Machine learning identifies clinical sepsis phenotypes that translate to the plasma proteome.

ABSTRACT:

Background

Sepsis therapy is still limited to treatment of the underlying infection and supportive measures. To date, various sepsis subtypes were proposed, but therapeutic options addressing the molecular changes of sepsis were not identified. With the aim of a future individualized therapy, we used machine learning (ML) to identify clinical phenotypes and their temporal development in a prospective, multicenter sepsis cohort and characterized them using plasma proteomics.

Methods

Routine clinical data and blood samples were collected from 384 patients. Sepsis phenotypes were identified based on clinical measurements and plasma samples from 301 patients were analyzed using mass spectrometry. The obtained data were evaluated in relation to the phenotypes, and supervised ML m

SUBMITTER: Bracht T 

PROVIDER: S-EPMC12675651 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature