Artificial Intelligence for Sepsis Prediction in ICU

Septic Shock Clinical Trial

— AICUSepsis  

Official title:

Application of Artificial Intelligence Sepsis Prediction Model to Assist ICU Clinical Decision

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT04913181
• Other study ID #: SHZJU-ICU2020-202
• Status: Not yet recruiting
• Start date: June 1, 2021
• Est. completion date: June 1, 2023

Study information

• Verified date: May 2021
• Source: Second Affiliated Hospital, School of Medicine, Zhejiang University
• Contact: ?? ?
• Phone: 13685753994
• Email: deter_leung[@]sina.com
• Is FDA regulated: No
• Study type: Observational

Clinical 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. 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.

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

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 2000
• Est. completion date: June 1, 2023
• Est. primary completion date: June 1, 2022
• Accepts healthy volunteers: No
• Gender: All
• Age group: 16 Years to 100 Years

Eligibility

Inclusion Criteria:

All patients with acute critical illness who are eligible for admission to ICU during the study period

Exclusion Criteria:

1. Patients under the age of 16;

2. Pregnant and parturient women;

3. Patients who planned to be admitted to the department for surgery and transferred the next day after evaluation;

4. Patients admitted to the department and diagnosed with sepsis;

5. Patients with ICU stay less than 24 hours;

Related Conditions & MeSH terms

• Artificial Intelligence
• Psychotic Disorders
• Septic Shock
• Shock, Septic

Intervention

Diagnostic Test:
• Artificial intelligence sepsis prediction model
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.

Locations

Country	Name	City	State
n/a

Sponsors (1)

Lead Sponsor	Collaborator
Second Affiliated Hospital, School of Medicine, Zhejiang University

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Accuracy of model diagnosis	Evaluation of the accuracy of prediction model in clinical application	2 years