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

Y

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

Trial contacts and locations

0

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Central trial contact

Jing Wang

Data sourced from clinicaltrials.gov

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