Clinical trial NCT03605810: Study to Develop a Tool to Estimate the Kidney Function in Databases Without Laboratory Data
Official title: An Estimated Glomerular Filtration Rate (eGFR) Level Prediction
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Renal Function
Keywords: Renal function, eGRF, Atrial fibrillation, Coronary artery disease, Type 2 diabetes mellitus, Machine learning, Prognostic modeling
Lead sponsor: Bayer
Sponsor class: INDUSTRY
First posted: 2018-07-30
Enrollment: 5132200
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
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.

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.

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.

Eligibility 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.

Primary outcomes:
measure: Performance of classification to predict eGFR
timeFrame: From eGRF values starting and lasting 180d + 370d
Secondary outcomes:
Other outcomes:
Locations:
city: Whippany
country: United States
facility: US OPTUM CDM database
state: New Jersey
Study officials:
References: