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Ascertainment of EMR-based Clinical Covariates Among Patients Receiving Oral and Non-insulin Injected Hypoglycemic Therapy

This study has been completed.
Sponsor:
Collaborator:
Eli Lilly and Company
Information provided by (Responsible Party):
Boehringer Ingelheim
ClinicalTrials.gov Identifier:
NCT02140645
First received: May 14, 2014
Last updated: December 15, 2016
Last verified: December 2016
  Purpose
The objective of this study is to identify EMR-based clinical covariates and quantify their association with the prescribing of each specific type 2 diabetes (T2DM) medication under investigation. This will include an assessment of how well these covariates are captured through claims data proxies, and their potential to confound comparative research of T2DM medications.

Condition Intervention
Diabetes Mellitus, Type 2
Drug: linagliptin

Study Type: Observational
Study Design: Observational Model: Cohort
Time Perspective: Retrospective
Official Title: Association of Clinical Covariates With Non-insulin Diabetes Medication Initiation Using Electronic Medical Records (EMR)

Resource links provided by NLM:


Further study details as provided by Boehringer Ingelheim:

Primary Outcome Measures:
  • Missing EMR (Electronic Medical Record) Characteristic: Smoking [ Time Frame: Up to 20 months ]

    The missing EMR characteristic smoking defined as current, unknown, versus past/never smoker.

    The associations between claims-based covariates and missingness on EMR characteristics were investigated by estimating a logistic regression model (and multinomial logistic regression, depending on the number of categories for the EMR characteristic) for each EMR characteristic where an indicator for missing the EMR characteristic smoking was the dependent variable and all claims-based covariates were included as independent variables.

    The estimated value represented is actually prediction accuracy defined by C-statistics.


  • Missing EMR Characteristic: Duration of Diabetes [ Time Frame: Up to 20 months ]

    The missing EMR characteristic duration of diabetes defined as >7, 5-6, 3-5, 1-3, <1 (in years) in duration.

    The associations between claims-based covariates and missingness on EMR characteristics were investigated by estimating a logistic regression model (and multinomial logistic regression, depending on the number of categories for the EMR characteristic) for each EMR characteristic where an indicator for missing the EMR characteristic duration of diabetes was the dependent variable and all claims-based covariates were included as independent variables.

    The estimated value represented is actually prediction accuracy defined by C-statistics.


  • Missing EMR Characteristic: Duration of Diabetes (Continuous) [ Time Frame: Up to 20 months ]

    The missing EMR characteristic duration of diabetes defined as starting year/starting age of diabetes.

    Linear regression models were ran using a prioritized list of claims-based covariates as predictors and the value of select EMR-based clinical characteristics duration of diabetes as continuous outcomes.

    The estimated value represented is actually prediction accuracy defined by R-squared.


  • Missing EMR Characteristic: BMI (Body Mass Index) [ Time Frame: Up to 20 months ]

    The missing EMR characteristic BMI defined as not obese, overweight, obese, severe obesity.

    The associations between claims-based covariates and missingness on EMR characteristics were investigated by estimating a logistic regression model (and multinomial logistic regression, depending on the number of categories for the EMR characteristic) for each EMR characteristic where an indicator for missing the EMR characteristic BMI was the dependent variable and all claims-based covariates were included as independent variables.

    The estimated value represented is actually prediction accuracy defined by C-statistics.


  • Missing EMR Characteristic: BMI (Continuous) [ Time Frame: Up to 20 months ]

    The missing EMR characteristic BMI is BMI value. Linear regression models were ran using a prioritized list of claims-based covariates as predictors and the value of select EMR-based clinical characteristics BMI as continuous outcomes.

    The estimated value represented is actually prediction accuracy defined by R-squared.


  • Missing EMR Characteristic: HbA1c (Hemoglobin A1c (Glycosylated Hemoglobin)) [ Time Frame: Up to 20 months ]

    The missing EMR characteristic HbA1c defined as value in 6 months prior to and including index date.

