{"doi":"10.1111/1756-185x.70320","title":"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","abstract":"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.","journal":"International Journal of Rheumatic Diseases","year":2025,"id":536247,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9505,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":517364,"name":"You‐Jung Ha","orcid":"0000-0001-6107-9523","position":1,"is_corresponding":false},{"id":1070567,"name":"Jeong Seok Lee","orcid":"0000-0001-8261-7044","position":2,"is_corresponding":false},{"id":342501,"name":"Eun Young Lee","orcid":"0000-0001-6975-8627","position":3,"is_corresponding":false},{"id":348235,"name":"Jonathan Goldin","orcid":"0000-0003-3007-9947","position":4,"is_corresponding":false},{"id":348234,"name":"Grace Hyun J. Kim","orcid":"0000-0003-1225-3489","position":5,"is_corresponding":false},{"id":1421085,"name":"Jiahao Tian","orcid":"0000-0002-1657-1735","position":0,"is_corresponding":true}],"reference_count":43,"raw_metadata":null,"created_at":"2026-07-19T02:52:05.227140Z","pmid":"40488961","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}