biostudies-literatureUnknown5(9)Garcia Lopez AInfection 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 Cell reports. Medicine101712https://www.ebi.ac.uk/biostudies/studies/S-EPMC11528229biostudies-literatureRisk assessment with gene expression markers in sepsis development.PMC11528229Lukaszewski RBauer MGarcia Lopez ASinger MSeelbinder BSae-Ong TPanagiotou GSchauble SGiamarellos-Bourboulis EJfalseRisk assessment with gene expression markers in sepsis development.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 2024-01-01T00:00:00Z2024 Sep2026-07-16T00:26:03.781Z2025-04-04T22:48:15.485ZS-EPMC115282293923249710.1016/j.xcrm.2024.101712