Development and validation of predictive factors influencing rheumatoid arthritis associated with interstitial lung disease.

OBJECTIVE

This study is aimed at identifying key risk factors associated with the onset of rheumatoid arthritis-associated interstitial lung disease (RA-ILD) and developing and validating a novel risk prediction model for RA-ILD.

METHODS

This is a hospital-based retrospective cohort study. A total of 459 RA patients were selected from Longhua Hospital Affiliated with Shanghai University of Traditional Chinese Medicine between 2015 and 2020 as observation subjects. Demographic and clinical data were collected through the hospital's medical record system. The analysis involved evaluating demographic factors, joint clinical characteristics, traditional Chinese medicine (TCM) syndrome classification, laboratory indicators, medication history, and their associations with RA-ILD. Subsequently, a machine learning model was applied to create and validate a novel risk prediction model for the onset of RA-ILD.

RESULTS

The overall frequency of RA-ILD was 42.70%. Advanced age, smoking, elevated rheumatoid DAS28 score, higher radiographic joint staging (Phases II and III), strong positive CCP status (above 200), and methotrexate therapy were identified as independent risk factors for RA-ILD. Conversely, hormone therapy was found to be a protective factor against RA-ILD development. The RA-ILD prediction model, formulated based on these risk factors, exhibited superior predictive performance compared to existing models, with an AUC of 0.8914 (95% CI 0.8593-0.9234), a sensitivity of 74.5%, and a specificity of 89.7%.

CONCLUSION

The study results highlighted the risk factors for the onset of RA-ILD and underscored the utility of the established RA-ILD nomogram model for early identification of RA-ILD patients and predicting the future risk of RA-ILD in individuals with rheumatoid arthritis.