Early Biomarker Signatures in Surgical Sepsis.

ABSTRACT:

Introduction

Sepsis has complex, time-sensitive pathophysiology and important phenotypic subgroups. The objective of this study was to use machine learning analyses of blood and urine biomarker profiles to elucidate the pathophysiologic signatures of subgroups of surgical sepsis patients.

Methods

This prospective cohort study included 243 surgical sepsis patients admitted to a quaternary care center between January 2015 and June 2017. We applied hierarchical clustering to clinical variables and 42 blood and urine biomarkers to identify phenotypic subgroups in a development cohort. Clinical characteristics and short-term and long-term outcomes were compared between clusters. A naïve Bayes classifier predicted cluster labels in a validation cohort.

Results

The developme

SUBMITTER: Madushani RWMA 

PROVIDER: S-EPMC9827429 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature