Dialysis PatientsNCT06791447
AI-Driven Prediction of Dialysis Outcome With EHR
An observational study of Dialysis Patients, run by The Eye Hospital of Wenzhou Medical University at 1 site 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 outcome of dialysis patients, leveraging multimodal health data. Read more about this study
- Who is it for?
- Ages 20 to 100 years
- Conditions the study is for
- Dialysis Patients
- What is being tested?
- AI-assisted Predictive Model for Dialysis Outcomes
- How many people
- 1,000,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2023to 1 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 20 to 100 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The study population consists of dialysis patients from the China Hemodialysis National Network Center, which includes a wide range of patients undergoing hemodialysis treatment at participating hospitals across China. Participants will be selected based on the availability of comprehensive electronic health records (EHR), including medical history, laboratory test results, dialysis treatment details, and clinical observations. The cohort will include both male and female patients, with varying degrees of health status, including those with comorbidities commonly associated with dialysis. The study aims to utilize this diverse group to assess and predict outcomes related to mortality and complications in dialysis patients.
Who may be able to join
- Patients who have been undergoing dialysis (either hemodialysis or peritoneal dialysis) for at least 3 months.
- Complete and accessible EHR data, including medical history, laboratory test results, dialysis treatment details, and clinical observations.
- Participants must provide informed consent for the use of their health data for research purposes.
Who cannot join
- Patients with incomplete or missing critical EHR data, including medical history, laboratory results, dialysis data, or treatment details necessary for the study.
- Patients who have been on dialysis for less than 3 months, to ensure stable data for outcome prediction.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
General Hospital of PLA
Beijing, Beijing Municipality
Delong Zhao, +86 13810512704, feiliu0108@gmail.com
About this study
Official title: Predicting Clinical Outcomes in Dialysis Patients Using Electronic Health Records: An AI-Based Approach
This is a multi-center, clinical study designed to evaluate the application and effectiveness of an AI-assisted predictive model for outcome of dialysis patients, leveraging multimodal health data.
Read the detailed description
This study aims to develop an AI-assisted model to predict clinical outcomes in dialysis patients, focusing on both primary outcomes (e.g., mortality) and intermediate outcomes (e.g., anemia, blood pressure, nutritional status, and calcium-phosphate metabolism). The study will utilize patients' EHR data, including laboratory test results, medical history, dialysis treatment information, and clinical observations, to predict these health outcomes. The goal is to improve early identification of at-risk patients, enabling better clinical decision-making and personalized care strategies.
- Conditions
- Dialysis Patients
- Being tested
- AI-assisted Predictive Model for Dialysis Outcomes
- Keywords
- Dialysis Patients, AI-Assisted Prediction
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-assisted Predictive Model for Dialysis OutcomesOther
This study utilizes an AI-assisted predictive model that analyzes multimodal data from electronic health records, including medical history, laboratory results, dialysis treatment details, and clinical observations, to predict outcomes for dialysis patients. The model employs deep learning algorithms to predict mortality risk, intermediate outcomes such as anemia, blood pressure control, nutrition, and calcium-phosphate metabolism, and helps identify early signs of deterioration. The intervention is not a direct treatment or procedure but aims to develop a tool for predicting patient outcomes and optimizing treatment strategies to improve overall health and survival rates for dialysis patients.
| Study group | Role | What they receive |
|---|---|---|
| High Risk Group | Participants predicted to have a high risk of mortality based on AI-assisted prediction models using their EHR data, including medical history, lab results, dialysis treatment details, and clinical observations. Receives: Other: AI-assisted Predictive Model for Dialysis Outcomes | |
| Low Risk Group | Participants predicted to have a low risk of mortality based on the AI-assisted prediction model, who will be compared with the high-risk group for evaluating the effectiveness of early intervention strategies. Receives: Other: AI-assisted Predictive Model for Dialysis Outcomes |
What is being measured
The primary outcome is the main question the study is designed to answer.
Mortality Prediction Accuracy
The ability of the AI-assisted predictive model to accurately predict the risk of mortality in dialysis patients. Prediction accuracy will be assessed using the Area Under the Curve (AUC), F1 score, and sensitivity/specificity. The model will be evaluated by comparing the predicted mortality risk with actual outcomes (i.e., whether patients survived or passed away during the study period).
