Department of Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI), 07745 Jena, Germany.
Department of Anesthesiology and Intensive Care Medicine, Jena University Hospital, 07747 Jena, Germany; Center for Sepsis Control and Care, Jena University Hospital, 07747 Jena, Germany.
Cell Rep Med. 2024 Sep 17;5(9):101712. doi: 10.1016/j.xcrm.2024.101712. Epub 2024 Sep 3.
Infection is a commonplace, usually self-limiting, condition but can lead to sepsis, a severe life-threatening dysregulated host response. We investigate the individual phenotypic predisposition to developing uncomplicated infection or sepsis in a large cohort of non-infected patients undergoing major elective surgery. Whole-blood RNA sequencing analysis was performed on preoperative samples from 267 patients. These patients developed postoperative infection with (n = 77) or without (n = 49) sepsis, developed non-infectious systemic inflammatory response (n = 31), or had an uncomplicated postoperative course (n = 110). Machine learning classification models built on preoperative transcriptomic signatures predict postoperative outcomes including sepsis with an area under the curve of up to 0.910 (mean 0.855) and sensitivity/specificity up to 0.767/0.804 (mean 0.746/0.769). Our models, confirmed by quantitative reverse-transcription PCR (RT-qPCR), potentially offer a risk prediction tool for the development of postoperative sepsis with implications for patient management. They identify an individual predisposition to developing sepsis that warrants further exploration to better understand the underlying pathophysiology.
感染是一种常见的、通常是自限性的疾病，但可导致脓毒症，这是一种严重的、危及生命的宿主失调反应。我们在一大群接受大型择期手术的非感染患者中研究了个体对发生单纯感染或脓毒症的表型易感性。对 267 名患者的术前样本进行了全血 RNA 测序分析。这些患者术后发生感染（n=77）或无感染（n=49）合并脓毒症、发生非感染性全身炎症反应（n=31）或术后恢复顺利（n=110）。基于术前转录组特征构建的机器学习分类模型可预测术后结局，包括脓毒症的曲线下面积高达 0.910（平均 0.855），敏感性/特异性高达 0.767/0.804（平均 0.746/0.769）。我们的模型经实时定量 RT-PCR（qRT-PCR）验证，为术后脓毒症的发展提供了一种风险预测工具，这对患者管理具有重要意义。它们确定了个体发生脓毒症的易感性，值得进一步探索以更好地理解潜在的病理生理学。
