Division of General Internal Medicine, Department of Medicine, Duke Regional Hospital, Duke University Health System, Duke University School of Medicine, 3643 N. Roxboro St., Durham, NC, 27704, USA.
Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA.
Sci Rep. 2023 Dec 18;13(1):22554. doi: 10.1038/s41598-023-49734-6.
Diagnostic limitations challenge management of clinically indistinguishable acute infectious illness globally. Gene expression classification models show great promise distinguishing causes of fever. We generated transcriptional data for a 294-participant (USA, Sri Lanka) discovery cohort with adjudicated viral or bacterial infections of diverse etiology or non-infectious disease mimics. We then derived and cross-validated gene expression classifiers including: 1) a single model to distinguish bacterial vs. viral (Global Fever-Bacterial/Viral [GF-B/V]) and 2) a two-model system to discriminate bacterial and viral in the context of noninfection (Global Fever-Bacterial/Viral/Non-infectious [GF-B/V/N]). We then translated to a multiplex RT-PCR assay and independent validation involved 101 participants (USA, Sri Lanka, Australia, Cambodia, Tanzania). The GF-B/V model discriminated bacterial from viral infection in the discovery cohort an area under the receiver operator curve (AUROC) of 0.93. Validation in an independent cohort demonstrated the GF-B/V model had an AUROC of 0.84 (95% CI 0.76-0.90) with overall accuracy of 81.6% (95% CI 72.7-88.5). Performance did not vary with age, demographics, or site. Host transcriptional response diagnostics distinguish bacterial and viral illness across global sites with diverse endemic pathogens.
诊断局限性挑战全球临床表现相似的急性传染病的管理。基因表达分类模型在区分发热原因方面显示出巨大的潜力。我们生成了一个由 294 名参与者（来自美国、斯里兰卡）组成的发现队列的转录组数据，这些参与者的感染病因多样，包括病毒或细菌感染以及非传染性疾病的类似物。然后，我们推导出并交叉验证了基因表达分类器，包括：1） 一个用于区分细菌与病毒的单一模型（全球发热-细菌/病毒 [GF-B/V]）和 2） 一个用于在非感染背景下区分细菌和病毒的双模型系统（全球发热-细菌/病毒/非传染性 [GF-B/V/N]）。然后，我们将其转化为多重 RT-PCR 检测，独立验证涉及 101 名参与者（来自美国、斯里兰卡、澳大利亚、柬埔寨和坦桑尼亚）。在发现队列中，GF-B/V 模型区分细菌和病毒感染的曲线下面积（AUROC）为 0.93。在独立队列中的验证表明，GF-B/V 模型的 AUROC 为 0.84（95%CI 0.76-0.90），整体准确率为 81.6%（95%CI 72.7-88.5）。性能不受年龄、人口统计学特征或地点的影响。宿主转录反应诊断可区分全球不同流行病原体地区的细菌和病毒疾病。
