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Study to Develop a Tool to Estimate the Kidney Function in Databases Without Laboratory Data

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ClinicalTrials.gov Identifier: NCT03605810
Recruitment Status : Completed
First Posted : July 30, 2018
Last Update Posted : March 25, 2019
Sponsor:
Collaborator:
Rocket Science OÜ
Information provided by (Responsible Party):
Bayer

Brief Summary:

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.


Condition or disease Intervention/treatment
Renal Function Other: No Intervention

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.


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Study Type : Observational
Actual Enrollment : 99999 participants
Observational Model: Cohort
Time Perspective: Retrospective
Official Title: An Estimated Glomerular Filtration Rate (eGFR) Level Prediction
Actual Study Start Date : July 15, 2018
Actual Primary Completion Date : February 28, 2019
Actual Study Completion Date : February 28, 2019

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Kidney Tests

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.
Other: 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.

Other: 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.
Other: 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.
Other: 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).




Primary Outcome Measures :
  1. Performance of classification to predict eGFR [ Time Frame: From eGRF values starting and lasting 180d + 370d ]

    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



Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.


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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
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.
Criteria
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.

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03605810


Locations
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United States, New Jersey
US OPTUM CDM database
Whippany, New Jersey, United States, 07981
Sponsors and Collaborators
Bayer
Rocket Science OÜ

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Responsible Party: Bayer
ClinicalTrials.gov Identifier: NCT03605810     History of Changes
Other Study ID Numbers: 20325
First Posted: July 30, 2018    Key Record Dates
Last Update Posted: March 25, 2019
Last Verified: March 2019

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Bayer:
Renal function, eGRF, Atrial fibrillation, Coronary artery disease, Type 2 diabetes mellitus, Machine learning, Prognostic modeling