Clinical trial NCT06791382: AI-Driven Prediction of Hospital-Acquired Infections With EHR
Official title: Predicting Hospital-Acquired Infections Using Electronic Health Records: An AI-Assisted Approach
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Hospital-acquired Infections
Lead sponsor: The Eye Hospital of Wenzhou Medical University
Sponsor class: OTHER
First posted: 2025-01-24
Enrollment: 1000000
Sex: ALL
Minimum age (years): 0
Maximum age (years): 90
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: CASE_CONTROL
Time perspective: OTHER
Sampling method: NON_PROBABILITY_SAMPLE
Study population: The study population consists of individuals who have received care at participating study centers. Participants must have comprehensive electronic health records (EHRs) available, including medical history, laboratory test results, treatment data, and clinical observations. Both individuals who have developed hospital-acquired infections (HAIs) and those who have not will be included in the study to evaluate the AI-assisted model's predictive capabilities for infection risk. The study will focus on patients with complete and documented care records from the participating centers, ensuring a diverse cohort for analysis across different age groups and infection types.

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.

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.

Primary outcomes:
measure: Area Under the Curve (AUC)
timeFrame: 1 year
measure: F1 Score
timeFrame: 1 year
Secondary outcomes:
measure: Sensitivity (True Positive Rate)
timeFrame: 1 year
measure: Specificity (True Negative Rate)
timeFrame: 1 year
Other outcomes:
Locations:
city: Wenzhou
country: China
facility: First Affiliated Hospital of Wenzhou Medical University
state: Zhejiang
city: Wenzhou
country: China
facility: Second Affiliated Hospital of Wenzhou Medical University
state: Zhejiang
Study officials:
References: