Artificial IntelligenceAICUSepsisNCT04913181
Artificial Intelligence for Sepsis Prediction in ICU
An observational study of Artificial Intelligence and Septic Shock, run by Second Affiliated Hospital, Zhejiang University, School of Medicine.
Trial summary
The development of sepsis prediction model in line with Chinese population, and extended to clinical, assist clinicians for early identification, early intervention, has a good application prospect. Read more about this study
- Who is it for?
- Ages 16 to 100 years
- Conditions the study is for
- Artificial Intelligence, Septic Shock, Intensive Care Unit Psychosis
- What is being tested?
- Artificial intelligence sepsis prediction model
- How many people
- 2,000 participants
- Where
- No sites listed yet
- Study dates
- 1 Jun 2021to 1 Jun 2023
- 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 16 to 100 years
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Patients who met the inclusion and exclusion criteria were predicted daily by sepsis model, and a quantitative list was formed according to the test results. There are two kinds of prediction results: low risk and high risk. The quantitative list is open to attending physicians to improve the efficiency of diagnosis. The predicted results are kept secret from clinicians.
Who may be able to join
- All patients with acute critical illness who are eligible for admission to ICU during the study period
Who cannot join
- Patients under the age of 16;
- Pregnant and parturient women;
- Patients who planned to be admitted to the department for surgery and transferred the next day after evaluation;
- Patients admitted to the department and diagnosed with sepsis;
- Patients with ICU stay less than 24 hours;
Trial locations and contacts
No sites are listed yet.
About this study
Official title: Application of Artificial Intelligence Sepsis Prediction Model to Assist ICU Clinical Decision
The development of sepsis prediction model in line with Chinese population, and extended to clinical, assist clinicians for early identification, early intervention, has a good application prospect. This study is a prospective observational study, mainly to evaluate the accuracy of the previously established sepsis prediction model. The occurrence of sepsis was determined by doctors' daily clinical judgment, and the results of the sepsis prediction model were matched and corrected to improve the clinical accuracy and applicability of the sepsis prediction model.
Read the detailed description
The sepsis prediction model adopted in this study has been completed in the preliminary preparation, which was constructed on 7,000 patients since the establishment of comprehensive ICU, and the sepsis 3.0 diagnostic standard was adopted.The sepsis prediction model was built using Python platform and XGBoost algorithm, which was used to predict the incidence of sepsis in ICU patients within 24 hours. The overall accuracy was 82%, and the area under the Auroc curve was 0.854.
Patients who met the inclusion and exclusion criteria were given a daily prediction of sepsis model, and a quantitative checklist was formed based on the test results.There are two kinds of forecast outcomes: low risk and high risk.Quantitative checklists are available to attending physicians to improve diagnostic efficiency.The results were kept confidential to the clinician.
All patients were diagnosed with sepsis by two senior attending physicians at a fixed time. The diagnosis consisted of two types: yes and no.If two attending physicians have different opinions, the third attending physician will be included for correction diagnosis, and the presence of sepsis will be determined in a 2:1 manner.The attending physicians are independent of each other.
When the diagnosis results of the attending physician are input into the system, the prediction results of yesterday's sepsis prediction model are compared and calculated to determine the accuracy of the prediction model
- Being tested
- Artificial intelligence sepsis prediction model
- Medical subject headings
- Shock, Septic
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial intelligence sepsis prediction modelDiagnostic test
The main purpose of this paper is to evaluate the accuracy of the sepsis prediction model established in the early stage. The occurrence of sepsis is determined by the daily clinical judgment of doctors, and the results of sepsis prediction model are matched and corrected.
| Study group | Role | What they receive |
|---|---|---|
| Sepsis prediction model | This group of people was used for the clinician's decision, and the sepsis prediction model was used simultaneously for the prediction, but the model was not involved in the decision, and was only used for verification Receives: Diagnostic Test: Artificial intelligence sepsis prediction model | |
| Daily clinical judgment of doctors | This group of people was used for the clinician's decision without sepsis prediction model. Receives: Diagnostic Test: Artificial intelligence sepsis prediction model |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of model diagnosis
Evaluation of the accuracy of prediction model in clinical application
Measured: 2 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Jun 2021
- Study start1 Jun 2021 (estimated)
- Primary completion1 Jun 2022 (estimated)
- Study completion1 Jun 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Artificial IntelligenceStudies 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
- 2,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Second Affiliated Hospital, Zhejiang University, School of Medicine
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04913181
- Sponsor's protocol ID
- SHZJU-ICU2020-202
- Acronym
- AICUSepsis
- Status last verified
- May 2021
- First submitted
- 31 May 2021
- Last updated
- 4 Jun 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 4 Jun 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Application of Artificial Intelligence Sepsis Prediction Model to Assist ICU Clinical Decision
- ClinicalTrials.gov ID
- NCT04913181
- Acronym / sponsor ID
- AICUSepsis / SHZJU-ICU2020-202
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 2,000 participants (estimated)
- Lead sponsor
- Second Affiliated Hospital, Zhejiang University, School of MedicineUniversity, hospital or other
- Study start
- 1 Jun 2021 (estimated)
- Primary completion
- 1 Jun 2022 (estimated)
- Study completion
- 1 Jun 2023 (estimated)
- First posted
- 4 Jun 2021 (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.