    The associations between claims-based covariates and missingness on EMR characteristics were investigated by estimating a logistic regression model (and multinomial logistic regression, depending on the number of categories for the EMR characteristic) for each EMR characteristic where an indicator for missing the EMR characteristic HbA1c was the dependent variable and all claims-based covariates were included as independent variables.

    The estimated value represented is actually prediction accuracy defined by C-statistics.


  • Missing EMR Characteristic: eGFR (Glomerular Filtration Rate) [ Time Frame: Upto 20 months ]

    The missing EMR characteristic eGFR defined as value in 6 months prior to and including index date.

    The associations between claims-based covariates and missingness on EMR characteristics were investigated by estimating a logistic regression model (and multinomial logistic regression, depending on the number of categories for the EMR characteristic) for each EMR characteristic where an indicator for missing the EMR characteristic eGFR was the dependent variable and all claims-based covariates were included as independent variables.

    The estimated value represented is actually prediction accuracy defined by C-statistics.


  • Missing EMR Characteristic: Total Cholesterol [ Time Frame: Up to 20 months ]

    The missing EMR characteristic total cholesterol defined as value in 6 months prior to and including index date.

    The associations between claims-based covariates and missingness on EMR characteristics were investigated by estimating a logistic regression model (and multinomial logistic regression, depending on the number of categories for the EMR characteristic) for each EMR characteristic where an indicator for missing the EMR characteristic total cholesterol was the dependent variable and all claims-based covariates were included as independent variables.

    The estimated value represented is actually prediction accuracy defined by C-statistics.


  • Missing EMR Characteristic: Systolic BP (Blood Pressure) [ Time Frame: Up to 20 months ]

    The missing EMR characteristic systolic BP defined as value in 6 months prior to and including index date.

    The associations between claims-based covariates and missingness on EMR characteristics were investigated by estimating a logistic regression model (and multinomial logistic regression, depending on the number of categories for the EMR characteristic) for each EMR characteristic where an indicator for missing the EMR characteristic systolic BP was the dependent variable and all claims-based covariates were included as independent variables.

    The estimated value represented is actually prediction accuracy defined by C-statistics.


  • Missing EMR Characteristic: Diastolic BP [ Time Frame: Up to 20 months ]

    The missing EMR characteristic diastolic BP defined as value in 6 months prior to and including index date.

    The associations between claims-based covariates and missingness on EMR characteristics were investigated by estimating a logistic regression model (and multinomial logistic regression, depending on the number of categories for the EMR characteristic) for each EMR characteristic where an indicator for missing the EMR characteristic diastolic BP was the dependent variable and all claims-based covariates were included as independent variables.

    The estimated value represented is actually prediction accuracy defined by C-statistics.


  • Binary EMR Characteristic: Neuropathy [ Time Frame: Up to 20 months ]

    The missing EMR characteristic neuropathy defined as participants with any note of diabetic neuropathy.

    The associations between claims-based covariates and missingness on EMR characteristics were investigated by estimating a logistic regression model (and multinomial logistic regression, depending on the number of categories for the EMR characteristic) for each EMR characteristic where an indicator for missing the EMR characteristic neuropathy was the dependent variable and all claims-based covariates were included as independent variables.

    The estimated value represented is actually prediction accuracy defined by C-statistics.


  • Binary EMR Characteristic: Nephropathy [ Time Frame: Upto 20 months ]

    The missing EMR characteristic nephropathy defined as participants with any note of diabetic nephropathy.

    The associations between claims-based covariates and missingness on EMR characteristics were investigated by estimating a logistic regression model (and multinomial logistic regression, depending on the number of categories for the EMR characteristic) for each EMR characteristic where an indicator for missing the EMR characteristic nephropathy was the dependent variable and all claims-based covariates were included as independent variables.

    The estimated value represented is actually prediction accuracy defined by C-statistics.


