Loading clinical trials...
Loading clinical trials...
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.
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.
Age
18 - No limit years
Sex
ALL
Healthy Volunteers
No
US OPTUM CDM database
Whippany, New Jersey, United States
Start Date
July 15, 2018
Primary Completion Date
December 31, 2018
Completion Date
December 31, 2018
Last Updated
December 10, 2019
5,132,200
ACTUAL participants
No Intervention
OTHER
Lead Sponsor
Bayer
Data Source & Attribution
This clinical trial information is sourced from ClinicalTrials.gov, a service of the U.S. National Institutes of Health.
Modifications: This data has been reformatted for display purposes. Eligibility criteria have been parsed into inclusion/exclusion sections. Location data has been geocoded to enable distance-based search. For the authoritative and most current information, please visit ClinicalTrials.gov.
Neither the United States Government nor Clareo Health make any warranties regarding the data. Check ClinicalTrials.gov frequently for updates.
View ClinicalTrials.gov Terms and ConditionsNCT06968182