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. Whole blood RNA sequencing analysis was performed on preoperative samples taken from 267 patients. These comprised patients who developed postoperative infection with (n=77) or without (n=49) sepsis, non-infectious systemic inflammatory response (n=31), or an uncomplicated postoperative course (n=110). Machine learning classification models built on preoperative transcriptomic signatures predicted postoperative outcomes including sepsis. Overall design: Whole blood RNA sequencing analysis was performed on preoperative samples taken from 267 patients. These comprised patients who developed postoperatively infection leading to sepsis (n=77) or an uncomplicated infection outcome (n=49), non-infectious systemic inflammatory response (n=31), or an uncomplicated postoperative course (n=110).