Early differential diagnosis models of Talaromycosis and Tuberculosis in HIV-negative hosts using clinical data and machine learning

作者：Ye Qiu, Zhengtu Li, Shi‐Xiong Yang, Wu-shu Chen, Yong Zhang, Qun-yu Kong, L. Chen, Jie Huang, Lin Lü, Kan Xie, Wen Zeng, Shaoqiang Li, Yangqing Zhan, Yan Wang, Jianquan Zhang, Feng Ye · 发表于：Journal of Infection and Public Health · 年份：2025 · DOI：10.1016/j.jiph.2025.102740 · 被引用次数：4 · 研究领域：Infectious Diseases and Tuberculosis、Orthopedic Infections and Treatments、Fungal Infections and Studies

BACKGROUND: Talaromyces marneffei is an emerging pathogen, and the number of infections in HIV-negative individuals is increasing. In HIV-negative individuals, talaromycosis is usually misdiagnosed as another disease, especially tuberculosis (TB). METHODS: We retrospectively extracted the clinical data of HIV-negative patients with Talaromyces marneffei infection from 2018 to 2023, analyzed the differences between TB patients and talaromycosis patients and attempted to establish differential diagnosis models utilizing clinical prediction models for these two diseases. RESULTS: Overall, 718 patients, including 137 patients with talaromycosis and 581 patients with pulmonary tuberculosis (PTB), were enrolled in this study. According to the multivariate analysis, age > 65 years, expectoration, and PLT count were independent predictors for TB. Fever, chest pain, gasping, rash, lymphadenectasis, osteolysis, Neu count, EOS count, and ALB were independent predictors for talaromycosis. Receiver operating characteristic (ROC) curve analysis of the training set showed that the area under the curve (AUC) (95 % CI) of the clinical differential model based on logistic regression analysis was 0.918 (0.884-0.953). The model was verified in the validation set. ROC curve analysis of the validation set showed that the AUC (95 % CI) was 0.900 (0.841-0.959). CONCLUSION: These new differential diagnosis models can calculate the probability of either talaromycosis or tuberculosis.