Development of a Risk Nomogram Model for Identifying Interstitial Lung Disease in Patients With Rheumatoid Arthritis - Xue, Jing; Hu, Wenfeng; Wu, Shuang; Wang, Jing; Chi, Shuhong; Liu, Xiaoming (Frontiers in Immunology, 2022)
The clinical features of rheumatoid arthritis (RA)-associated ILD (RA-ILD) usually manifest to an advanced stage of lung disease, which leads the challenge of early diagnosis and the difficulty in guiding treatments for patients with RA-ILD in clinical settings. The aim of this study was to construct a nomogram for identifying ILD in RA patients. Through the incorporation of the level of matrix metalloproteinase-3 (MMP-3) in plasma, demographics, clinical feature and laboratory parameters of 223 RA patients (85 RA-ILD) those were grouped as training cohorts and validation cohorts, a identifying nomogram of RA-ILD was built. Candidate variables for the nomogram were screened using univariable analysis and multivariable logistic regression analysis. The accuracy of the diagnostic nomogram was measured via concordance index (C-index), calibration plots and decision curve analysis (DCA). Results showed that plasma MMP-3 protein was elevated in RA-ILD patients compared with non-ILD RA patients in both training cohorts (p=0.0475) and validation cohorts (p=0.0006). Following a final regression analysis, the gender of male, current smoking state, levels of circulating rheumatoid factor (RF), C-reactive protein (CRP) and MMP-3 were identified as risk factors for the construction of nomogram. The calibration plots further showed a favorable consistency between the identifying nomogram and actual clinical findings. In consistence, the C-index (0.826 for both training cohorts and validation cohorts) indicated satisfactory discriminative ability of the nomogram. Although the incorporation of MMP-3 failed to significantly improve identified outcomes of the nomogram as determined by DCA, including the level of circulating MMP-3 increased the diagnostic accuracy of nomogram for ILD in RA patients. Thus, our proposed model can serve as a non-invasive tool to identify ILD in RA patients, which may assist physicians to make treatment decisions for RA patients.