Clinical trial NCT06791486: AI-Driven Prediction of Biological Age With EHR
Official title: Predicting Biological Age Using Electronic Health Records: An AI-Based Approach
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Biological Age
Keywords: Biological Age
electronic health records
AI prediction
aging
Lead sponsor: The Eye Hospital of Wenzhou Medical University
Sponsor class: OTHER
First posted: 2025-01-24
Enrollment: 1000000
Sex: ALL
Minimum age (years): 0
Maximum age (years): 100
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: RETROSPECTIVE
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.

Brief summary: 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.

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.

Eligibility criteria: 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.

Primary outcomes:
measure: Biological Age Prediction Accuracy
timeFrame: 1 year
Secondary outcomes:
measure: Health Risk Correlation
timeFrame: 1 year
Other outcomes:
Locations:
city: Guangzhou
country: China
facility: Nanfang Hospital
state: Guangdong
city: Wenzhou
country: China
facility: First Affiliated Hospital of Wenzhou Medical University
state: Zhejiang
city: Wenzhou
country: China
facility: Second Affiliated Hospital of Wenzhou Medical University
state: Zhejiang
city: Wenzhou
country: China
facility: The Eye Hospital of Wenzhou Medical University
state: Zhejiang
Study officials:
References: