Department of Paediatrics, Centre for Clinical Vaccinology and Tropical Medicine, Oxford Vaccine Group, Oxford, UK.
Oxford National Institute of Health Research Biomedical Centre, University of Oxford, Oxford, UK.
EMBO Mol Med. 2019 Oct;11(10):e10431. doi: 10.15252/emmm.201910431. Epub 2019 Aug 30.
Misdiagnosis of enteric fever is a major global health problem, resulting in patient mismanagement, antimicrobial misuse and inaccurate disease burden estimates. Applying a machine learning algorithm to host gene expression profiles, we identified a diagnostic signature, which could distinguish culture-confirmed enteric fever cases from other febrile illnesses (area under receiver operating characteristic curve > 95%). Applying this signature to a culture-negative suspected enteric fever cohort in Nepal identified a further 12.6% as likely true cases. Our analysis highlights the power of data-driven approaches to identify host response patterns for the diagnosis of febrile illnesses. Expression signatures were validated using qPCR, highlighting their utility as PCR-based diagnostics for use in endemic settings.
肠热病的误诊是一个全球性的主要卫生问题，导致患者处理不当、抗菌药物滥用和疾病负担估计不准确。我们应用机器学习算法对宿主基因表达谱进行分析，确定了一个诊断特征，可以将培养确诊的肠热病病例与其他发热性疾病区分开来（接受者操作特征曲线下面积>95%）。将该特征应用于尼泊尔培养阴性疑似肠热病队列中，进一步确定了 12.6%的可能真正病例。我们的分析强调了数据驱动方法在识别发热性疾病宿主反应模式方面的强大功能。使用 qPCR 验证了表达谱，突出了它们作为基于 PCR 的诊断在流行地区使用的效用。
