Modeling using clinical examination indicators predicts interstitial lung disease among patients with rheumatoid arthritis

PeerJ

article

Yao Wang¹  Wuqi Song¹  Jing Wu¹  Zhangming Li³  Fengyun Mu⁴  Yang Li⁵  He Huang⁵  Wenliang Zhu⁶  Fengmin Zhang¹ 

[1] Department of Microbiology, Wu Lien-Teh Institute, Harbin Medical University, Harbin, Heilongjiang Province;Department of Microbiology and Immunology, School of Basic Medical Sciences, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang Province;Department of Pharmacy Administration, Harbin Medical University, Harbin, Heilongjiang Province;Department of Laboratory Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province;Department of Rheumatology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province;Institute of Clinical Pharmacology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province

关键词: Clinical examination indicator;    Artificial neural network;    Interstitial lung disease;    Pulmonary fibrosis;    Rheumatoid arthritis;    Euclidean distance;   

DOI  :  10.7717/peerj.3021

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

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.

【 授权许可】

CC BY