The development of sepsis prediction model in line with Chinese population, and extended to clinical, assist clinicians for early identification, early intervention, has a good application prospect. This study is a prospective observational study, mainly to evaluate the accuracy of the previously established sepsis prediction model. The occurrence of sepsis was determined by doctors' daily clinical judgment, and the results of the sepsis prediction model were matched and corrected to improve the clinical accuracy and applicability of the sepsis prediction model.
Detailed description
The sepsis prediction model adopted in this study has been completed in the preliminary preparation, which was constructed on 7,000 patients since the establishment of comprehensive ICU, and the sepsis 3.0 diagnostic standard was adopted.The sepsis prediction model was built using Python platform and XGBoost algorithm, which was used to predict the incidence of sepsis in ICU patients within 24 hours. The overall accuracy was 82%, and the area under the Auroc curve was 0.854.
Patients who met the inclusion and exclusion criteria were given a daily prediction of sepsis model, and a quantitative checklist was formed based on the test results.There are two kinds of forecast outcomes: low risk and high risk.Quantitative checklists are available to attending physicians to improve diagnostic efficiency.The results were kept confidential to the clinician.
All patients were diagnosed with sepsis by two senior attending physicians at a fixed time. The diagnosis consisted of two types: yes and no.If two attending physicians have different opinions, the third attending physician will be included for correction diagnosis, and the presence of sepsis will be determined in a 2:1 manner.The attending physicians are independent of each other.
When the diagnosis results of the attending physician are input into the system, the prediction results of yesterday's sepsis prediction model are compared and calculated to determine the accuracy of the prediction model
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Sepsis prediction model | – | Diagnostic Test: Artificial intelligence sepsis prediction modelThis group of people was used for the clinician's decision, and the sepsis prediction model was used simultaneously for the prediction, but the model was not involved in the decision, and was only used for verification |
| Daily clinical judgment of doctors | – | Diagnostic Test: Artificial intelligence sepsis prediction modelThis group of people was used for the clinician's decision without sepsis prediction model. |
Who may qualify
- Ages
- 16 to 100 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients who met the inclusion and exclusion criteria were predicted daily by sepsis model, and a quantitative list was formed according to the test results. There are two kinds of prediction results: low risk and high risk. The quantitative list is open to attending physicians to improve the efficiency of diagnosis. The predicted results are kept secret from clinicians.
Key inclusion criteria (1 listed)
- All patients with acute critical illness who are eligible for admission to ICU during the study period
Key exclusion criteria (5 listed)
- Patients under the age of 16;
- Pregnant and parturient women;
- Patients who planned to be admitted to the department for surgery and transferred the next day after evaluation;
- Patients admitted to the department and diagnosed with sepsis;
- Patients with ICU stay less than 24 hours;
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Artificial intelligence sepsis prediction modelThe main purpose of this paper is to evaluate the accuracy of the sepsis prediction model established in the early stage. The occurrence of sepsis is determined by the daily clinical judgment of doctors, and the results of sepsis prediction model are matched and corrected. | Diagnostic test | Daily clinical judgment of doctors, Sepsis prediction model |
Primary outcome
Accuracy of model diagnosis
Time frame: 2 years
Evaluation of the accuracy of prediction model in clinical application
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 4 Jun 2021.
- Individual participant dataSharing planned: No
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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.
Questions about this trial
What is this clinical trial studying?
An observational study of Artificial Intelligence and Septic Shock, run by Second Affiliated Hospital, Zhejiang University, School of Medicine.
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 1 inclusion and 5 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 4 Jun 2021. Status can change between updates, so confirm with the study contact before making plans.
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 琦强 梁 (13685753994, deter_leung@sina.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of model diagnosis (time frame: 2 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 4 Jun 2021.