AI-Driven Prediction of Dialysis Outcome With EHR

Recruiting

• Conditions: Dialysis Patients

• Registration Number: NCT06791447
• Lead Sponsor: The Eye Hospital of Wenzhou Medical University

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.

Study Timeline

• Study Start: 2023-01-01
• Study First Submitted: 2025-01-19
• Study First Submitted QC: 2025-01-19
• Study First Posted: 2025-01-24
• Status Verified: 2025-04
• Last Update Submitted: 2025-04-16
• Last Update Posted: 2025-04-17
• Primary Completion: 2025-05
• Study Completion: 2025-05-01

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 1000000

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.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
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 Outcome Measures

Name	Time	Method
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.

Trial Locations

Locations (1)

General Hospital of PLA; 🇨🇳 Beijing, Beijing, China