Study to Develop a Tool to Estimate the Kidney Function in Databases Without Laboratory Data
| Status: | Completed |
|---|---|
| Conditions: | Renal Impairment / Chronic Kidney Disease |
| Therapuetic Areas: | Nephrology / Urology |
| Healthy: | No |
| Age Range: | 18 - Any |
| Updated: | 3/27/2019 |
| Start Date: | July 15, 2018 |
| End Date: | February 28, 2019 |
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.
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.
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.
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.
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