Novel Host Response-Based Diagnostics to Differentiate the Etiology of Fever in Patients Presenting to the Emergency Department.
Novel Host Response-Based Diagnostics to Differentiate the Etiology of Fever in Patients Presenting to the Emergency Department.
复制标题
新型基于宿主反应的诊断方法，用于区分急诊科患者发热的病因。
DOI：
10.3390/diagnostics13050953
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发表时间：
2023-03-02
期刊：
影响因子：
3.6
通讯作者：
Mansour, Michael K. K.
中科院分区：
文献类型：
--
作者：
Atallah, Johnny;Ghebremichael, Musie;Timmer, Kyle D. D.;Warren, Hailey M. M.;Mallinger, Ella;Wallace, Ellen;Strouts, Fiona R. R.;Persing, David H. H.;Mansour, Michael K. K.
发热是急诊常见症状，与多种疾病进程相关。为快速确定发热病因，有必要改进诊断方法。这项前瞻性研究纳入了100名住院发热患者（按感染状态分为感染阳性（FP）和感染阴性（FN）两组）以及22名健康对照（HC）。我们评估了一种基于聚合酶链反应（PCR）的新型检测方法的性能，该方法可直接从全血中检测五种宿主信使核糖核酸（mRNA）转录本，以区分感染性与非感染性发热综合征，并与传统基于病原体的微生物学检测结果进行对比。在FP组和FN组中观察到了一个强大的网络结构，这五个基因之间存在显著相关性。感染阳性状态与其中四个基因存在统计学显著关联：干扰素调节因子9（IRF - 9，优势比（OR） = 1.750，95%置信区间（CI） = 1.16 - 2.638）、整合素αM（ITGAM，OR = 1.533，95% CI = 1.047 - 2.244）、脯氨酸丝氨酸苏氨酸相互作用蛋白2（PSTPIP2，OR = 2.191，95% CI = 1.293 - 3.711）以及RUNX1（OR = 1.974，95% CI = 1.069 - 3.646）。我们开发了一个分类模型，基于这五个基因及其他相关变量对研究参与者进行分类，以评估这些基因的鉴别能力。该分类模型能将超过80%的参与者正确归类到相应组别，即FP组或FN组。GeneXpert原型有望为快速临床决策提供指导，降低医疗成本，并改善因不明原因发热前来急诊评估患者的治疗效果。
Fever is a common presentation to urgent-care services and is linked to multiple disease processes. To rapidly determine the etiology of fever, improved diagnostic modalities are necessary. This prospective study of 100 hospitalized febrile patients included both positive (FP) and negative (FN) subjects in terms of infection status and 22 healthy controls (HC). We evaluated the performance of a novel PCR-based assay measuring five host mRNA transcripts directly from whole blood to differentiate infectious versus non-infectious febrile syndromes as compared to traditional pathogen-based microbiology results. The FP and FN groups observed a robust network structure with a significant correlation between the five genes. There were statistically significant associations between positive infection status and four of the five genes: IRF-9 (OR = 1.750, 95% CI = 1.16–2.638), ITGAM (OR = 1.533, 95% CI = 1.047–2.244), PSTPIP2 (OR = 2.191, 95% CI = 1.293–3.711), and RUNX1 (OR = 1.974, 95% CI = 1.069–3.646). We developed a classifier model to classify study participants based on these five genes and other variables of interest to assess the discriminatory power of the genes. The classifier model correctly classified more than 80% of the participants into their respective groups, i.e., FP or FN. The GeneXpert prototype holds promise for guiding rapid clinical decision-making, reducing healthcare costs, and improving outcomes in undifferentiated febrile patients presenting for urgent evaluation.
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