Evidence map›Paper›PMID 41015965›Full record

ArticleTherapeutic advances in respiratory disease

A clinical predictive model for the long-term survival of progressive fibrosis interstitial lung disease patients.

Jin-Min Gu, Si-Yao Xiao, Bo-Tao Chen, Shao-Ting Kang, Wei-Chao Li, Mei-Yi Zhang, Yang Yu, Gui-Ling Han

Abstract readMulticenter StudyValidation Study
In one paragraph

Article in Therapeutic advances in respiratory disease. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Jin-Min GuInstitute of Clinical Medicine, Beijing University of Chinese Medicine, Beijing, China.
Si-Yao XiaoNational Center for Respiratory Medicine, Beijing, China.
Bo-Tao ChenInstitute of Clinical Medicine, Chongqing Jiulongpo District People's Hospital, Chongqing, China.
Shao-Ting KangInstitute of Clinical Medicine, Beijing University of Chinese Medicine, Beijing, China.
Wei-Chao LiInstitute of Clinical Medicine, Beijing University of Chinese Medicine, Beijing, China.
Mei-Yi ZhangInstitute of Clinical Medicine, Beijing University of Chinese Medicine, Beijing, China.
Yang YuNational Center for Respiratory Medicine, Beijing, China.
Gui-Ling HanNational Center for Respiratory Medicine, Beijing, China.ORCID 0009-0005-6381-1315

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn patients with chronic fibrosing interstitial lung disease (ILD), some may develop a progressive fibrosing (PF) phenotype, which presents as rapid progression and often results in poor clinical outcomes.

objectiveThe objective of this study was to construct and test a model to identify independent predictors of mortality in PF-ILD and to trace the lung function trajectory of patients with PF-ILD.

designThis multicenter retrospective cohort study enrolled patients with PF-ILD from two distinct centers with 8-year follow-up to develop and validate a prognostic nomogram based on clinical factors and assess longitudinal lung function trajectories.

methodsWe enrolled patients diagnosed with PF-ILD from China-Japan Friendship Hospital (training cohort) and Jiulongpo Hospital of Traditional Chinese Medicine (validation cohort). Survival status was recorded during the 8-year follow-up period. Clinical demographics, laboratory data, pulmonary function test (PFT) results, and high-resolution computed tomography results were collected for analysis. A training cohort of patients with PF-ILD was used to identify predictors of mortality, which were then validated in an external cohort. A nomogram was established based on multivariate factors. The predictive performance of the model was evaluated using receiver operating characteristic curves and calibration curves. Survival estimates were performed using the Kaplan-Meier method and compared using the log-rank test. The PFT trajectory was estimated using a linear mixed model.

resultsA total of 1419 patients with PF-ILD from China-Japan Friendship Hospital (training cohort) and 282 patients with PF-ILD from Jiulongpo Hospital of Traditional Chinese Medicine (validation cohort) were enrolled. During the 8-year follow-up, 150 (10.57%) patients received lung transplantation, while 43.55% (

conclusionA predictive model incorporating multiple factors effectively predicted 8-year survival in patients with PF-ILD. In addition to these baseline predictors, AEs and progressive declines in FVC and DLCO were strongly associated with poor prognosis and may serve as valuable longitudinal biomarkers for ongoing risk stratification.

Indexed as

Decision Support TechniquesLungLung Diseases, InterstitialNomogramsPulmonary FibrosisAgedChinaDisease ProgressionFemaleHumansMaleMiddle AgedPredictive Value of TestsPrognosisReproducibility of ResultsRespiratory Function Testslung function trajectorymortalitynomogramPF-ILDpredictive model

Identifiers

PMID41015965
PMCPMC12477370

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.