SepsisEIPPSMNCT05305469
Early Identification and Prognosis Prediction of Sepsis Through Multiomics
An observational study of Sepsis, run by Yantai Yuhuangding Hospital at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 85 yearsHealthy volunteers can take part
- Conditions the study is for
- Sepsis
- How many people
- 900 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2022to 31 Dec 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 85 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: The study cases are from the Department of Critical Care Medicine, a top-grade hospital in Yantai
Who may be able to join
- Patients with sepsis or septic shock who meet the diagnostic criteria (2016 sepsis 3.0 standard);
- Age 18～85 years old.
Who cannot join
- 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 locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Yantai Yuhuangding Hospital
Yantai, Shandong, 264000
Jing Wang, wangjinghehe@sina.com
About this study
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.
Read the 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.
- Conditions
- Sepsis
- Keywords
- Prognosis, Inflammation, Immune, Multiomics, Machine learning
- Medical subject headings
- Sepsis, Inflammation
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| GN | Gram-negative bacteria infection group | |
| GP | Gram-positive bacteria infection group | |
| Fungal | Fungal infection group | |
| Viral | Viral infection group | |
| Control | Non-sepsis group |
What is being measured
The primary outcome is the main question the study is designed to answer.
Pathogen-specific patterns
To elucidate the unique infection pathogen-specific molecular patterns in septic patients
Measured: March 2022 - December 2023
Secondary outcomes (1)
Prognostic prediction models
To establish the models using multi-omics data to predict the prognosis of sepsis
Measured: March 2022 - December 2024
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted31 Mar 2022
- Study start1 Jan 2022
- Primary completion31 Dec 2024 (estimated)
- Study completion31 Dec 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for SepsisStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 900 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Yantai Yuhuangding Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Jing Wang, Principal investigator, Yantai Yuhuangding Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05305469
- Sponsor's protocol ID
- 2022-031
- Acronym
- EIPPSM
- Status last verified
- Jan 2024
- First submitted
- 22 Mar 2022
- Last updated
- 24 Jan 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Wang J, Sun Y, Teng S, Li K. Prediction of sepsis mortality using metabolite biomarkers in the blood: a meta-analysis of death-related pathways and prospective validation. BMC Med. 2020 Apr 15;18(1):83. doi: 10.1186/s12916-020-01546-5.
PubMed 32290837
Study at a glance
From the registry record, last updated by the sponsor on 24 Jan 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Early Identification and Prognosis Prediction of Sepsis Through Multiomics
- ClinicalTrials.gov ID
- NCT05305469
- Acronym / sponsor ID
- EIPPSM / 2022-031
- Conditions
- Sepsis
- Keywords
- Prognosis, Inflammation, Immune, Multiomics, Machine learning
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 900 participants (estimated)
- Lead sponsor
- Yantai Yuhuangding HospitalUniversity, hospital or other
- Study start
- 1 Jan 2022 (actual)
- Primary completion
- 31 Dec 2024 (estimated)
- Study completion
- 31 Dec 2025 (estimated)
- First posted
- 31 Mar 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 5
| Arm | Type | Receives |
|---|---|---|
| GN | – | –Gram-negative bacteria infection group |
| GP | – | –Gram-positive bacteria infection group |
| Fungal | – | –Fungal infection group |
| Viral | – | –Viral infection group |
| Control | – | –Non-sepsis group |
Who may qualify
- Ages
- 18 to 85 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- The study cases are from the Department of Critical Care Medicine, a top-grade hospital in Yantai
Key inclusion criteria (2 listed)
- Patients with sepsis or septic shock who meet the diagnostic criteria (2016 sepsis 3.0 standard);
- Age 18～85 years old.
Key exclusion criteria (6 listed)
- 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;
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Pathogen-specific patterns
Time frame: March 2022 - December 2023
To elucidate the unique infection pathogen-specific molecular patterns in septic patients
1 secondary outcome
Prognostic prediction models
Time frame: March 2022 - December 2024
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 24 Jan 2024.
- Results publication: PubMed 32290837Wang J, Sun Y, Teng S, Li K. Prediction of sepsis mortality using metabolite biomarkers in the blood: a meta-analysis of death-related pathways and prospective validation. BMC Med. 2020 Apr 15;18(1):83. doi: 10.1186/s12916-020-01546-5.
- Individual participant dataSharing planned: No
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Sepsis
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Jing Wang principal investigator
Yantai Yuhuangding Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Sepsis, run by Yantai Yuhuangding Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 24 Jan 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Jing Wang (8605356691999 ext. 83608, wangjinghehe@sina.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Pathogen-specific patterns (time frame: March 2022 - December 2023). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05305469, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 24 Jan 2024; AzTrials last refreshed its copy on 7 Nov 2026.