Renal FunctionNCT03605810
Study to Develop a Tool to Estimate the Kidney Function in Databases Without Laboratory Data
An observational study of Renal Function, run by Bayer at 1 site in United States.
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Renal Function
- How many people
- 5,132,200 participants
- Where
- 1 siteUnited States
- Study dates
- 15 Jul 2018to 31 Dec 2018
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
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.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: 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.
Read the 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.
- Conditions
- Renal Function
- Being tested
- No Intervention
- Keywords
- Renal function, eGRF, Atrial fibrillation, Coronary artery disease, Type 2 diabetes mellitus, Machine learning, Prognostic modeling
- Medical subject headings
- Atrial Fibrillation, Coronary Artery Disease, Diabetes Mellitus, Type 2
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No InterventionOther
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).
| Study group | Role | What they receive |
|---|---|---|
| eGFR-population | 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. Receives: Other: No Intervention | |
| Atrial fibrillation (AF) sub-population | To be included in the AF sub-population patients need to satisfy the inclusion criteria for the eGFR-population; have two inpatient or outpatient diagnoses for AF or atrial flutter on two different days within the study period irrespective of time points when eGFR is measured.
Patients with at least one inpatient or outpatient diagnosis or procedure code for mitral stenosis and prosthetic valves within the study period will be excluded. Receives: Other: No Intervention | |
| Coronary artery disease (CAD) sub-population | To be included in the CAD sub-population patients need to satisfy the inclusion criteria for the eGFR-population; have at least one inpatient CAD diagnosis within the study period irrespective of time points when eGFR is measured. Receives: Other: No Intervention | |
| Type 2 diabetes mellitus (T2DM) sub-population | To be included in the T2DM sub-population patients need to satisfy the inclusion criteria for the eGFR-population; have at least two inpatient or outpatient diagnosis of T2DM on two different days within the study period irrespective of time points when eGFR is measured. Receives: Other: No Intervention |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: From eGRF values starting and lasting 180d + 370d
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Jul 2018
- Study start15 Jul 2018
- Primary completion31 Dec 2018
- Study completion31 Dec 2018
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Renal FunctionStudies of the same condition, open and under way.
- More trials run by BayerThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 5,132,200 participants (actual)
Sponsor and investigators
- Lead sponsor
- Bayer
- Sponsor type
- Industry (pharma, biotech, devices)
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03605810
- Sponsor's protocol ID
- 20325
- Status last verified
- Dec 2019
- First submitted
- 23 Jul 2018
- Last updated
- 10 Dec 2019
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 10 Dec 2019. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- An Estimated Glomerular Filtration Rate (eGFR) Level Prediction
- ClinicalTrials.gov ID
- NCT03605810
- Acronym / sponsor ID
- 20325
- Conditions
- Renal Function
- Keywords
- Renal function, eGRF, Atrial fibrillation, Coronary artery disease, Type 2 diabetes mellitus, Machine learning, Prognostic modeling
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 5,132,200 participants (actual)
- Lead sponsor
- BayerIndustry (pharma, biotech, devices)
- Study start
- 15 Jul 2018 (actual)
- Primary completion
- 31 Dec 2018 (actual)
- Study completion
- 31 Dec 2018 (actual)
- First posted
- 30 Jul 2018 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 4
| Arm | Type | Receives |
|---|---|---|
| eGFR-population | – | Other: No InterventionTo 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. |
| Atrial fibrillation (AF) sub-population | – | Other: No InterventionTo be included in the AF sub-population patients need to satisfy the inclusion criteria for the eGFR-population; have two inpatient or outpatient diagnoses for AF or atrial flutter on two different days within the study period irrespective of time points when eGFR is measured. Patients with at least one inpatient or outpatient diagnosis or procedure code for mitral stenosis and prosthetic valves within the study period will be excluded. |
| Coronary artery disease (CAD) sub-population | – | Other: No InterventionTo be included in the CAD sub-population patients need to satisfy the inclusion criteria for the eGFR-population; have at least one inpatient CAD diagnosis within the study period irrespective of time points when eGFR is measured. |
| Type 2 diabetes mellitus (T2DM) sub-population | – | Other: No InterventionTo be included in the T2DM sub-population patients need to satisfy the inclusion criteria for the eGFR-population; have at least two inpatient or outpatient diagnosis of T2DM on two different days within the study period irrespective of time points when eGFR is measured. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| No InterventionThis 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). | Other | Atrial fibrillation (AF) sub-population, Coronary artery disease (CAD) sub-population, Type 2 diabetes mellitus (T2DM) sub-population, eGFR-population |
Primary outcome
Performance of classification to predict eGFR
Time frame: From eGRF values starting and lasting 180d + 370d
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
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 10 Dec 2019.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Renal Function, run by Bayer at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 10 Dec 2019. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Performance of classification to predict eGFR (time frame: From eGRF values starting and lasting 180d + 370d). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 10 Dec 2019.