Evidence map›Paper›PMID 41905888›Full record

ArticleZhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences2026

[Development and validation of a risk assessment model for interstitial lung disease in patients with rheumatoid arthritis].

Xiuyuan Xu, Dan Liang, Weiwei Ye, Mengru Guo, Jianwei Xiao, Rongsheng Wang, Dongyi He

Abstract readValidation StudyEnglish Abstract
In one paragraph

Article in Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences, 2026. 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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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Xiuyuan XuDepartment of Rheumatology and Immunology, Guanghua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai 200052, China. xiuyuanx@126.com.
Dan LiangDepartment of Rheumatology and Immunology, Guanghua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai 200052, China.
Weiwei YeDepartment of Endocrinology, Dahua Hospital, Shanghai 200237, China.
Mengru GuoDepartment of Rheumatology and Immunology, Guanghua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai 200052, China.
Jianwei XiaoDepartment of Rheumatology and Immunology, Futian District Rheumatology Hospital, Shenzhen 518000, Guangdong Province, China.
Rongsheng WangDepartment of Rheumatology and Immunology, Guanghua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai 200052, China. rowsonwang@126.com.
Dongyi HeDepartment of Rheumatology and Immunology, Guanghua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai 200052, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo develop and validate a risk assessment model for interstitial lung disease (ILD) in patients with rheumatoid arthritis (RA).

methodsA retrospective study analyzed clinical data from 312 patients with RA treated at Guanghua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine between January 1, 2021 and June 30, 2024. Patients were divided into an RA-only group and an RA-ILD group based on the presence of ILD. Demographic characteristics, laboratory test results, clinical manifestations, disease activity scores, medication history, joint X-ray findings, and traditional Chinese medicine (TCM) syndrome types were collected as potential predictors. Variables were screened using univariable analysis and LASSO regression, and binary logistic regression was used to build the model and construct a nomogram using the rms package in R software. The discrimination and clinical utility of this model were assessed via receiver operating characteristic (ROC) and decision curves, and calibration was evaluated using Bootstrap-resampled (1000 iterations) calibration curves. A LightGBM machine learning model was also developed based on the selected predictors, and its performance was evaluated using ROC and precision-recall (PR) curves. Five-fold cross-validation was employed to further assess the robustness of the predictors.

resultsMultivariate logistic regression identified sex, age, anti-cyclic citrullinated peptide (CCP) antibody, disease activity score in 28 joints (DAS28) based on erythrocyte sedimentation rate (ESR), Krebs von den Lungen-6 (KL-6), international normalized ratio (INR), activated partial thromboplastin time (APTT), and methotrexate use as independent predictors of RA-ILD (all

conclusionsThe developed risk assessment model for ILD in patients with RA indicates high predictive ability and clinical utility.

Indexed as

Arthritis, RheumatoidLung Diseases, InterstitialBoosting Machine Learning AlgorithmsFemaleHumansLogistic ModelsMaleMedicine, Chinese TraditionalMiddle AgedNomogramsRetrospective StudiesRisk AssessmentROC CurveInterstitial lung diseaseMachine learning algorithmNomogramsRheumatoid arthritisRisk assessment model

Identifiers

PMID41905888
PMCPMC13154139

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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.