Establishment of Early Diagnosis and Monitoring Model for Sepsis Patients

NCT ID: NCT04974411

Last Updated: 2021-07-23

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 2000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2021-02-02
• Study Completion Date: 2024-03-20

Brief Summary

In recent years, although the clinical treatment of sepsis has been greatly improved, it is still an important cause of death in ICU patients, and seriously threatens human health. Its predictive biomarkers have become one of the bottlenecks in the field of disease diagnosis, treatment and development of effective drugs to reduce incidence rate and mortality. This will eventually become the key point of treatment for patients with sepsis. In the early stage, the investigators have established a single center sepsis database and sepsis animal model, and made a preliminary exploration on the mechanism and treatment of sepsis. Based on the previous results, this study intends to create a national multi center sepsis apparent database and sample bank, collect the data of sepsis patients' injury characteristics, clinical characteristics, biochemical indicators, micro multidimensional and omics results, etiological characteristics, etc., and integrate them. Using big data combined with machine learning method, the early warning and real-time course monitoring model of traumatic sepsis is established. The completion of this project can achieve early warning of sepsis, real-time monitoring of the progress of the disease, early rational allocation of medical care, and reduce the mortality of sepsis patients.

Detailed Description

Sepsis is one of the most fatal diseases worldwide, characterized by high incidence rate (18.6/1 000 hospitalization) and high mortality (50%). The patients often need to be treated in ICU, and the medical cost accounts for a large proportion. The ideal state, that is, accurate and early identification, must be the key point to influence the clinical decision-making of sepsis and guide more accurate treatment and intervention. With the development and improvement of pre hospital emergency technology, surgical technology and intensive care technology, the early mortality of patients with sepsis decreased significantly, but the mortality caused by multiple organ dysfunction (MODS) increased significantly. However, there are few reports on early sepsis warning and real-time monitoring of sepsis patients.

The existing research on early warning and course monitoring of sepsis can be roughly divided into demographic data, trauma severity score system, physiological and biochemical indicators, genetic background and so on. However, most studies only focus on the significance of a single index in the early warning and diagnosis of sepsis, which can only reflect one aspect of the body, and the diagnostic sensitivity is not high. Although there are a few multi marker related studies, such as the haplotype (- 1082-819-592ata) of three gene polymorphisms in IL-10 promoter region can affect the risk of sepsis in a small population (114 cases). The combination of plasma and cell biomarkers in critically ill patients suggests that the combination of plasma PCT, sTREM-1 and neutrophil CD64 index is better than single index in the early warning diagnosis of sepsis risk. However, this kind of research is still limited to a certain kind of indicators, and its clinical guidance value is limited. In addition, metabonomics and proteomics also have great potential to help identify specific sepsis phenotypes, and to find much-needed predictive and prognostic biomarkers, so as to guide more personalized management and treatment. Therefore, it is necessary to integrate the injury characteristics, clinical characteristics, biochemical indicators, micro multidimensional and omics results, etiological characteristics and other data to make accurate and efficient early warning and course monitoring of sepsis.

The project team has established a single center sepsis database in the early stage, and how to expand the scale of the database in the future, and use the samples in the sample library for multidimensional and omics methods to screen 100 biological molecular targets. Further research will integrate sepsis patients' injury characteristics, clinical characteristics, biochemical indicators, micro multidimensional and omics results, etiological characteristics and other relatively independent parts, and use big data combined with machine learning method to establish early warning and real-time course monitoring model of traumatic sepsis.

This study will be carried out from the following three levels: 1) to establish a multi center database of patients with sepsis; ② 100 biological molecular targets were screened by micro multidimensional and omics, and the data of injury characteristics, clinical characteristics, biochemical indexes, micro multidimensional and omics results, etiological characteristics and other aspects of sepsis patients were integrated to establish an early accurate early warning and real-time disease monitoring model of sepsis; ③ The application of the prediction model in sepsis patients was further verified by a cross regional multicenter prospective cohort study.

Conditions

Sepsis

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

Infection

Patients diagnosed with infection but did not reach the sepsis marker.

infection

Intervention Type: OTHER

this study had two observation cohorts, one that observed sepsis in the presence of infection; the other cohort was monitored during sepsis until septic shock appeared

sepsis

The patient was diagnosed with sepsis but did not develop septic shock

infection

Intervention Type: OTHER

this study had two observation cohorts, one that observed sepsis in the presence of infection; the other cohort was monitored during sepsis until septic shock appeared

sepsis

Intervention Type: OTHER

this study had two observation cohorts, one that observed sepsis in the presence of infection; the other cohort was monitored during sepsis until septic shock appeared

sepsis shock

The patient was diagnosed with sepsis shock

infection

Intervention Type: OTHER

this study had two observation cohorts, one that observed sepsis in the presence of infection; the other cohort was monitored during sepsis until septic shock appeared

sepsis

Intervention Type: OTHER

this study had two observation cohorts, one that observed sepsis in the presence of infection; the other cohort was monitored during sepsis until septic shock appeared

Interventions

infection

this study had two observation cohorts, one that observed sepsis in the presence of infection; the other cohort was monitored during sepsis until septic shock appeared

Intervention Type: OTHER

sepsis

this study had two observation cohorts, one that observed sepsis in the presence of infection; the other cohort was monitored during sepsis until septic shock appeared

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

• Patients diagnosed with an infectious disease or Sepsis patients meeting the diagnostic criteria for sepsis 3.0 (determined by two doctors with senior professional titles)
• Older than 16 years and younger than 60 years
• Agree to cooperate with the investigation and sign the informed consent

Exclusion Criteria

• The infection was preexisting with clinically diagnosed organ insufficiency or failure
• Hematological disorders predate infection
• Sepsis is preceded by a serious infectious disease
• Long-term use of immunosuppressant or immunodeficiency patients.
• A pregnant woman

• Minimum Eligible Age: 16 Years
• Maximum Eligible Age: 60 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Xinhua Hospital, Shanghai Jiao Tong University School of Medicine

OTHER

Sponsor Role: collaborator

Second Affiliated Hospital of Xi'an Jiaotong University

OTHER

Sponsor Role: collaborator

Tang-Du Hospital

OTHER

Sponsor Role: collaborator

First Affiliated Hospital of Harbin Medical University

OTHER

Sponsor Role: collaborator

Affiliated Hospital of Qinghai University

OTHER

Sponsor Role: collaborator

General Hospital of Ningxia Medical University

OTHER

Sponsor Role: collaborator

LanZhou University

OTHER

Sponsor Role: collaborator

The Fourth People's Hospital of Nanning

OTHER

Sponsor Role: collaborator

First Affiliated Hospital of Xinjiang Medical University

OTHER

Sponsor Role: collaborator

First Affiliated Hospital Xi'an Jiaotong University

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Liu Chang, MD

Role: STUDY_DIRECTOR

The First Affiliated Hospital of Xi 'an Jiaotong University

Locations

The First Affiliated Hospital of Xi'an Jiaotong University

Xi'an, Shaanxi, China

Site Status: RECRUITING

Countries

China

Central Contacts

Miao RunChen, MD

Role: CONTACT

miaozao@126.com

0086-18229097849

Liu Tong, Master

Role: CONTACT

1656044911@qq.com

0086-15129935253

Facility Contacts

Liu Chang, MD

Role: primary

liuchangdoctor@163.com

0086-18991232095

Miao RunChen, MD

Role: backup

miaozao@126.com

0086-18229097849

Other Identifiers

XJTU1AF-CRF-2020-003

Identifier Type: -

Identifier Source: org_study_id