Renal Function Clinical Trial
Official title:
An Estimated Glomerular Filtration Rate (eGFR) Level Prediction
NCT number | NCT03605810 |
Other study ID # | 20325 |
Secondary ID | |
Status | Completed |
Phase | |
First received | |
Last updated | |
Start date | July 15, 2018 |
Est. completion date | December 31, 2018 |
Verified date | December 2019 |
Source | Bayer |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Observational |
Scientific analyses are frequently performed on e.g. health insurance databases to study the
usage and effectiveness of drugs in real life.
Kidney function is known to have an influence on a patients disease development and/or drug
levels in blood.
However, often direct measures for kidney function are not available in databases.
This study plans to develop tools to classify the renal function of patients, which helps
scientists to identify patient cohorts (groups of patients sharing same characteristics) for
scientific analyses.
Status | Completed |
Enrollment | 5132200 |
Est. completion date | December 31, 2018 |
Est. primary completion date | December 31, 2018 |
Accepts healthy volunteers | No |
Gender | All |
Age group | 18 Years and older |
Eligibility | To be included in the eGFR-population, patients have to have at least one recorded eGFR value in the OPTUM CDM database between January 1, 2007 and December 31, 2016, be adults (>18 years of age at the time of eGFR test) and have at least 370/180 days (180 days serves as sensitivity analysis) of continuous enrollment in medical and pharmacy insurance plans since eGFR test date. |
Country | Name | City | State |
---|---|---|---|
United States | US OPTUM CDM database | Whippany | New Jersey |
Lead Sponsor | Collaborator |
---|---|
Bayer |
United States,
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Performance of classification to predict eGFR | For numeric models cross-validated performance is measured as correlation via r*2. Class based performances are measured as cross-validated sensitivities given pre-defined false discovery rates with following definition for positives and negatives: Observed eGFR class X: positive: eGFR measured at begin of time frame is in class X negative: eGFR measured at begin of time frame is not in class X Class predicted by model: positive: eGFR predicted is class X negative: eGFR predicted is not class X |
From eGRF values starting and lasting 180d + 370d |
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