Dialysis Patients
Conditions
Keywords
Dialysis Patients, AI-Assisted Prediction
Brief 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.
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
OTHERAI-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.
Sponsors
The Eye Hospital of Wenzhou Medical University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
20 Years to 100 Years
Healthy volunteers
No
Inclusion criteria
1. Patients who have been undergoing dialysis (either hemodialysis or peritoneal dialysis) for at least 3 months. 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
1. Patients with incomplete or missing critical EHR data, including medical history, laboratory results, dialysis data, or treatment details necessary for the study. 2. Patients who have been on dialysis for less than 3 months, to ensure stable data for outcome prediction.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Mortality Prediction Accuracy | 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). |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Complications Prediction Accuracy | 1 year | 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. |
Countries
China
Contacts
Primary ContactFei Liu, MD
Outcome results
None listed