Refining Risk Prediction Models for Older Adults Using Electronic Health Records
- Conditions
- Predictive Modeling
- Registration Number
- NCT06995365
- Lead Sponsor
- University of California, Los Angeles
- Brief Summary
This study aims to improve how lab results are communicated to older adults by refining a predictive model that uses electronic health record (EHR) data. The model was originally developed to estimate the risk of chronic kidney disease (CKD) progression. Researchers will use existing health data to test and improve the accuracy of the model and explore how it might be adapted for use in other health conditions. The study does not involve direct interaction with patients and is conducted entirely using de-identified data in a secure environment.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- NOT_YET_RECRUITING
- Sex
- All
- Target Recruitment
- 18000
Not provided
- Patients younger than 65 years old
- Patients with less than 5 years of clinical follow-up
- Patients from health systems outside of the UC Health network.
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Performance of the Risk Prediction Model Up to 5 years of retrospective follow up Evaluate the predictive performance of a machine learning-based risk model using retrospective Electronic Health Records (EHR) data. The model estimates the likelihood of disease progression in older adults. The model should be designed to be adaptable to various clinical conditions. Metrics include Area Under the Receiver Operating Characteristic Curve (AUC-ROC), sensitivity, and specificity.
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (1)
UCLA Health System
🇺🇸Los Angeles, California, United States
UCLA Health System🇺🇸Los Angeles, California, United States