Study to Develop a Tool to Estimate the Kidney Function in Databases Without Laboratory Data
NCT ID: NCT03605810
Last Updated: 2019-12-10
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
5132200 participants
OBSERVATIONAL
2018-07-15
2018-12-31
Brief Summary
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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.
Detailed Description
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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.
Conditions
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Keywords
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Study Design
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COHORT
RETROSPECTIVE
Study Groups
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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.
No Intervention
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.
No Intervention
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.
No Intervention
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.
No Intervention
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).
Interventions
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No Intervention
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).
Eligibility Criteria
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Inclusion Criteria
18 Years
ALL
No
Sponsors
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Bayer
INDUSTRY
Responsible Party
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Locations
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US OPTUM CDM database
Whippany, New Jersey, United States
Countries
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Related Links
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Other Identifiers
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20325
Identifier Type: -
Identifier Source: org_study_id