Artificial Intelligence for Sepsis Prediction in ICU (AICUSepsis)

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Septic Shock; Artificial Intelligence; Intensive Care Unit Psychosis
• Intervention / Treatment: Diagnostic test: Artificial intelligence 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 Type: Observational
• Enrollment (Anticipated): 2000

Contacts and Locations

• Study Contact: Name: 琦强 梁; Phone Number: 13685753994; Email: deter_leung@sina.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 16 years to 100 years (Child, Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

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.

Description

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;

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
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	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.
Daily clinical judgment of doctors; This group of people was used for the clinician's decision without sepsis prediction model.	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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Accuracy of model diagnosis	Evaluation of the accuracy of prediction model in clinical application	2 years

Collaborators and Investigators

• Sponsor: Second Affiliated Hospital, School of Medicine, Zhejiang University

Study record dates

Study Major Dates

• Study Start (Anticipated): June 1, 2021
• Primary Completion (Anticipated): June 1, 2022
• Study Completion (Anticipated): June 1, 2023

Study Registration Dates

• First Submitted: May 31, 2021
• First Submitted That Met QC Criteria: June 2, 2021
• First Posted (Actual): June 4, 2021

Study Record Updates

• Last Update Posted (Actual): June 4, 2021
• Last Update Submitted That Met QC Criteria: June 2, 2021
• Last Verified: May 1, 2021

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Mental Disorders; Pathologic Processes; Infections; Systemic Inflammatory Response Syndrome; Inflammation; Schizophrenia Spectrum and Other Psychotic Disorders; Shock; Sepsis; Psychotic Disorders; Shock, Septic
• Other Study ID Numbers: SHZJU-ICU2020-202

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No