  • Binary EMR Characteristic: Retinopathy [ Time Frame: Up to 20 months ]

    The missing EMR characteristic retinopathy defined as participants with any note of diabetic retinopathy.

    The associations between claims-based covariates and missingness on EMR characteristics were investigated by estimating a logistic regression model (and multinomial logistic regression, depending on the number of categories for the EMR characteristic) for each EMR characteristic where an indicator for missing the EMR characteristic retinopathy was the dependent variable and all claims-based covariates were included as independent variables.

    The estimated value represented is actually prediction accuracy defined by C-statistics.


  • Binary EMR Characteristic: Pancreatitis [ Time Frame: Up to 20 months ]

    The missing EMR characteristic pancreatitis defined as participants with any note of prior pancreatitis.

    The associations between claims-based covariates and missingness on EMR characteristics were investigated by estimating a logistic regression model (and multinomial logistic regression, depending on the number of categories for the EMR characteristic) for each EMR characteristic where an indicator for missing the EMR characteristic pancreatitis was the dependent variable and all claims-based covariates were included as independent variables.

    The estimated value represented is actually prediction accuracy defined by C-statistics.



Enrollment: 166613
Study Start Date: May 2014
Study Completion Date: March 2015
Primary Completion Date: March 2015 (Final data collection date for primary outcome measure)
Groups/Cohorts Assigned Interventions
Linagliptin1
T2DM patients initiating Linagliptin (DPP-4 comparison)
Drug: linagliptin
non-randomized
Other DPP4
T2DM patients initiating a non-linagliptin DPP-4 inhibitor
Linagliptin2
T2DM patients initiating Linagliptin (glitizaone comparison)
Glitazones
T2DM patients initiating Thiazolidinediones (glitazones)
Sulfonylurea
T2DM patients initiating any medication in the Sulfonylurea class
Linagliptin3
T2DM patients initiating Linagliptin (Sulfonylurea comparison)

Detailed Description:
Purpose:
  Eligibility

Ages Eligible for Study:   18 Years and older   (Adult, Senior)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
T2DM patients aged 18 or older, initiating antidiabetic treatment after at least 6 months of continuous enrollment
Criteria

Inclusion criteria:

  • Dispensing of an oral or non-insulin injected hypoglycemic medication between May 2011 and June 2012
  • Diagnosis of type 2 diabetes mellitus
  • Presence of electronic medical records (for the EMR-based subset)

Exclusion criteria:

  • Age <18 at T2DM medication initiation
  • Missing or ambiguous age or sex information
  • At least one diagnosis of type 1 diabetes mellitus
  • Less than 6 months enrolment in the database preceding the date of the first dispensing
  • Prior use of the index drug
  Contacts and Locations
Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the Contacts provided below. For general information, see Learn About Clinical Studies.

Please refer to this study by its ClinicalTrials.gov identifier: NCT02140645

Locations
United States, Massachusetts
Boehringer Ingelheim Investigational Site
Boston, Massachusetts, United States
Sponsors and Collaborators
Boehringer Ingelheim
Eli Lilly and Company
Investigators
Study Chair: Boehringer Ingelheim Boehringer Ingelheim
  More Information

Additional Information:
Responsible Party: Boehringer Ingelheim
ClinicalTrials.gov Identifier: NCT02140645     History of Changes
Other Study ID Numbers: 1218.162
Study First Received: May 14, 2014
Results First Received: March 29, 2016
Last Updated: December 15, 2016

Additional relevant MeSH terms:
Diabetes Mellitus
Diabetes Mellitus, Type 2
Glucose Metabolism Disorders
Metabolic Diseases
Endocrine System Diseases
Linagliptin
Hypoglycemic Agents
Physiological Effects of Drugs
Incretins
Hormones
Hormones, Hormone Substitutes, and Hormone Antagonists
Dipeptidyl-Peptidase IV Inhibitors
Protease Inhibitors
Enzyme Inhibitors
Molecular Mechanisms of Pharmacological Action

ClinicalTrials.gov processed this record on March 22, 2017