Early Identification and Prognosis Prediction of Sepsis Through Multiomics

This study aims to integrate multi-omics data and clinical indicators to reveal pathogen-specific molecular patterns in patients with sepsis and establish prognostic prediction models through multiple machine learning algorithms.

This study aims to quantify the plasma metabolome, single nucleotide polymorphisms (SNPs) of exons and immunocytokines of septic patients with different pathogen infections and prognostic outcomes. Multi-omics data, cytokines, and clinical indicators will be integrated through multiple machine learning algorithms to reveal pathogen-specific molecular patterns and multi-dimensional prognostic prediction models.

Eligibility

Relevant conditions:

Sepsis

If you aren't sure if you meet the criteria above speak to your healthcare professional. Criteria may be updated but not reflected here, do not hesitate to contact the trial if you think are close to fitting criteria.

locations

Contact Information

Overall Contact

Jing Wang

wangjinghehe@sina.com

8605356691999

Data sourced from ClinicalTrials.gov