An observational study in Dialysis Patients, sponsored by The Eye Hospital of Wenzhou Medical University. Recruiting at 1 site in China. Open to participants aged 20 Years to 100 Years. Per ClinicalTrials.gov, last updated 2025-04-17.
Sponsored by The Eye Hospital of Wenzhou Medical University · Observational
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.
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.
The Eye Hospital of Wenzhou Medical University is the lead sponsor of 15 studies on the registry; 10 are open to participants now.
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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.
Exclusion Criteria:
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.
Other: AI-assisted Predictive Model for Dialysis Outcomes
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.
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 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.
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).
Time frame: 1 year
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.
Time frame: 1 year
Plan to share: No
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The Eye Hospital of Wenzhou Medical University