Predictive ModelingNCT06995365
Refining Risk Prediction Models for Older Adults Using Electronic Health Records
An observational study of Predictive Modeling, run by University of California, Los Angeles at 1 site in United States.
Trial 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. Read more about this study
- Who is it for?
- Ages 65 years and older
- Conditions the study is for
- Predictive Modeling
- What is being tested?
- Risk Prediction Model
- How many people
- 18,000 participants
- Where
- 1 siteUnited States
- Study dates
- Sep 2026to Mar 2028
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 65 years and older
- All sexes
- No healthy volunteers
- Older adult
Study population: Adults aged 65 and older who received care within the UCLA or UC Health system, have at least 5 years of clinical follow-up, and have had a serum creatinine test. Data are drawn from existing electronic health records.
Who may be able to join
- being over the age of 65; having at least 5 years of clinical follow up; and having a serum creatinine lab test conducted
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
About this study
Official title: Patient-centered Precision Medicine Lab Result Communication for Older Adults - Validation and Refinement of an Existing Chronic Kidney Disease (CKD) Risk Model
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.
- Conditions
- Predictive Modeling
- Being tested
- Risk Prediction Model
- Keywords
- Lab Result Communication, Risk Stratification
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Risk Prediction ModelOther
This study analyzes retrospective electronic health record (EHR) data from older adults to refine and validate a predictive model for other conditions in future studies.
What is being measured
The primary outcome is the main question the study is designed to answer.
Performance of the Risk Prediction Model
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.
Measured: Up to 5 years of retrospective follow up
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted29 May 2025
- Study startSep 2026 (estimated)
- Primary completionDec 2027 (estimated)
- Study completionMar 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Predictive ModelingStudies of the same condition, open and under way.
- More trials run by University of California, Los AngelesThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 18,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of California, Los Angeles
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Catherine A. Sarkisian, University of California, Los Angeles
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06995365
- Sponsor's protocol ID
- IRB-26-1370
- Status last verified
- Jul 2026
- First submitted
- 20 May 2025
- Last updated
- 24 Jul 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 24 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Patient-centered Precision Medicine Lab Result Communication for Older Adults - Validation and Refinement of an Existing Chronic Kidney Disease (CKD) Risk Model
- ClinicalTrials.gov ID
- NCT06995365
- Acronym / sponsor ID
- IRB-26-1370
- Conditions
- Predictive Modeling
- Keywords
- Lab Result Communication, Risk Stratification
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 18,000 participants (estimated)
- Lead sponsor
- University of California, Los AngelesUniversity, hospital or other
- Study start
- Sep 2026 (estimated)
- Primary completion
- Dec 2027 (estimated)
- Study completion
- Mar 2028 (estimated)
- First posted
- 29 May 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 65 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Older adult
- Study population
- Adults aged 65 and older who received care within the UCLA or UC Health system, have at least 5 years of clinical follow-up, and have had a serum creatinine test. Data are drawn from existing electronic health records.
Key inclusion criteria (1 listed)
- being over the age of 65; having at least 5 years of clinical follow up; and having a serum creatinine lab test conducted
Key exclusion criteria (3 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Risk Prediction ModelThis study analyzes retrospective electronic health record (EHR) data from older adults to refine and validate a predictive model for other conditions in future studies. | Other | – |
Primary outcome
Performance of the Risk Prediction Model
Time frame: 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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 24 Jul 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Predictive Modeling, run by University of California, Los Angeles at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 24 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Katelyn Nguyen (13102675250, katenguyen@mednet.ucla.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Performance of the Risk Prediction Model (time frame: Up to 5 years of retrospective follow up). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 24 Jul 2026.