Early Identification and Prognosis Prediction of Sepsis Through Multiomics
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Sepsis
- Sponsor
- Yantai Yuhuangding Hospital
- Enrollment
- 900
- Locations
- 1
- Primary Endpoint
- Pathogen-specific patterns
- Status
- Recruiting
- Last Updated
- 2 years ago
Overview
Brief 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.
Investigators
Eligibility Criteria
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.
Outcomes
Primary Outcomes
Pathogen-specific patterns
Time Frame: March 2022 - December 2023
To elucidate the unique infection pathogen-specific molecular patterns in septic patients
Secondary Outcomes
- Prognostic prediction models(March 2022 - December 2024)