Artificial Intelligence for Sepsis Prediction in ICU (AICUSepsis)

Zhejiang University

Status: Unknown

Conditions: Artificial Intelligence; Septic Shock; Intensive Care Unit Psychosis

Treatments: Diagnostic Test: Artificial intelligence sepsis prediction model

Study type: Observational

Funder types: Other

Identifiers

NCT04913181

SHZJU-ICU2020-202

Details and patient eligibility

About

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.

Full 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

Enrollment: 2,000 estimated patients

Sex: All

Ages: 16 to 100 years old

Volunteers: No Healthy Volunteers

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;

Trial design

2,000 participants in 2 patient groups

Sepsis prediction model

Description:

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

Treatment:

Diagnostic Test: Artificial intelligence sepsis prediction model

Daily clinical judgment of doctors

Description:

This group of people was used for the clinician's decision without sepsis prediction model.

Treatment:

Diagnostic Test: Artificial intelligence sepsis prediction model