Risk prediction model in rheumatoid arthritis‐associated interstitial lung disease. Issue 12 (22nd May 2020) - Kim, Ho Cheol; Lee, Jeong Seok; Lee, Eun Young; Ha, You‐Jung; Chae, Eun Jin; Han, Minkyu; Cross, Gary; Barnett, Joseph; Joseph, Jacob; Song, Jin Woo
ABSTRACT: Background and objective: RA‐ILD has a variable clinical course, and its prognosis is difficult to predict. Moreover, risk prediction models for prognosis remain undefined. Methods: The prediction model was developed using retrospective data from 153 patients with RA‐ILD and validated in an independent RA‐ILD cohort ( n = 149). Candidate variables for the prediction models were screened using a multivariate Cox proportional hazard model. C‐statistics were calculated to assess and compare the predictive ability of each model. Results: In the derivation cohort, the median follow‐up period was 54 months, and 38.6% of the subjects exhibited a UIP pattern on HRCT imaging. In multivariate Cox analysis, old age (≥60 years, HR: 2.063), high fibrosis score (≥20% of the total lung extent, HR: 4.585), a UIP pattern (HR: 1.899) and emphysema (HR: 2.596) on HRCT were significantly poor prognostic factors and included in the final model. The prediction model demonstrated good performance in the prediction of 5‐year mortality (C‐index: 0.780, P  < 0.001); furthermore, patients at risk were divided into three groups with 1‐year mortality rates of 0%, 5.1% and 24.1%, respectively. Predicted and observed mortalities at 1, 2 and 3 years were similar in the derivation cohort, and the prediction model was also effective in predicting prognosis of the validation cohort (C‐index: 0.638, P  < 0.001). Conclusion: Our results suggest that a risk prediction model based on HRCT variables could be useful for patients with RA‐ILD. Abstract : The risk prediction model for RA‐ILD patients using age from 60 years onwards and imaging parameters such as extent of fibrosis at ≥20% of the total lung extent, a UIP pattern and emphysema, was effective in predicting prognosis both in the derivation and validation cohorts. See related Editorial