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.
Study Type
OBSERVATIONAL
Enrollment
5,132,200
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).
US OPTUM CDM database
Whippany, New Jersey, United States
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
Time frame: From eGRF values starting and lasting 180d + 370d
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