Risk assessment with gene expression markers in sepsis development. Risk assessment with gene expression markers in sepsis development

We investigated the individual phenotypic predisposition to developing uncomplicated infection or sepsis in a large cohort of non-infected patients undergoing major elective surgery. We built machine learning classification models on preoperative transcriptomic signatures to predict postoperative outcomes including sepsis. To test the predictive capability of these models for ongoing infection, whole blood RNA sequencing analysis on 61 independent patients with COVID-19 (10 mild, 51 severe cases) was performed. Overall design: Whole blood RNA sequencing analysis was performed on 61 affected COVID-19 patients (10 mild, 51 severe cases).

本研究针对接受择期大手术的未感染患者大型队列，探究了个体发生非复杂性感染或脓毒症（sepsis）的表型易感性。我们基于术前转录组特征（transcriptomic signatures）构建机器学习分类模型，以预测包括脓毒症在内的术后结局。为验证上述模型对活动性感染的预测能力，本研究对61名独立新型冠状病毒肺炎（COVID-19）患者（轻症10例、重症51例）开展了全血RNA测序分析。整体实验设计：本研究对61名新型冠状病毒肺炎（COVID-19）患者（轻症10例、重症51例）实施了全血RNA测序分析。