NCT05305469

Early Identification and Prognosis Prediction of Sepsis Through Multiomics

• Status: Unknown
• Phase: —
• Study type: Observational
• Enrollment: 900 (estimated)
• Sponsor: Yantai Yuhuangding Hospital · Academic / Other
• Sex: All
• Age: 18 Years – 85 Years
• Healthy volunteers: Accepted

Summary

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.

Detailed description

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.

Conditions

• Sepsis

Timeline

• Start date: 2022-01-01
• Primary completion: 2024-12-31
• Completion: 2025-12-31
• First posted: 2022-03-31
• Last updated: 2024-01-24

Locations

1 site across 1 country: China