Diagnosing Sepsis Through Proteomic Insights: Findings from a Prospective ICU Cohort.

ABSTRACT:

Introduction

Sepsis diagnosis remains clinical and heterogeneous. We hypothesized that a proteomics-informed machine-learning approach could identify a small, easy-to-use, and optimized set of clinical variables to complement or potentially outperform SOFA.

Methods

We conducted a prospective, single-center, observational study in an academic intensive care unit. Plasma from critically ill patients with and without sepsis was analyzed using liquid chromatography coupled with tandem mass spectrometry (LC-MS). Data were acquired with data-independent acquisition parallel accumulation-serial fragmentation (diaPASEF) and processed using DIA-NN software. Differentially expressed proteins informed model development. Random Forest models were trained in a Discovery cohort (n=55) to

SUBMITTER: Ardabili AK 

PROVIDER: S-EPMC12407673 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature