Application of a Growth‐Rate Model to Enhance Subgroup Identification in Heterogeneous Clinical Courses of the Idiopathic Inflammatory Myopathy‐Associated Interstitial Lung Disease and Its Prognostic Implication

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BACKGROUND: Analyzing longitudinal real-world data with nonuniform study-time intervals is challenging. This study aimed to identify subgroups in heterogeneous clinical courses of idiopathic inflammatory myopathies-associated interstitial lung disease (IIM-ILD) using a growth rate model and to assess their prognostic significance. METHODS: In this retrospective cohort study, 243 chest high-resolution computed tomography (HRCT) scans from 80 patients with IIM-ILD were analyzed using a computer-aided quantification system to estimate quantitative lung fibrosis (QLF) scores. Longitudinal patterns were identified through a growth-rate model, and a landmark survival analysis was performed using the last HRCT date as an anchor. RESULTS: Using the growth-rate model, we identified five different patterns in the serial QLF scores: progressive (n = 19), improving (n = 20), convex (n = 10), others (mostly concave, n = 22), and stable (n = 9). When the group with the progressive pattern was divided into the rapid progression and slow progression by the median progression rate (g = 1.029%/month), the rapid progressive group was significantly associated with mortality (Hazard ratio 15.926, 95% confidence interval 1.079-548.324, p = 0.043), compared to the reference group. However, the intensity of immunosuppression or QLF scores at landmark time were not associated with mortality. CONCLUSION: Combined volumetric measurement of lung fibrosis and application of growth-rate model had the potential to identify subgroups in analyzing complex, dynamic real-world data of IIMs-ILD. This approach may help extrapolate the future course and provide useful information about prognosis in patients with ILD.