Risk assessment with gene expression markers in sepsis development.
Cell Rep Med
; 5(9): 101712, 2024 Sep 17.
Article
em En
| MEDLINE
| ID: mdl-39232497
ABSTRACT
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.
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Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Biomarcadores
/
Sepse
Limite:
Aged
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Cell Rep Med
Ano de publicação:
2024
Tipo de documento:
Article