AI-Driven Prediction of Dialysis Outcome With EHR

ConditionDialysis Patients

Trial statusRecruiting

Trial phaseNot listed

Trial typeObservational

Biological sexAll

Age20-100

SponsorThe Eye Hospital of Wenzhou Medical University

About this trial

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.

Eligibility criteria

This trial does not accept healthy volunteers

Qualifiers

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.

Disqualifiers

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 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.

Trial design

Design model

Cohort

Time perspective

Prospective

Treatments tested in this trial

AI-assisted Predictive Model for Dialysis Outcomes
Other intervention
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.

Treatment groups

1,000,000 Participants

are divided into 2 treatment groups

Group A: High Risk Group1 intervention

Group B: Low Risk Group1 intervention

Trial outcomes

Primary outcomes

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

Secondary outcomes

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

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

The Eye Hospital of Wenzhou Medical University

Lead sponsor

Wenzhou Medical University

Sponsor institution

Source ClinicalTrials.gov

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