- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT03605810
Study to Develop a Tool to Estimate the Kidney Function in Databases Without Laboratory Data
An Estimated Glomerular Filtration Rate (eGFR) Level Prediction
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.
Study Overview
Detailed Description
Renal impairment is a common comorbidity in patients with diverse main underlying diseases and a pathology accompanying increasing age. Renal function might be an important modifier of treatment effects.
Population-based administrative claims databases are increasingly used in large-scale comparative outcomes studies of drug treatments. However, claims databases often lack information on laboratory tests results limiting their usefulness in Real-World Evidence(RWE) research of patients with renal impairment.
There is a need to develop methods for identification of patients with renal dysfunction from healthcare administrative claims-based proxies.
The main objective of this study is the development of algorithms/models to predict eGFR values and/or classes for patients at certain time point based on entries in claims database (demographic characteristics, clinical diagnoses, procedures and drug treatments) for a general population and a variety of use-cases (atrial fibrillation, coronary artery disease, type 2 diabetes mellitus patients sub-populations). To achieve this, modern data-driven machine learning techniques will be applied to discover relationships between renal status, measured by eGFR, and longitudinal patient-level data.
Evaluation of models' performance (out of sample validation, benchmark test, performance differences between eGFR value prediction algorithms and classification models tailored for the pre-defined eGFR classes) will be done as well.
Study Type
Enrollment (Actual)
Contacts and Locations
Study Locations
-
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New Jersey
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Whippany, New Jersey, United States, 07981
- US OPTUM CDM database
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Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Adult patients with at least one recorded eGFR value in the OPTUM CDM database between January 1, 2007 and December 31, 2016 will be included in the use-case 1 "eGFR population". Further cases refer to the sub-populations of the eGFR-population, namely
- Atrial fibrillation (AF) sub-population;
- Coronary artery disease (CAD) sub-population;
- Type 2 diabetes mellitus (T2DM) sub-population.
Description
Study Plan
How is the study designed?
Design Details
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
---|---|
eGFR-population
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.
|
This study is the development of algorithms/models to predict eGFR values and/or classes for patients at certain time point based on entries in claims database (demographic characteristics, clinical diagnoses, procedures and drug treatments) for a general population and a variety of use-cases (AF, CAD, T2DM patients sub-populations).
|
Atrial fibrillation (AF) sub-population
To be included in the AF sub-population patients need to satisfy the inclusion criteria for the eGFR-population; have two inpatient or outpatient diagnoses for AF or atrial flutter on two different days within the study period irrespective of time points when eGFR is measured. Patients with at least one inpatient or outpatient diagnosis or procedure code for mitral stenosis and prosthetic valves within the study period will be excluded. |
This study is the development of algorithms/models to predict eGFR values and/or classes for patients at certain time point based on entries in claims database (demographic characteristics, clinical diagnoses, procedures and drug treatments) for a general population and a variety of use-cases (AF, CAD, T2DM patients sub-populations).
|
Coronary artery disease (CAD) sub-population
To be included in the CAD sub-population patients need to satisfy the inclusion criteria for the eGFR-population; have at least one inpatient CAD diagnosis within the study period irrespective of time points when eGFR is measured.
|
This study is the development of algorithms/models to predict eGFR values and/or classes for patients at certain time point based on entries in claims database (demographic characteristics, clinical diagnoses, procedures and drug treatments) for a general population and a variety of use-cases (AF, CAD, T2DM patients sub-populations).
|
Type 2 diabetes mellitus (T2DM) sub-population
To be included in the T2DM sub-population patients need to satisfy the inclusion criteria for the eGFR-population; have at least two inpatient or outpatient diagnosis of T2DM on two different days within the study period irrespective of time points when eGFR is measured.
|
This study is the development of algorithms/models to predict eGFR values and/or classes for patients at certain time point based on entries in claims database (demographic characteristics, clinical diagnoses, procedures and drug treatments) for a general population and a variety of use-cases (AF, CAD, T2DM patients sub-populations).
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Performance of classification to predict eGFR
Time Frame: From eGRF values starting and lasting 180d + 370d
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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:
Class predicted by model:
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From eGRF values starting and lasting 180d + 370d
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Collaborators and Investigators
Sponsor
Publications and helpful links
Study record dates
Study Major Dates
Study Start (ACTUAL)
Primary Completion (ACTUAL)
Study Completion (ACTUAL)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (ACTUAL)
Study Record Updates
Last Update Posted (ACTUAL)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Other Study ID Numbers
- 20325
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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