NCT06791447

AI-Driven Prediction of Dialysis Outcome With EHR

Predicting Clinical Outcomes in Dialysis Patients Using Electronic Health Records: An AI-Based Approach

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 1,000,000 (estimated)
• Sponsor: The Eye Hospital of Wenzhou Medical University · Academic / Other
• Sex: All
• Age: 20 Years – 100 Years
• Healthy volunteers: Not accepted

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.

Conditions

• Dialysis Patients

Interventions

Type	Name	Description
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.

Timeline

• Start date: 2023-01-01
• Primary completion: 2025-05-01
• Completion: 2025-05-01
• First posted: 2025-01-24
• Last updated: 2025-04-17

Locations

1 site across 1 country: China