The value of serum biomarkers in the management of interstitial lung disease in patients with connective tissue disease: a protocol for an observational study
Background
Three key points in managing interstitial lung disease (ILD) in patients with connective tissue disease (CTD) are: First, identifying risk markers or factors for ILD in CTD patients without ILD. Second, diagnosing ILD in CTD patients poses uncertainty about the diagnosis of ILD. Third, predicting treatment response and outcomes of CTD patients with ILD. The current management approaches largely depend on lung biopsy or high-resolution computed tomography (HRCT), both of which have limitations in terms of invasiveness and radiation exposure. Serum biomarkers offer the advantages of non-invasiveness, low cost, radiation-free exposure, and thus represent a potential tool for managing ILD.
Methods
Here, we plan to conduct a prospective study to evaluate the value of serum biomarkers for predicting ILD risk, diagnosing ILD, assessing ILD prognosis, and predicting ILD treatment responsiveness in patients with CTD. We named this study “The value of serum biomarkers in diagnosing interstitial lung disease in connective tissue disease: a prospective diagnostic accuracy study” (VELD study). We will prospectively enroll CTD patients with uncertainty regarding ILD who visit the Departments of Rheumatology and Immunology at The Affiliated Hospital of Inner Mongolia Medical University between 2026 and 2028. Their serum specimens will be collected and stored at −80 ℃ for laboratory analyses, including biomarker screening and validation. The predictive and diagnostic values of serum biomarkers for ILD will be estimated using receiver operating characteristic (ROC) curve, decision curve analysis (DCA), logistic regression, net reclassification index (NRI), and integrated discrimination index (IDI).
Discussion
The VELD study will provide a novel insight into the clinical role of serum biomarkers in the management of CTD-associated ILD.
Trial Registration
chictr.org.cn (ChiCTR2600120197).








