date of biobank enrollment. To simulate EHR duration, various years of EHR lookback were applied, starting at the index date and going backward. Model performance was determined by sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve (AUC). Results The eMERGE algorithm performed well in this cohort, with overall sensitivity 53%, specificity 99%, positive predictive value 97%, negative predictive value 74%, and AUC 76%. Among patients with RA duration <2 years, sensitivity and AUC were only 9% and 54%, respectively, but increased to 71% and 85% among patients with RA duration >10 years. Longer EHR lookback also improved model performance up to a threshold of 10 years, in which sensitivity reached 52% and AUC 75%. However, optimal EHR lookback varied by RA duration; an EHR lookback of 3 years was best able to identify recently diagnosed RA cases. Conclusions eMERGE algorithm performance improves with longer RA duration as well as EHR duration up to 10 years, though shorter EHR lookback can improve identification of recently diagnosed RA cases.

Objective Since comorbidities such as autoimmune diseases may be associated with rheumatoid arthritis (RA) risk, we hypothesized that a family history of these other conditions might also predict RA. Therefore, we aimed to determine the association between family history of 79 comorbidities and RA. Methods This case–control study identified 821 cases of RA in the Mayo Clinic Biobank (positive predictive value 95%) and matched 3 controls to each case based on age, sex, recruitment year, and location. Patients self reported family history and characteristics (adjusted). Logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (95% CIs) for RA risk according to the presence of family history for each comorbidity, adjusted for body mass index, race, and smoking. Results Family history of several conditions was associated with developing RA, including rheumatic autoimmune diseases (OR adj 1.89 [95% CI 1.41–2.52]), pulmonary fibrosis (OR adj 2.12 [95% CI 1.16–3.80]), inflammatory bowel disease (OR adj 1.45 [95% CI 1.05–1.98]), hyper/hypothyroidism (OR adj 1.34 [95% CI 1.10–1.63]), and obstructive sleep apnea (OR adj 1.28 [95% CI 1.05–1.55]). Parkinson’s disease and type 2 diabetes mellitus were associated with a statistically decreased risk of RA that did not reach the prespecified significance threshold of P < 0.01 (OR adj 0.70 [95% CI 0.49–0.98] and OR adj 0.81 [95% CI 0.67–0.97], respectively). Analyses among 143 cases of incident RA were similar and also suggested an association with a family history of autism (OR 10.5 [95% CI 2.51–71.3]). Conclusion Family history of several autoimmune and nonautoimmune comorbidities was associated with increased risk of RA, providing an opportunity to identify novel populations at risk for RA.

ObjectiveRheumatoid arthritis (RA) is postulated to originate at mucosal surfaces, particularly the airway mucosa. To investigate this hypothesis, we determined the association between RA and asthma, passive smoke exposure, and age at start of smoking.MethodsFor this case–control study, we identified 1,023 cases of RA (175 incident) within a single‐center biobank population, using a rules‐based algorithm that combined self‐report with 2 diagnostic codes. Exposures were self‐reported on biobank questionnaires. Logistic regression models were used to calculate the association of exposures with RA, adjusting for potential confounders. Odds ratios (ORs) with 95% confidence intervals (95% CIs) were calculated.ResultsAfter adjustment for allergies, urban environment, and passive smoke exposure, asthma was found to be associated with RA in the full cohort (OR 1.28 [95% CI 1.04–1.58; P = 0.02]) but not the incident RA cohort (OR 1.17 [95% CI 0.66–2.06; P = 0.60]). History of allergic disease was associated with RA in both the full cohort (OR 1.30 [95% CI 1.12–1.51; P < 0.001]) and the incident RA cohort (OR 1.61 [95% CI 1.11–2.33; P = 0.01]), especially food allergy, which was significantly associated with RA in the full cohort (OR 1.38 [95% CI 1.08–1.75; P = 0.01]) and showed a trend toward significance in the incident RA cohort (OR 1.83 [95% CI 0.97–3.45; P = 0.06]). Passive smoke exposure at home or work was not associated with RA. Finally, age at start of smoking was not associated with increased odds of developing RA in either the full cohort (OR 1.03 [95% CI 1.00–1.06; P = 0.03]) or the incident RA cohort (OR 1.00 [95% CI 0.92–1.08; P = 0.98]).ConclusionAsthma and allergies may be associated with increased risk of RA. Passive smoke exposure and early age at start of smoking do not appear to influence risk of RA.