Early Identification and Prognosis Prediction of Sepsis Through Multiomics (EIPPSM)

Yantai Yuhuangding Hospital

Status: Enrolling

Conditions: Sepsis

Study type: Observational

Funder types: Other

Identifiers

NCT05305469

2022-031

Details and patient eligibility

About

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.

Full 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.

Enrollment: 900 estimated patients

Sex: All

Ages: 18 to 85 years old

Volunteers: Accepts Healthy Volunteers

Inclusion criteria

• Patients with sepsis or septic shock who meet the diagnostic criteria (2016 sepsis 3.0 standard);
• Age 18～85 years old.

Exclusion criteria

• ICU stay of the subjects less than 72 hours;
• Female subjects who are pregnant;
• The subjects not sure if infected;
• The subjects performed CPR;
• The subjects suffer from chronic renal disease;
• The subjects with incomplete clinical data.

Trial design

900 participants in 5 patient groups

GN

Description:

Gram-negative bacteria infection group

GP

Description:

Gram-positive bacteria infection group

Fungal

Description:

Fungal infection group

Viral

Description:

Viral infection group

Control

Description:

Non-sepsis group