Predicting autoimmune thyroiditis in primary Sjogren’s syndrome patients using a random forest classifier: a retrospective study

作者：Jing Wu, Jingyu Zhang, Wen-qi Xia, Yuening Kang, Ruyi Liao, Yuling Chen, Xiaomin Li, Ya Wen, Fan-xuan Meng, Liling Xu, Sheng-hui Wen, Huifen Liu, Yuanqing Li, Jieruo Gu, Qing Lv, Yongcheng Ren · 发表于：Arthritis Research & Therapy · 年份：2025 · DOI：10.1186/s13075-024-03469-5 · 被引用次数：3 · 研究领域：Salivary Gland Disorders and Functions、Thyroid Disorders and Treatments、Oral microbiology and periodontitis research

BACKGROUND: Primary Sjogren's syndrome (pSS) and autoimmune thyroiditis (AIT) share overlapping genetic and immunological profiles. This retrospective study evaluates the efficacy of machine learning algorithms, with a focus on the Random Forest Classifier, to predict the presence of thyroid-specific autoantibodies (TPOAb and TgAb) in pSS patients. METHODS: A total of 96 patients with pSS were included in the retrospective study. All participants underwent a complete clinical and laboratory evaluation. All participants underwent thyroid function tests, including TPOAb and TgAb, and were accordingly divided into positive and negative thyroid autoantibody groups. Four machine learning algorithms were then used to analyze the risk factors affecting patients with pSS with positive and negative for thyroid autoantibodies. RESULTS: The results indicated that the Random Forest Classifier algorithm (AUC = 0.755) outperformed the other three machine learning algorithms. The random forest classifier indicated Age, IgG, C4 and dry mouth were the main factors influencing the prediction of positive thyroid autoantibodies in pSS patients. It is feasible to predict AIT in pSS using machine learning algorithms. CONCLUSIONS: Analyzing clinical and laboratory data from 96 pSS patients, the Random Forest model demonstrated superior performance (AUC = 0.755), identifying age, IgG levels, complement component 4 (C4), and absence of dry mouth as primary predictors. This approach offers a promising...