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Clinical Trial Details — Status: Completed

Administrative data

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

Study information

Verified date December 2019
Source Bayer
Contact n/a
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial 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.


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.


Recruitment information / eligibility

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.

Study Design


Related Conditions & MeSH terms


Intervention

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).

Locations

Country Name City State
United States US OPTUM CDM database Whippany New Jersey

Sponsors (1)

Lead Sponsor Collaborator
Bayer

Country where clinical trial is conducted

United States, 

Outcome

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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