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Updated: Jan 18, 2026

Imaging Features of Systemic Sclerosis-Associated Interstitial Lung Disease
Published on: June 16, 2020
Predicting trajectories of lung function decline in systemic sclerosis-related interstitial lung disease
Boyang Zheng1, Mandana Nikpour2,3, Wendy Stevens4
1Division of Rheumatology, University of British Columbia, Vancouver, BC, Canada.
Objective:
SSc-related interstitial lung disease (SSc-ILD) is a major cause of morbidity. We aimed to identify patients following similar trajectories of forced vital capacity (FVC) decline, and examine their association with mortality and risk factors for FVC decline.
Methods:
This is a multicentre retrospective study of 444 SSc patients with ILD and ≤7-year disease duration. Patients were grouped based on similar FVC decline trajectories using semi-parametric modelling with latent class analysis. Survival was compared between the worst FVC trajectory group and the others. Logistic regression models with backwards selection were applied to identify predictors of FVC trajectory using baseline disease features.
Results:
Four FVC trajectory groups were identified. The most progressive trajectory declined by -2.18% per year and the other three trajectory groups were stable or progressed slowly. The most progressive group had a higher mortality rate than those with a stable/slow FVC trajectory (hazard ratio 2.95, 95% CI 1.74, 4.98). Baseline FVC (P < 0.001) and CRP elevation (P = 0.039) were associated the progressive trajectory. Baseline FVC ≤72% predicted the progressive trajectory with a sensitivity of 0.88 and specificity of 0.91. A lower baseline FVC was in turn associated with older age, Caucasian race, longer disease duration, anti-topoisomerase I presence and elevated CRP on exploratory analyses.
Conclusion:
Distinct FVC trajectories are associated with different survival outcomes and the most important predictor of a progressive FVC trajectory was existing ILD severity. More work is needed to assess the utility of imaging or paraclinical findings that can improve prediction of distinct FVC trajectories.
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