Multiplexed Plasma Immune Mediator Signatures Can Differentiate Sepsis From NonInfective SIRS: American Surgical Association 2020 Annual Meeting Paper.
Multiplexed Plasma Immune Mediator Signatures Can Differentiate Sepsis From NonInfective SIRS: American Surgical Association 2020 Annual Meeting Paper.
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多路复用血浆免疫介质特征可以将败血症与非感染的SIRS区分开：美国手术协会2020年会议论文。
DOI：
10.1097/sla.0000000000004379
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发表时间：
2020-10
影响因子：
9
通讯作者：
中科院分区：
文献类型：
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败血症和无菌都会释放“危险信号”，诱发全身炎症反应综合征（SIRS）。因此，区分感染和SIRS可能具有挑战性。精确的诊断分析可以限制不必要的抗生素使用，改善结果。在调查了人类白细胞细胞因子产生对无菌损伤相关分子模式（DAMPs）、细菌病原体相关分子模式和细菌的反应后，我们创建了31种细胞因子的多重检测。然后，我们研究了菌血症、感染性休克、“严重败血症”或创伤（ISS≥15伴循环DAMPs）患者以及对照组的血浆。根据住院后复查判定感染。使用单变量和多变量方法研究血浆感染和损伤，以确定多重检测方法如何最好地区分感染性SIRS和非感染性SIRS。与对照组相比，感染患者血浆白细胞介素(IL)-6、IL-1α和髓样细胞上触发受体-1 （TREM-1）表达较高[错误发现率(FDR) <0.01, <0.01, <0.0001]。相反，损伤抑制了许多介质，包括MDC （FDR <0.0001）、TREM-1 （FDR <0.001）、IP-10 （FDR <0.01）、MCP-3 （FDR <0.05）、FLT3L （FDR <0.05）、Tweak （FDR <0.05）、GRO-α (FDR <0.05)和ENA-78 （FDR <0.05）。在单变量研究中，临床组之间的分析物重叠妨碍了临床相关性。多变量模型对损伤和感染的区分效果较好，2组随机森林模型对11/11例损伤和28/29例感染患者进行了正确的分类。创伤性SIRS的循环细胞因子明显不同于健康或败血症。可变性限制了单介质测定的准确性，但基于多介质血浆测定的机器学习揭示了败血症和损伤相关SIRS的不同模式。定义生物标志物释放模式，区分特定SIRS人群，可能会减少抗生素在这些临床情况下的使用。需要大规模的前瞻性研究来验证和操作这种方法。
Sepsis and sterile both release “danger signals" that induce the systemic inflammatory response syndrome (SIRS). So differentiating infection from SIRS can be challenging. Precision diagnostic assays could limit unnecessary antibiotic use, improving outcomes. After surveying human leukocyte cytokine production responses to sterile damage-associated molecular patterns (DAMPs), bacterial pathogen-associated molecular patterns, and bacteria we created a multiplex assay for 31 cytokines. We then studied plasma from patients with bacteremia, septic shock, “severe sepsis,” or trauma (ISS ≥15 with circulating DAMPs) as well as controls. Infections were adjudicated based on post-hospitalization review. Plasma was studied in infection and injury using univariate and multivariate means to determine how such multiplex assays could best distinguish infective from noninfective SIRS. Infected patients had high plasma interleukin (IL)-6, IL-1α, and triggering receptor expressed on myeloid cells-1 (TREM-1) compared to controls [false discovery rates (FDR) <0.01, <0.01, <0.0001]. Conversely, injury suppressed many mediators including MDC (FDR <0.0001), TREM-1 (FDR <0.001), IP-10 (FDR <0.01), MCP-3 (FDR <0.05), FLT3L (FDR <0.05), Tweak, (FDR <0.05), GRO-α (FDR <0.05), and ENA-78 (FDR <0.05). In univariate studies, analyte overlap between clinical groups prevented clinical relevance. Multivariate models discriminated injury and infection much better, with the 2-group random-forest model classifying 11/11 injury and 28/29 infection patients correctly in out-of-bag validation. Circulating cytokines in traumatic SIRS differ markedly from those in health or sepsis. Variability limits the accuracy of single-mediator assays but machine learning based on multiplexed plasma assays revealed distinct patterns in sepsis- and injury-related SIRS. Defining biomarker release patterns that distinguish specific SIRS populations might allow decreased antibiotic use in those clinical situations. Large prospective studies are needed to validate and operationalize this approach.