Refining Risk Prediction Models for Older Adults
This study aims to improve how we predict health risks for older adults using information from their electronic health records (EHRs). Researchers are analyzing existing health data from people aged 65 and older who have at least 5 years of health records and a serum creatinine lab test. The goal is to make a "Risk Prediction Model" more accurate, especially for conditions like chronic kidney disease (CKD) progression, and see if it can be used for other health issues. The study will look at data collected up to 5 years ago. This is an observational study, meaning researchers are just looking at existing data and you won't have any direct interaction or appointments. The current status of the study is unclear, but it plans to include 18,000 participants.
- Study design
- This is an observational study that will analyze existing health records from 18,000 older adults. It is not a clinical trial where you receive a new treatment.
- What's involved
- Not specified in the trial record.
- Compensation
- Not stated in the trial record.
- Follow-up
- The study will look at your health records for up to 5 years of past information.
AI-generated from the public study record. Only the study team can confirm whether you're eligible — confirm details with them before making decisions.
Refining Risk Prediction Models for Older Adults Using Electronic Health Records
At a glance
Conditions
NCT06995365
Where you'd take part
This study runs at 1 site. They're the same protocol — you choose where, and that choice sets who your contact draft is addressed to.
UCLA Health System
Los Angeles, Californiano site contact published
Sites open and close at different times, so the status above is per site — it can differ from the study's overall status.
Who to contact
Opens a ready-to-send draft in your own email app — review before sending.
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Inclusion
Exclusion
What this trial measures
- Performance of the Risk Prediction ModelUp 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.