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NCT Number: NCT06791447

AI-Driven Prediction of Dialysis Outcome With EHR

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.

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Key information

Conditions

Dialysis Patients

Age range

20 year–100 year

Sex eligibility

All sexes

Study type

Observational

Primary location

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • 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.

Exclusion criteria

  • 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.

Treatment and study plan

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.

Primary outcomes

  1. Mortality Prediction Accuracy

    Time frame: 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 outcomes

  1. Complications Prediction Accuracy

    Time frame: 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.

Study contacts

Contact information is provided by the study sponsor or research team.

Fei Liu, MD

CONTACT

[email protected]

+86 13810512704

Sponsors and collaborators

Lead sponsor

The Eye Hospital of Wenzhou Medical University

Other

Registry information

Official study title

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

Important dates

Study start
2023
Primary completion
2025
Study completion
2025
First posted
Jan 24, 2025
Registry last updated
Apr 17, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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