Using Machine Learning Methods to Predict Early Treatment Outcomes for Multidrug-Resistant or Rifampicin-Resistant Tuberculosis to Enhance Patient Cure Rates: Development and Validation of Multiple Models

作者：Fuzhen Zhang, Zi-Long Yang, Xiaonan Geng, Yu Dong, Shanshan Li, Cong Yao, Yuanyuan Shang, Weicong Ren, Ruichao Liu, Haobin Kuang, Liang Li, Yu Pang · 发表于：Journal of Medical Internet Research · 年份：2025 · DOI：10.2196/69998 · 被引用次数：7 · 研究领域：Tuberculosis Research and Epidemiology、Image Processing Techniques and Applications、Diagnosis and treatment of tuberculosis

BACKGROUND: Early prediction of treatment outcomes for patients with multidrug-resistant or rifampicin-resistant tuberculosis (MDR/RR-TB) undergoing extended therapy is crucial for enhancing clinical prognoses and preventing the transmission of this deadly disease. However, the absence of validated predictive models remains a significant challenge. OBJECTIVE: This study compared a conventional logistic regression model with machine learning (ML) models using demographic and clinical data to predict outcomes at 2 and 6 months of treatment for MDR/RR-TB. The goal was to advance model applications, refine control strategies, and boost MDR/RR-TB cure rates. METHODS: This retrospective study encompassed an internal cohort of 744 patients with MDR/RR-TB examined between January 2017 and June 2023, as well as an external cohort comprising 137 patients with MDR/RR-TB examined between March 2021 and June 2022. Data on culture conversion were collected at 2 and 6 months, and culture conversion was tracked in the external cohort at the same time points. The internal cohort was assigned as the training set, whereas the external cohort was used as the validation set. Logistic regression and 7 ML models were developed to predict the culture conversion of patients with MDR/RR-TB at 2 and 6 months of treatment. Model performance was evaluated using the area under the curve, accuracy, sensitivity, and specificity. RESULTS: In the internal cohort, culture conversion rates for MDR/RR-TB were 81...