Oxford Vaccine Group, Department of Paediatrics, University of Oxford, Oxford, UK; Department of Paediatrics, Maastricht University Medical Centre, MosaKids Children's Hospital, Maastricht, the Netherlands.
Division of Global HIV and Tuberculosis, Centers for Disease Control and Prevention, Atlanta, USA; Department of Health Policy and Management, Yale University School of Public Health, New Haven, CT, USA.
Tuberculosis (Edinb). 2024 Dec;149:102566. doi: 10.1016/j.tube.2024.102566. Epub 2024 Sep 10.
The diagnosis of paediatric pulmonary tuberculosis is difficult, especially in young infants who cannot expectorate sputum spontaneously. Breath testing has shown promise in diagnosing respiratory tract infections, but data on paediatric tuberculosis are limited. We performed a prospective cross-sectional study in Kenya in children younger than five years with symptoms of tuberculosis. We analysed exhaled breath with a hand-held battery-powered nose device. For data analysis, machine learning was applied using samples classified as positive (microbiologically confirmed) or negative (unlikely tuberculosis) to assess diagnostic accuracy. Breath analysis was performed in 118 children. The area under the curve of the optimal model was 0.73. At a sensitivity of 86 % (CI 62-96 %), this resulted in a specificity of 42 % (95 % CI 30-55 %). Exhaled breath analysis shows promise as a triage test for TB in young children, although the WHO target product characteristics were not met.
儿童肺结核的诊断较为困难，尤其是无法自主咳痰的婴幼儿。呼吸测试在诊断呼吸道感染方面显示出了一定的前景，但关于儿童肺结核的数据有限。我们在肯尼亚开展了一项针对五岁以下有肺结核症状儿童的前瞻性横断面研究。我们使用手持式电池供电的鼻腔设备分析呼气样本。为了进行数据分析，我们采用了机器学习方法，使用经微生物学确认的阳性样本（确诊肺结核）和不太可能是肺结核的阴性样本进行分类，以评估诊断准确性。对 118 名儿童进行了呼吸分析。最佳模型的曲线下面积为 0.73。在敏感性为 86%（62-96% CI）的情况下，特异性为 42%（95% CI 30-55%）。呼气分析显示出作为儿童结核病筛查测试的潜力，尽管尚未达到世卫组织目标产品特性。
