AI-Driven Prediction of Dialysis Outcome With EHR

NCT06791447 | Recruiting

• 1 other identifier: Dialysis
• Study Type: observational
• Target: 1,000,000
• Locations: 1 country
• Sites: 1

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.

Conditions

Dialysis Patients

Keywords

Dialysis Patients; AI-Assisted Prediction

Outcome Measures

Primary Outcomes (1)

• 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).

  1 year

Secondary Outcomes (1)

• Complications Prediction Accuracy

  1 year

Study Arms (2)

High Risk Group

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

Low Risk Group

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

Interventions

AI-assisted Predictive Model for Dialysis Outcomes OTHER

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.

High Risk Group; Low Risk Group

Eligibility Criteria

• Age: 20 Years - 100 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study 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.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• The Eye Hospital of Wenzhou Medical University: lead

Study Sites (1)

General Hospital of PLA

Beijing, Beijing Municipality, China

RECRUITING

Central Study Contacts

Fei Liu, MD

CONTACT

+86 13810512704; liufei_2359@163.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Chief Scientist

Study Record Dates

• First Submitted: January 19, 2025
• First Posted: January 24, 2025
• Study Start: January 1, 2023
• Primary Completion: May 1, 2025
• Study Completion: May 1, 2025
• Last Updated: April 17, 2025

Record last verified: 2025-04

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)