Background/purpose: Interstitial lung disease (ILD) is an important problem for patients with rheumatoid arthritis (RA). However, current approaches to ILD case finding in real-world data have been evaluated only in limited settings and identify only prevalent ILD and not new-onset disease. Our objective was to develop, refine, and validate a claims-based algorithm to identify both prevalent and incident ILD in RA patients compared to the gold standard of medical record review.
Methods: We used administrative claims data 2006-2015 from Medicare to derive a cohort of RA patients. We then identified suspected ILD using variations of ILD algorithms to classify both prevalent and incident ILD based on features of the data that included hospitalization vs. outpatient setting, physician specialty, pulmonary-related diagnosis codes, and exclusions for potentially mimicking pulmonary conditions. Positive predictive values (PPV) of several ILD algorithm variants for both prevalent and incident ILD were evaluated.
Results: We identified 234 linkable RA patients with sufficient data to evaluate for ILD. Overall, 108 (46.2%) of suspected cases were confirmed as ILD. Most cases (64%) were diagnosed in the outpatient setting. The best performing algorithm for prevalent ILD had a PPV of 77% (95% CI 67-84%) and for incident ILD was 96% (95% CI 85-100%).
Conclusion: Case finding in administrative data for both prevalent and incident interstitial lung disease in RA patients is feasible and has reasonable accuracy to support population-based research and real-world evidence generation.
Keywords: Administrative data; Algorithm; Interstitial lung disease; Positive predictive value; Rheumatoid arthritis; Validation.
© 2021. The Author(s).