Prediction of Tuberculosis Risk in the Elderly Population of Eastern China: Development and Validation of Multiple Machine Learning Models

Abstract

Xiaofei Yu,1,* Zhongqi Li,2,* Hongmei Guo,3 Xiu Chen,4 Hui Jiang5 1Department of Stomatology, Zhongda Hospital, Southeast University, Nanjing, Jiangsu, People’s Republic of China; 2Anhui Province Clinical Research Center for Critical Respiratory Medicine, the First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College), Wuhu, People’s Republic of China; 3Department of Chronic Communicable Disease, Disease Control and Prevention of Yangzhong City, Zhenjiang, Jiangsu Province, People’s Republic of China; 4Department of General Surgery, The First Affiliated Hospital with Nanjing Medical University, Nanjing, Jiangsu Province, People’s Republic of China; 5Department of Chronic Communicable Disease, Disease Control and Prevention of Zhenjiang City, Zhenjiang, Jiangsu Province, People’s Republic of China*These authors contributed equally to this workCorrespondence: Hui Jiang, Department of Chronic Communicable Disease, Disease Control and Prevention of Zhenjiang City, 9 Huangshan S Road, Zhenjiang, Jiangsu Province, 212000, People’s Republic of China, Tel +86-15358599683, Fax +86-511-84434786, Email Zjcdcjh@sina.com Xiu Chen, Department of General Surgery, The First Affiliated Hospital with Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, Jiangsu Province, 210029, People’s Republic of China, Email chenxiu@njmu.edu.cnBackground: Tuberculosis (TB) remains a significant public health burden among older adults, yet predictive tools for this population are limited. This study aimed to develop and validate machine learning models to predict TB risk among older adults in Eastern China.Methods: A prospective cohort of 33,935 participants aged ≥ 60 years was followed for over 8 years. TB diagnosis was confirmed through linkage with the national TB surveillance system. LassoCox regression was used to identify key predictors of TB risk. Four machine learning models—CoxBoost, Generalized Boosted Models (GBM), LassoCox, and Random Survival Forests (RSF)—were developed and compared. Model performance was evaluated using time-dependent area under the receiver operating characteristic curve (AUC), Brier score, and concordance index.Results: During follow-up, 387 participants developed TB, yielding an incidence rate of 134.5 per 100,000 person-years. The LassoCox model identified 14 predictors, including sex, alcohol consumption, dietary quality, body mass index, and C-reactive protein levels. Among the four models, the LassoCox model demonstrated the best discriminatory ability with an AUC of 0.717 (95% CI: 0.692– 0.742), followed by GBM (AUC: 0.712, 95% CI: 0.687– 0.737), CoxBoost (AUC: 0.708, 95% CI: 0.683– 0.733), and RSF (AUC: 0.637, 95% CI: 0.611– 0.663). The LassoCox model also demonstrated satisfactory calibration, with a Brier score of 0.338. Decision curve analysis confirmed clinical utility at threshold probabilities below 20%. Kaplan-Meier survival analysis showed significant differences between risk groups (log-rank P < 0.001), though survival curves revealed limited separation between low- and high-risk groups.Conclusion: The LassoCox model demonstrated acceptable predictive performance for TB risk in older Chinese adults. These findings suggest that machine learning-based risk prediction tools could facilitate targeted TB screening by identifying high-risk individuals in aging populations, thereby enabling more efficient allocation of screening resources and earlier intervention. However, further model refinement and external validation in diverse populations are warranted before clinical implementation.Plain Language Summary: Tuberculosis (TB) remains a serious health concern for older adults, particularly in countries like China. However, there are few tools available to predict who is most at risk. In this study, we followed over 33,000 adults aged 60 years and older in Eastern China for more than 8 years. We used computer-based methods called machine learning to build models that predict TB risk. Among the models tested, the LassoCox model performed best, correctly identifying individuals at higher risk. Key factors linked to TB risk included sex, alcohol use, diet quality, body weight, and markers of inflammation in the blood. Our findings suggest that these prediction tools could help doctors and public health officials identify older adults who might benefit most from TB screening. Further research is needed to improve these models and test them in other populations.Keywords: tuberculosis, elderly tuberculosis, incidence, risk factor, machine learning

Ähnliche Arbeiten