Marambire Edson Tawanda, Calderwood Claire J, Larsson Leyla, Held Kathrin, Khan Palwasha, Banze Denise, Nhamuave Celina, Minja Lillian T, Mfinanga Alfred, Gupta Rishi K, Khosa Celso, Mutsvangwa Junior, Heinrich Norbert, Kranzer Katharina
The Health Research Unit Zimbabwe, Biomedical Research and Training Institute, Harare, Zimbabwe.
CIHLMU Center for International Health, University Hospital, LMU Munich, Germany.
PLOS Glob Public Health. 2025 Mar 31;5(3):e0004340. doi: 10.1371/journal.pgph.0004340. eCollection 2025.
Tuberculosis household contacts are at high risk of developing tuberculosis. Tuberculosis preventive therapy (TPT) is highly effective, but implementation is hindered by limited accessibility of diagnostic tests aimed at detecting Mycobacterium tuberculosis (Mtb) infection. Development of Mtb infection prediction models to guide clinical decision-making aims to overcome these challenges. We used data from 1905 tuberculosis household contacts (age ≥10 years) from Zimbabwe, Mozambique and Tanzania to develop two prediction models for Mtb infection determined by interferon-gamma release assay (IGRA) using logistic regression with backward elimination and cross-validation and converted these into a risk score. Model performance was assessed using area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. We developed a basic model with six predictors (age, caregiver role, index case symptom duration, index HIV status, household crowding, and index GeneXpert MTB/Rif results) and a comprehensive model with eleven predictors. The basic and comprehensive risk scores showed limited predictive capability (AUROC 0.592, sensitivity 76%, specificity 35% and AUROC 0.586, sensitivity 76%, specificity 36% respectively), with considerable overlap across IGRA-positive and -negative individuals. Neither model conferred net benefit over a treat-all strategy. Overall, our results suggest that the prediction models developed in this study do not add value for guiding TPT use in high-tuberculosis burden settings. This likely reflects complex Mtb transmission dynamics at the household- and community-level, variation in individual-level susceptibility and immune response, as well as limited accuracy of IGRA testing. Improved diagnostics to determine Mtb infection status in terms of ease-of-use, accuracy, and costs are needed.
结核病家庭接触者患结核病的风险很高。结核病预防性治疗（TPT）非常有效，但针对检测结核分枝杆菌（Mtb）感染的诊断测试可及性有限，阻碍了其实施。开发Mtb感染预测模型以指导临床决策旨在克服这些挑战。我们使用了来自津巴布韦、莫桑比克和坦桑尼亚的1905名结核病家庭接触者（年龄≥10岁）的数据，通过采用向后消除和交叉验证的逻辑回归方法，开发了两个用于通过干扰素-γ释放试验（IGRA）确定Mtb感染的预测模型，并将其转换为风险评分。使用受试者工作特征曲线下面积（AUROC）、敏感性和特异性评估模型性能。我们开发了一个包含六个预测因素（年龄、照顾者角色、索引病例症状持续时间、索引HIV状态、家庭拥挤程度以及索引GeneXpert MTB/Rif结果）的基本模型和一个包含十一个预测因素的综合模型。基本风险评分和综合风险评分显示出有限的预测能力（AUROC分别为0.592、敏感性为76%、特异性为35%以及AUROC为0.586、敏感性为76%、特异性为36%），IGRA阳性和阴性个体之间存在相当大的重叠。两个模型都没有比全面治疗策略带来净益处。总体而言，我们的结果表明，本研究中开发的预测模型在高结核病负担环境中指导TPT使用方面没有增加价值。这可能反映了家庭和社区层面复杂的Mtb传播动态、个体层面易感性和免疫反应的差异，以及IGRA检测的有限准确性。需要改进诊断方法，以在易用性、准确性和成本方面确定Mtb感染状态。
