Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.
RECRUITING
1000000 participants
OBSERVATIONAL
2023-01-01
2025-05-01
Brief Summary
Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.
Related Clinical Trials
Explore similar clinical trials based on study characteristics and research focus.
Incremental Dialysis Decision Model Based on Expert-Guided Machine Learning
NCT06775067
Machine Learning and Artificial Intelligence Algorithms to Optimize the Performance and Delivery of Acute Dialysis
NCT07312929
Emotion and Cognitive Function and Brain Imaging Change in HD Patients
NCT05137470
High-flux Hemodialysis Versus Hemodiafiltration for End-Stage Renal Disease
NCT03456232
Early Detection, Remote Augmented Reality Combined Rehabilitation Therapy, and Translational Therapy for Patients With Chronic Kidney Disease and Cognitive Disorder
NCT07272733
Detailed Description
Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.
Conditions
See the medical conditions and disease areas that this research is targeting or investigating.
Study Design
Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.
COHORT
PROSPECTIVE
Study Groups
Review each arm or cohort in the study, along with the interventions and objectives associated with them.
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.
AI-assisted Predictive Model for Dialysis Outcomes
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.
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.
AI-assisted Predictive Model for Dialysis Outcomes
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.
Interventions
Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.
AI-assisted Predictive Model for Dialysis Outcomes
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.
Eligibility Criteria
Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.
Inclusion Criteria
2. Complete and accessible EHR data, including medical history, laboratory test results, dialysis treatment details, and clinical observations.
3. Participants must provide informed consent for the use of their health data for research purposes.
Exclusion Criteria
2. Patients who have been on dialysis for less than 3 months, to ensure stable data for outcome prediction.
20 Years
100 Years
ALL
No
Sponsors
Meet the organizations funding or collaborating on the study and learn about their roles.
The Eye Hospital of Wenzhou Medical University
OTHER
Responsible Party
Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.
Kang Zhang
Chief Scientist
Locations
Explore where the study is taking place and check the recruitment status at each participating site.
General Hospital of PLA
Beijing, Beijing Municipality, China
Countries
Review the countries where the study has at least one active or historical site.
Central Contacts
Reach out to these primary contacts for questions about participation or study logistics.
Facility Contacts
Find local site contact details for specific facilities participating in the trial.
Other Identifiers
Review additional registry numbers or institutional identifiers associated with this trial.
Dialysis
Identifier Type: -
Identifier Source: org_study_id
More Related Trials
Additional clinical trials that may be relevant based on similarity analysis.