Bartl Lena, Zeeb Marius, Kälin Marisa, Loosli Tom, Notter Julia, Furrer Hansjakob, Hoffmann Matthias, Hirsch Hans H, Zangerle Robert, Grabmeier-Pfistershammer Katharina, Knappik Michael, Calmy Alexandra, Fernandez Jose Damas, Labhardt Niklaus D, Bernasconi Enos, Günthard Huldrych F, Kouyos Roger D, Kusejko Katharina, Nemeth Johannes
Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland.
Institute of Medical Virology, University of Zurich, Zurich, Switzerland.
Clin Infect Dis. 2025 Oct 6;81(3):521-530. doi: 10.1093/cid/ciaf149.
Coinfections of Mycobacterium tuberculosis (MTB) and human immunodeficiency virus (HIV) impose a substantial global health burden. Patients with MTB infection face a heightened risk of progression to incident active TB, which preventive therapy can mitigate. Current testing methods often fail to identify individuals who subsequently develop incident active TB.
We developed random forest models to predict incident active TB using patients' medical data at HIV-1 diagnosis. Training our model involved using clinical data routinely collected at enrollment from the Swiss HIV Cohort Study (SHCS). This dataset encompassed 55 people with HIV (PWH) who developed incident active TB 6 months after enrollment and 1432 matched PWH without TB enrolled between 2000 and 2023. External validation used data from the Austrian HIV Cohort Study, comprising 43 people with incident active TB and 1005 people without TB.
We predicted incident active TB with an area under the receiver operating characteristic curve of 0.83 (95% CI: .8-.86) in the SHCS. After adjusting for ethnicity and the region of origin and refitting the model with fewer parameters, we obtained comparable receiver operating characteristic curve values of 0.72 (SHCS) and 0.67 (Austrian HIV Cohort Study). Our model outperformed the standard of care (tuberculin skin test and interferon-gamma release assay) in identifying high-risk patients, demonstrated by a lower number needed to diagnose (1.96 vs 4).
Models based on machine learning offer considerable promise for improving care for PWH, requiring no additional data collection and incurring minimal additional costs while enhancing the identification of PWH that could benefit from preventive TB treatment.
结核分枝杆菌（MTB）与人类免疫缺陷病毒（HIV）的合并感染给全球健康带来了沉重负担。MTB感染患者发展为活动性结核病的风险更高，预防性治疗可以降低这种风险。目前的检测方法往往无法识别出后续发展为活动性结核病的个体。
我们开发了随机森林模型，利用患者在HIV-1诊断时的医疗数据来预测活动性结核病的发生。训练我们的模型使用了瑞士HIV队列研究（SHCS）在入组时常规收集的临床数据。该数据集包括55名在入组后6个月发生活动性结核病的HIV感染者（PWH）以及1432名在2000年至2023年期间入组的匹配的未患结核病的PWH。外部验证使用了奥地利HIV队列研究的数据，包括43名发生活动性结核病的患者和1005名未患结核病的患者。
在SHCS中，我们预测活动性结核病发生的受试者工作特征曲线下面积为0.83（95%CI：0.8 - 0.86）。在调整种族和原籍地区并使用较少参数重新拟合模型后，我们在SHCS中获得了0.72的可比受试者工作特征曲线值，在奥地利HIV队列研究中为0.67。在识别高危患者方面，我们的模型优于标准治疗方法（结核菌素皮肤试验和干扰素-γ释放试验），诊断所需人数更低（分别为1.96和4）。
基于机器学习的模型在改善PWH的护理方面具有很大潜力，无需额外的数据收集，成本增加极少，同时能更好地识别可从预防性结核病治疗中受益的PWH。
