AI-Driven Prediction of Biological Age With EHR

April 1, 2025 updated by: Kang Zhang, The Eye Hospital of Wenzhou Medical University

Predicting Biological Age Using Electronic Health Records: An AI-Based Approach

This is a multi-center, retrospective clinical study designed to evaluate the application and effectiveness of an AI-assisted predictive model for predicting biological age using electronic health records (EHR). The study will analyze various health data points, including medical history, laboratory results, and clinical observations, to estimate the biological age of patients. By comparing biological age with chronological age, the study aims to assess the accuracy of the model and its potential in identifying age-related health risks and improving patient care.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

Biological age prediction is crucial for assessing overall health, determining the risk of age-related diseases, and providing personalized healthcare. While chronological age is a key factor, it does not always reflect an individual's true biological aging process. Early identification of accelerated biological aging and associated health risks can significantly impact early interventions and long-term health outcomes. In clinical practice, healthcare providers integrate a wide range of patient data, including medical history, laboratory test results, and clinical observations, to understand an individual's health status and predict potential future risks. As precision medicine becomes more important, the ability to predict biological age and personalize care plans is essential. Recent advancements in artificial intelligence and data analysis techniques have shown promise in enhancing the accuracy of biological age predictions. This study aims to develop an AI-assisted decision-making system by integrating multimodal data from electronic health records, laboratory results, clinical observations, and patient demographics. The objective is to improve diagnostic accuracy, optimize clinical workflows, and provide more personalized healthcare for patients by predicting biological age, identifying at-risk individuals, and improving health outcomes.

Study Type

Observational

Enrollment (Estimated)

1000000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • China
    • Guangdong
      • Guangzhou, Guangdong, China
    • Zhejiang
      • Wenzhou, Zhejiang, China
        • Recruiting
        • First Affiliated Hospital of Wenzhou Medical University
        • Contact:
      • Wenzhou, Zhejiang, China
        • Recruiting
        • The Eye Hospital of Wenzhou Medical University
        • Contact:
      • Wenzhou, Zhejiang, China
        • Recruiting
        • Second Affiliated Hospital of Wenzhou Medical University
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Non-Probability Sample

Study Population

The study population consists of individuals who have received care at participating hospitals or healthcare centers with accessible electronic health records (EHR). Participants will include those with complete EHR data, including medical history, laboratory test results, imaging data, and lifestyle factors such as diet, physical activity, and smoking habits. The cohort will comprise both individuals who are healthy and those with chronic conditions or comorbidities to analyze biological age prediction across different health statuses. The study will be conducted across multiple healthcare facilities to ensure a diverse patient population representing a wide range of age groups, health conditions, and demographics.

Description

Inclusion Criteria:

  1. Patients with comprehensive and accessible EHR data, including medical history, laboratory results, treatment data, imaging data (if available), and lifestyle factors (e.g., smoking, physical activity, diet).
  2. Patients with no significant cognitive impairments that would prevent them from providing informed consent or participating in the study.
  3. All participants must provide informed consent for the use of their medical data for research purposes.

Exclusion Criteria:

  1. Patients with incomplete or missing critical EHR data such as medical history, laboratory results, or treatment data that are necessary for predicting biological age.
  2. atients with severe cognitive disorders (e.g., dementia, significant mental disabilities) who are unable to provide informed consent or participate meaningfully in the study.
  3. Patients with terminal illnesses or those with limited life expectancy where biological age predictions may not be relevant for the purposes of the study.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Biologically Younger Group
Participants whose biological age is predicted to be younger than their chronological age.
Other: AI-assisted predictive model
This study utilizes an AI-assisted predictive model that analyzes multimodal data from electronic health records, including medical history, laboratory results, imaging data, and lifestyle factors, to estimate biological age. The model employs deep learning algorithms to predict biological age, compare it to chronological age, and identify early signs of age-related health risks. The intervention is not a direct treatment or procedure but aims to develop a tool for predicting biological age to help personalize care and improve long-term health outcomes.
Biologically Older Group
Participants whose biological age is predicted to be older than their chronological age.
Other: AI-assisted predictive model
This study utilizes an AI-assisted predictive model that analyzes multimodal data from electronic health records, including medical history, laboratory results, imaging data, and lifestyle factors, to estimate biological age. The model employs deep learning algorithms to predict biological age, compare it to chronological age, and identify early signs of age-related health risks. The intervention is not a direct treatment or procedure but aims to develop a tool for predicting biological age to help personalize care and improve long-term health outcomes.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Biological Age Prediction Accuracy
Time Frame: 1 year
The accuracy of the AI model in predicting biological age compared to chronological age. This will be evaluated using the Pearson Correlation Coefficient (PCC) to assess the strength of the correlation between predicted biological age and chronological age. Additionally, R-squared (R²) will be used to evaluate the proportion of variance in biological age explained by the model.
1 year

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Health Risk Correlation
Time Frame: 1 year
The correlation between predicted biological age and various health risks, such as the development of chronic diseases (e.g., cardiovascular disease, diabetes), using PCC to evaluate the relationship between biological age predictions and health outcomes.
1 year

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

The Eye Hospital of Wenzhou Medical University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

March 1, 2023

Primary Completion (Estimated)

April 2, 2025

Study Completion (Estimated)

April 2, 2025

Study Registration Dates

First Submitted

January 19, 2025

First Submitted That Met QC Criteria

January 19, 2025

First Posted (Actual)

March 25, 2025

Study Record Updates

Last Update Posted (Actual)

April 2, 2025

Last Update Submitted That Met QC Criteria

April 1, 2025

Last Verified

April 1, 2025

More Information

Terms related to this study

Keywords

Other Study ID Numbers

  • Biological Age

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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