Hospital-acquired InfectionsNCT06791382
AI-Driven Prediction of Hospital-Acquired Infections With EHR
An observational study of Hospital-acquired Infections, run by The Eye Hospital of Wenzhou Medical University at 2 sites in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 0 to 90 yearsHealthy volunteers can take part
- Conditions the study is for
- Hospital-acquired Infections
- What is being tested?
- AI-Based Diagnostic and Prognostic Model
- How many people
- 1,000,000 participants
- Where
- 2 sitesChina
- Study dates
- 1 Feb 2023to May 2025
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 0 to 90 years
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
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.
Who may be able to join
- Patients with complete and accessible EHR data, including medical history, laboratory test results, treatment regimens, clinical observations, and infection history.
- Patients who have been admitted to the participating hospital or healthcare facility during the study period.
- All participants must provide informed consent to use their health data for research purposes.
Who cannot join
- Patients with incomplete or missing critical EHR data, such as lab results, medical history, or treatment details, which are necessary for infection prediction.
- Patients who have severe cognitive disorders, dementia, or conditions that prevent them from providing informed consent or participating in the study.
- Patients who have not been admitted to the hospital during the study period or who are receiving outpatient care only.
- Patients with terminal conditions where infection prediction may not be applicable to the clinical goals of the study.
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
China2 sites, 2 recruiting
- Recruiting
First Affiliated Hospital of Wenzhou Medical University
Wenzhou, Zhejiang
Cheng Tang, c249325687@163.com
- Recruiting
Second Affiliated Hospital of Wenzhou Medical University
Wenzhou, Zhejiang
Sian Liu, +86-0577-88002888, liusan@mail3.sysu.edu.cn
About this study
Official title: Predicting Hospital-Acquired Infections Using Electronic Health Records: An AI-Assisted Approach
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.
Read the 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
- Being tested
- AI-Based Diagnostic and Prognostic Model
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-Based Diagnostic and Prognostic ModelDiagnostic 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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Diagnostic Test: AI-Based Diagnostic and Prognostic Model | |
| 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. Receives: Diagnostic Test: AI-Based Diagnostic and Prognostic Model |
What is being measured
The primary outcome is the main question the study is designed to answer.
Area Under the Curve (AUC)
AUC of the ROC curve, used to quantify diagnostic accuracy. No unit (a ratio or percentage, typically expressed as a number between 0 and 1).
Measured: 1 year
F1 Score
The F1 score is the harmonic mean of precision and sensitivity (recall). It is a good measure of the model's ability to identify both true positives and minimize false positives, especially in cases where the classes are imbalanced (e.g., when the number of healthy cases is much higher than disease cases). The F1 score ranges from 0 to 1, with 1 indicating perfect precision and recall.
Measured: 1 year
Secondary outcomes (2)
Sensitivity (True Positive Rate)
Sensitivity measures how well the AI model identifies true positive cases, such as correctly diagnosing pregnant women with complications or identifying neonatal disorders.
Measured: 1 year
Specificity (True Negative Rate)
Specificity measures the ability of the AI model to correctly identify cases without diseases, ensuring that healthy mothers and infants are correctly identified as negative.
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Jan 2025
- Study start1 Feb 2023
- Primary completionMay 2025 (estimated)
- Study completionMay 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Hospital-acquired InfectionsStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Other
- Enrolment
- 1,000,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- The Eye Hospital of Wenzhou Medical University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Kang Zhang, Wenzhou Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06791382
- Sponsor's protocol ID
- Hospital-Acquired Infections
- Status last verified
- Apr 2025
- First submitted
- 19 Jan 2025
- Last updated
- 17 Apr 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 17 Apr 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Predicting Hospital-Acquired Infections Using Electronic Health Records: An AI-Assisted Approach
- ClinicalTrials.gov ID
- NCT06791382
- Acronym / sponsor ID
- Hospital-Acquired Infections
- Conditions
- Hospital-acquired Infections
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 1,000,000 participants (estimated)
- Lead sponsor
- The Eye Hospital of Wenzhou Medical UniversityUniversity, hospital or other
- Study start
- 1 Feb 2023 (actual)
- Primary completion
- May 2025 (estimated)
- Study completion
- May 2025 (estimated)
- First posted
- 24 Jan 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Case-control
- Time perspective
- Other
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Hospital-Acquired Infection Cohort | – | Diagnostic Test: AI-Based Diagnostic and Prognostic ModelThis 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. |
| Healthy Cohort (No HAI) | – | Diagnostic Test: AI-Based Diagnostic and Prognostic ModelThis 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. |
Who may qualify
- Ages
- 0 to 90 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- 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.
Key inclusion criteria (3 listed)
- Patients with complete and accessible EHR data, including medical history, laboratory test results, treatment regimens, clinical observations, and infection history.
- Patients who have been admitted to the participating hospital or healthcare facility during the study period.
- All participants must provide informed consent to use their health data for research purposes.
Key exclusion criteria (4 listed)
- Patients with incomplete or missing critical EHR data, such as lab results, medical history, or treatment details, which are necessary for infection prediction.
- Patients who have severe cognitive disorders, dementia, or conditions that prevent them from providing informed consent or participating in the study.
- Patients who have not been admitted to the hospital during the study period or who are receiving outpatient care only.
- Patients with terminal conditions where infection prediction may not be applicable to the clinical goals of the study.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-Based Diagnostic and Prognostic ModelThis 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. | Diagnostic test | Healthy Cohort (No HAI), Hospital-Acquired Infection Cohort |
Primary outcome
Area Under the Curve (AUC)
Time frame: 1 year
AUC of the ROC curve, used to quantify diagnostic accuracy. No unit (a ratio or percentage, typically expressed as a number between 0 and 1).
F1 Score
Time frame: 1 year
The F1 score is the harmonic mean of precision and sensitivity (recall). It is a good measure of the model's ability to identify both true positives and minimize false positives, especially in cases where the classes are imbalanced (e.g., when the number of healthy cases is much higher than disease cases). The F1 score ranges from 0 to 1, with 1 indicating perfect precision and recall.
2 secondary outcomes
Sensitivity (True Positive Rate)
Time frame: 1 year
Specificity (True Negative Rate)
Time frame: 1 year
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Apr 2025.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Hospital-acquired Infections, run by The Eye Hospital of Wenzhou Medical University at 2 sites in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 17 Apr 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Fei Liu, MD (+86 13810512704, liufei_2359@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area Under the Curve (AUC) (time frame: 1 year). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 17 Apr 2025.