Decision tree analysis for evaluating disease activity in patients with rheumatoid arthritis
Lei Wang, Lifen Zhu, Jiahui Jiang, Lijuan Wang, Wanmao Ni
Abstract
OBJECTIVE: Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease characterized by inflammatory synovitis. We developed a new disease activity evaluation system using important cytokines to help doctors better evaluate disease activity in patients with RA. METHODS: Flow cytometry was used to detect the levels of seven cytokines. Then, the results were analyzed using an R language decision tree. RESULTS: The levels of six cytokines, namely interleukin (IL)-2, IL-4, IL-6, IL-10, tumor necrosis factor-α, and interferon-γ, were significantly different between the active disease and remission stages. Decision tree analysis of the six cytokines with statistical significance identified two judgment rules for the remission stage and three judgment rules for the active disease stage. CONCLUSION: We proposed the use of the decision tree method to analyze cytokine levels in patients with RA and obtain a more intuitive and objective RA disease activity scoring system. This method revealed the relationships of IL-6 and TNF-α levels with inflammatory characteristics in patients with RA, which can help predict disease activity.