Early Identification and Prognosis Prediction of Sepsis Through Multiomics
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 900
- 试验地点
- 1
- 主要终点
- Pathogen-specific patterns
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 85 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Patients with sepsis or septic shock who meet the diagnostic criteria (2016 sepsis 3.0 standard);
- •Age 18～85 years old.
排除标准
- •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.
结局指标
主要结局
Pathogen-specific patterns
时间窗: March 2022 - December 2023
To elucidate the unique infection pathogen-specific molecular patterns in septic patients
次要结局
- Prognostic prediction models(March 2022 - December 2024)