Airways Abnormalities in a Prospective Cohort of Patients With Rheumatoid Arthritis

Background: Rheumatoid arthritis (RA) affects roughly 1% of the population and commonly involves the lungs. Of lung involvement in RA, interstitial lung disease (ILD) is well known; however, airways disease in RA is relatively understudied.

Research question: What are the baseline airways abnormalities in a prospective cohort of patients with RA based on pulmonary function testing (PFT) results, high-resolution CT (HRCT) scans, and computational imaging analysis and are there associations between these abnormalities and respiratory symptoms?

Study design and methods: In this single-center study, 188 patients with RA without a clinical diagnosis of ILD underwent HRCT imaging and PFT. Radiologists assessed HRCT scans for airway abnormalities. Computational imaging via VIDA Vision software and in-house quantitative CT imaging analysis was applied to 147 HRCT scans to quantify airway abnormalities.

Results: Airways obstruction (FEV₁ to FVC ratio < 0.7) was present in 20.7% of patients and was associated with older age, male sex, and higher smoking rate. Radiologists identified airway abnormalities in 61% of patients: 55% had bronchial wall thickening, 12% had bronchiectasis, and 5% had mosaic attenuation. These airways findings were associated with older age; male sex; lower FEV₁, FVC, and FEV₁ to FVC ratio; and higher rates of rheumatoid factor positivity. Prespecified quantitative CT scan metrics (wall thickening percentage and emphysema percentage) correlated with obstruction in PFT results and more severe respiratory symptoms, including shortness of breath and cough.

Interpretation: High rates of airways abnormalities were found in this prospective RA cohort based on 3 methods of detection. Significant associations were identified between quantitative CT scan measures and respiratory symptoms. Airways disease may be an underrecognized extra-articular manifestation of RA and quantitative CT imaging may be a sensitive method to detect the clinical impact on respiratory symptoms.