Measured: 1 year
Secondary outcomes (1)
Complications Prediction Accuracy
The accuracy of the AI-assisted predictive model in forecasting complications commonly experienced by dialysis patients, including anemia, uncontrolled blood pressure, poor nutritional status, and abnormalities in calcium-phosphate metabolism. The model's performance will be assessed using metrics such as AUC, F1 score, and accuracy by comparing predicted values to actual clinical outcomes, such as lab results, clinical diagnoses, and patient health status.
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 Jan 2023
- Primary completionMay 2025 (estimated)
- Study completion1 May 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Dialysis PatientsStudies 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
- Cohort
- Time perspective
- Prospective
- 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
- NCT06791447
- Sponsor's protocol ID
- Dialysis
- 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 Clinical Outcomes in Dialysis Patients Using Electronic Health Records: An AI-Based Approach
- ClinicalTrials.gov ID
- NCT06791447
- Acronym / sponsor ID
- Dialysis
- Conditions
- Dialysis Patients
- Keywords
- Dialysis Patients, AI-Assisted Prediction
- 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 Jan 2023 (actual)
- Primary completion
- May 2025 (estimated)
- Study completion
- 1 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 outcome of dialysis patients, leveraging multimodal health data.
Detailed description
This study aims to develop an AI-assisted model to predict clinical outcomes in dialysis patients, focusing on both primary outcomes (e.g., mortality) and intermediate outcomes (e.g., anemia, blood pressure, nutritional status, and calcium-phosphate metabolism). The study will utilize patients' EHR data, including laboratory test results, medical history, dialysis treatment information, and clinical observations, to predict these health outcomes. The goal is to improve early identification of at-risk patients, enabling better clinical decision-making and personalized care strategies.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| High Risk Group | – | Other: AI-assisted Predictive Model for Dialysis OutcomesParticipants predicted to have a high risk of mortality based on AI-assisted prediction models using their EHR data, including medical history, lab results, dialysis treatment details, and clinical observations. |
| Low Risk Group | – | Other: AI-assisted Predictive Model for Dialysis OutcomesParticipants predicted to have a low risk of mortality based on the AI-assisted prediction model, who will be compared with the high-risk group for evaluating the effectiveness of early intervention strategies. |
Who may qualify
- Ages
- 20 to 100 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study population consists of dialysis patients from the China Hemodialysis National Network Center, which includes a wide range of patients undergoing hemodialysis treatment at participating hospitals across China. Participants will be selected based on the availability of comprehensive electronic health records (EHR), including medical history, laboratory test results, dialysis treatment details, and clinical observations. The cohort will include both male and female patients, with varying degrees of health status, including those with comorbidities commonly associated with dialysis. The study aims to utilize this diverse group to assess and predict outcomes related to mortality and complications in dialysis patients.
Key inclusion criteria (3 listed)
- Patients who have been undergoing dialysis (either hemodialysis or peritoneal dialysis) for at least 3 months.
- Complete and accessible EHR data, including medical history, laboratory test results, dialysis treatment details, and clinical observations.
- Participants must provide informed consent for the use of their health data for research purposes.
Key exclusion criteria (2 listed)
- Patients with incomplete or missing critical EHR data, including medical history, laboratory results, dialysis data, or treatment details necessary for the study.
- Patients who have been on dialysis for less than 3 months, to ensure stable data for outcome prediction.
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-assisted Predictive Model for Dialysis OutcomesThis study utilizes an AI-assisted predictive model that analyzes multimodal data from electronic health records, including medical history, laboratory results, dialysis treatment details, and clinical observations, to predict outcomes for dialysis patients. The model employs deep learning algorithms to predict mortality risk, intermediate outcomes such as anemia, blood pressure control, nutrition, and calcium-phosphate metabolism, and helps identify early signs of deterioration. The intervention is not a direct treatment or procedure but aims to develop a tool for predicting patient outcomes and optimizing treatment strategies to improve overall health and survival rates for dialysis patients. | Other | High Risk Group, Low Risk Group |
Primary outcome
Mortality Prediction Accuracy
Time frame: 1 year
The ability of the AI-assisted predictive model to accurately predict the risk of mortality in dialysis patients. Prediction accuracy will be assessed using the Area Under the Curve (AUC), F1 score, and sensitivity/specificity. The model will be evaluated by comparing the predicted mortality risk with actual outcomes (i.e., whether patients survived or passed away during the study period).
1 secondary outcome
Complications Prediction Accuracy
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.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Dialysis Patients, run by The Eye Hospital of Wenzhou Medical University at 1 site 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 2 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 1 site 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?
Mortality Prediction Accuracy (time frame: 1 year). 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.