GSE208587
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).