title: Modeling using clinical examination indicators predicts interstitial lung disease among patients with rheumatoid arthritis.
abstract: Interstitial lung disease (ILD) is a severe extra-articular manifestation of rheumatoid arthritis (RA) that is well-defined as a chronic systemic autoimmune disease. A proportion of patients with RA-associated ILD (RA-ILD) develop pulmonary fibrosis (PF), resulting in poor prognosis and increased lifetime risk. We investigated whether routine clinical examination indicators (CEIs) could be used to identify RA patients with high PF risk. A total of 533 patients with established RA were recruited in this study for model building and 32 CEIs were measured for each of them. To identify PF risk, a new artificial neural network (ANN) was built, in which inputs were generated by calculating Euclidean distance of CEIs between patients. Receiver operating characteristic curve analysis indicated that the ANN performed well in predicting the PF risk (Youden index = 0.436) by only incorporating four CEIs including age, eosinophil count, platelet count, and white blood cell count. A set of 218 RA patients with healthy lungs or suffering from ILD and a set of 87 RA patients suffering from PF were used for independent validation. Results showed that the model successfully identified ILD and PF with a true positive rate of 84.9% and 82.8%, respectively. The present study suggests that model integration of multiple routine CEIs contributes to identification of potential PF risk among patients with RA.
authors.affiliation: Department of Microbiology, Wu Lien-Teh Institute, Harbin Medical University, Harbin, Heilongjiang Province, China; Department of Microbiology and Immunology, School of Basic Medical Sciences, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang Province, China.
authors.forename: Yao
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Department of Microbiology, Wu Lien-Teh Institute, Harbin Medical University , Harbin , Heilongjiang Province , China.
authors.forename: Wuqi
authors.initials: W
authors.lastname: Song
authors.affiliation: Department of Microbiology, Wu Lien-Teh Institute, Harbin Medical University , Harbin , Heilongjiang Province , China.
authors.forename: Jing
authors.initials: J
authors.lastname: Wu
authors.affiliation: Department of Pharmacy Administration, Harbin Medical University , Harbin , Heilongjiang Province , China.
authors.forename: Zhangming
authors.initials: Z
authors.lastname: Li
authors.affiliation: Department of Laboratory Medicine, The Second Affiliated Hospital of Harbin Medical University , Harbin , Heilongjiang Province , China.
authors.forename: Fengyun
authors.initials: F
authors.lastname: Mu
authors.affiliation: Department of Rheumatology, The Second Affiliated Hospital of Harbin Medical University , Harbin , Heilongjiang Province , China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Li
authors.affiliation: Department of Rheumatology, The Second Affiliated Hospital of Harbin Medical University , Harbin , Heilongjiang Province , China.
authors.forename: He
authors.initials: H
authors.lastname: Huang
authors.affiliation: Institute of Clinical Pharmacology, The Second Affiliated Hospital of Harbin Medical University , Harbin , Heilongjiang Province , China.
authors.forename: Wenliang
authors.initials: W
authors.lastname: Zhu
authors.affiliation: Department of Microbiology, Wu Lien-Teh Institute, Harbin Medical University , Harbin , Heilongjiang Province , China.
authors.forename: Fengmin
authors.initials: F
authors.lastname: Zhang
keywords: Artificial neural network; Clinical examination indicator; Euclidean distance; Interstitial lung disease; Pulmonary fibrosis; Rheumatoid arthritis