Sepsis
Conditions
Keywords
Prognosis, Inflammation, Immune, Multiomics, Machine learning
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.
Interventions
None listed
Sponsors
Yantai Yuhuangding Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 85 Years
Healthy volunteers
Yes
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Pathogen-specific patterns | March 2022 - December 2023 | To elucidate the unique infection pathogen-specific molecular patterns in septic patients |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Prognostic prediction models | March 2022 - December 2024 | To establish the models using multi-omics data to predict the prognosis of sepsis |
Countries
China
Contacts
Primary ContactJing Wang
Outcome results
None listed