AI-Driven Prediction of Hospital-Acquired Infections With EHR

NCT ID: NCT06791382

Last Updated: 2025-04-17

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 1000000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2023-02-01
• Study Completion Date: 2025-05-31

Brief Summary

This is a multi-center, clinical study designed to evaluate the application and effectiveness of an AI-assisted predictive model for identifying and diagnosing infection, leveraging multimodal health data.

Detailed Description

Hospital-acquired infections (HAIs) are a significant cause of morbidity and mortality in healthcare settings. Early identification and prevention of HAIs are crucial for improving patient outcomes, reducing healthcare costs, and preventing the spread of infections. In clinical practice, healthcare providers often need to integrate a wide range of patient data, including medical history, laboratory test results, medication usage, surgical procedures, and clinical observations, to assess infection risks and prevent HAIs. As infection control and precision medicine become increasingly important, the challenge remains to predict and prevent infections, especially in patients with subtle or asymptomatic risk factors. Recent advancements in artificial intelligence and data analysis techniques have shown great promise in improving the accuracy and efficiency of infection prediction and prevention. This study aims to develop an AI-assisted decision-making system by integrating multimodal data from electronic health records, lab results, clinical observations, and patient demographics. The objective is to enhance the early identification of patients at risk for HAIs, streamline clinical workflows, and optimize infection control measures. Ultimately, this system seeks to reduce the incidence of hospital-acquired infections, improve patient safety, and enhance overall healthcare quality.

Conditions

Hospital-acquired Infections

Study Design

• Observational Model Type: CASE_CONTROL
• Study Time Perspective: OTHER

Study Groups

Hospital-Acquired Infection Cohort

This group consists of patients who have developed a hospital-acquired infection (HAI) during their hospital stay. Participants in this cohort will be used to evaluate the effectiveness of the AI-assisted predictive model in identifying the risk factors leading to hospital-acquired infections. The model will be assessed based on the accuracy of predicting infection risks in hospitalized patients. No specific interventions will be provided as part of this cohort beyond the existing hospital infection control practices.

AI-Based Diagnostic and Prognostic Model

Intervention Type: DIAGNOSTIC_TEST

This intervention involves an AI system that integrates multimodal data, including patient medical history, laboratory test results, clinical observations, and treatment data, to predict the risk of hospital-acquired infections (HAIs). The system uses deep learning algorithms to provide real-time, accurate predictions, enabling early identification of patients at risk for infections. By analyzing historical health data, the model aims to predict potential infection developments, improving early detection, prevention strategies, and patient outcomes in hospital settings.

Healthy Cohort (No HAI)

This group consists of patients who have not developed any hospital-acquired infections during their hospital stay. Participants in this cohort will serve as the control group for comparison against the experimental group. The AI-assisted model will be evaluated for its ability to distinguish between patients who are at risk for developing infections and those who remain infection-free during hospitalization. No interventions will be provided as part of this cohort, as they represent patients without infection-related complications.

AI-Based Diagnostic and Prognostic Model

Intervention Type: DIAGNOSTIC_TEST

This intervention involves an AI system that integrates multimodal data, including patient medical history, laboratory test results, clinical observations, and treatment data, to predict the risk of hospital-acquired infections (HAIs). The system uses deep learning algorithms to provide real-time, accurate predictions, enabling early identification of patients at risk for infections. By analyzing historical health data, the model aims to predict potential infection developments, improving early detection, prevention strategies, and patient outcomes in hospital settings.

Interventions

AI-Based Diagnostic and Prognostic Model

This intervention involves an AI system that integrates multimodal data, including patient medical history, laboratory test results, clinical observations, and treatment data, to predict the risk of hospital-acquired infections (HAIs). The system uses deep learning algorithms to provide real-time, accurate predictions, enabling early identification of patients at risk for infections. By analyzing historical health data, the model aims to predict potential infection developments, improving early detection, prevention strategies, and patient outcomes in hospital settings.

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

1. Patients with complete and accessible EHR data, including medical history, laboratory test results, treatment regimens, clinical observations, and infection history.

2. Patients who have been admitted to the participating hospital or healthcare facility during the study period.

3. All participants must provide informed consent to use their health data for research purposes.

Exclusion Criteria

1. Patients with incomplete or missing critical EHR data, such as lab results, medical history, or treatment details, which are necessary for infection prediction.

2. Patients who have severe cognitive disorders, dementia, or conditions that prevent them from providing informed consent or participating in the study.

3. Patients who have not been admitted to the hospital during the study period or who are receiving outpatient care only.

4. Patients with terminal conditions where infection prediction may not be applicable to the clinical goals of the study.

• Minimum Eligible Age: 0 Years
• Maximum Eligible Age: 90 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

The Eye Hospital of Wenzhou Medical University

OTHER

Sponsor Role: lead

Responsible Party

Kang Zhang

Chief Scientist

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

First Affiliated Hospital of Wenzhou Medical University

Wenzhou, Zhejiang, China

Site Status: RECRUITING

Second Affiliated Hospital of Wenzhou Medical University

Wenzhou, Zhejiang, China

Site Status: RECRUITING

Countries

China

Central Contacts

Fei Liu, MD

Role: CONTACT

liufei_2359@163.com

+86 13810512704

Facility Contacts

Cheng Tang

Role: primary

c249325687@163.com

Sian Liu

Role: primary

liusan@mail3.sysu.edu.cn

+86-0577-88002888

Other Identifiers

Hospital-Acquired Infections

Identifier Type: -

Identifier Source: org_study_id