AI-Driven Prediction of Dialysis Outcome With EHR
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: AI-assisted Predictive Model for Dialysis Outcomes.
- Who it may be relevant to
- Registry conditions: Dialysis Patients. Basic parameters: 20 years — 100 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Unsure about the terms? Read our patient guide →
Official title
Predicting Clinical Outcomes in Dialysis Patients Using Electronic Health Records: An AI-Based Approach
Overview
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.
Interventions
- Other 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 interventio
Primary outcome measures
- Mortality Prediction Accuracy [Time frame: 1 year]
Secondary outcome measures (1)
- Complications Prediction Accuracy [Time frame: 1 year]
Eligibility criteria
Inclusion criteria
- 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.
Exclusion criteria
- 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.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
China · 1 center
- General Hospital of PLA — Beijing
Identifiers
NCT: NCT06791447 · Dialysis