ArticleBMC pulmonary medicine2025
A multi-biomarker machine learning approach for early prediction of interstitial lung disease in rheumatoid arthritis.
Article in BMC pulmonary medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Detection of rheumatoid arthritis-associated interstitial lung disease: a systematic review and meta-analysis.BMC pulmonary medicine · 2026Pooled it
- A machine learning-based classification model for interstitial lung disease in rheumatoid arthritis.Frontiers in medicine · 2026Trial
- Augmented Anti-Bactericidal Permeability-Increasing Protein Antibody Levels in Rheumatoid Arthritis Patients Complicated by Usual Interstitial Pneumonia.Journal of clinical medicine · 2026Article
- Recent Knowledge Regarding the Epidemiology, Exacerbating Factors, and Treatment of Rheumatoid Arthritis Complicated by Interstitial Lung Disease.Journal of clinical medicine · 2026Review
- Advances in the Diagnosis of Rheumatoid Arthritis-Associated Interstitial Lung Disease: Integrating Conventional Tools and Emerging Biomarkers.International journal of molecular sciences · 2026Review
- Correlation of a combined serum biomarker panel with the composite physiologic index in connective tissue disease-associated interstitial lung disease.Frontiers in physiology · 2026Article
- Development and validation of machine learning models based on blood routine tests and tumor markers in early screening of primary bronchogenic lung cancer.Translational lung cancer research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundInterstitial lung disease (ILD) is a severe complication affecting 10-30% of rheumatoid arthritis (RA) patients. Current diagnostic methods typically detect ILD only after substantial lung damage has occurred. This delay emphasizes the need for early detection strategies. This study aims to develop and validate machine learning models for early RA-ILD prediction and identify key predictive biomarkers.
methodsWe conducted a cross-sectional study enrolling 149 RA patients (84 with ILD, 65 without ILD) between January 2020 and December 2023. We evaluated demographic characteristics, clinical parameters, and laboratory markers, including inflammatory indicators, hematological parameters, and specific biomarkers. We developed and compared four machine learning (ML) models (XGBoost, Random Forest, Support Vector Machine, and Logistic Regression) for ILD prediction capabilities.
resultsThe XGBoost model demonstrated superior predictive performance (AUC = 0.891, 95% CI: 0.847-0.935). Feature importance analysis identified Krebs von den Lungen-6 (KL-6) as the strongest predictor (importance score = 0.285), followed by interleukin-6 (IL-6) and cytokeratin 19 fragment (CYFRA21-1). The ILD group exhibited significantly elevated levels of inflammatory markers and specific biomarkers, particularly KL-6 (826.4 ± 458.2 vs. 285.6 ± 124.8 U/ml, P < 0.001), alongside distinct patterns in hematological parameters.
conclusionMachine learning approaches, particularly XGBoost, demonstrate promising potential for early RA-ILD prediction. The integration of KL-6 and other identified biomarkers into clinical screening protocols may facilitate early detection and improved patient outcomes. These findings suggest that machine learning models could serve as valuable tools for risk stratification and early intervention in RA-ILD management, providing new approaches for individualized risk assessment in clinical practice.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.