Ascertainment of EMRbased Clinical Covariates Among Patients Receiving Oral and Noninsulin Injected Hypoglycemic Therapy
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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 Noninsulin Diabetes Medication Initiation Using Electronic Medical Records (EMR) 
 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 claimsbased 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 claimsbased covariates were included as independent variables.
The estimated value represented is actually prediction accuracy defined by Cstatistics.
 Missing EMR Characteristic: Duration of Diabetes [ Time Frame: Up to 20 months ]
The missing EMR characteristic duration of diabetes defined as >7, 56, 35, 13, <1 (in years) in duration.
The associations between claimsbased 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 claimsbased covariates were included as independent variables.
The estimated value represented is actually prediction accuracy defined by Cstatistics.
 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 claimsbased covariates as predictors and the value of select EMRbased clinical characteristics duration of diabetes as continuous outcomes.
The estimated value represented is actually prediction accuracy defined by Rsquared.
 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 claimsbased 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 claimsbased covariates were included as independent variables.
The estimated value represented is actually prediction accuracy defined by Cstatistics.
 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 claimsbased covariates as predictors and the value of select EMRbased clinical characteristics BMI as continuous outcomes.
The estimated value represented is actually prediction accuracy defined by Rsquared.
 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 claimsbased 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 claimsbased covariates were included as independent variables.
The estimated value represented is actually prediction accuracy defined by Cstatistics.
 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 claimsbased 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 claimsbased covariates were included as independent variables.
The estimated value represented is actually prediction accuracy defined by Cstatistics.
 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 claimsbased 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 claimsbased covariates were included as independent variables.
The estimated value represented is actually prediction accuracy defined by Cstatistics.
 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 claimsbased 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 claimsbased covariates were included as independent variables.
The estimated value represented is actually prediction accuracy defined by Cstatistics.
 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 claimsbased 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 claimsbased covariates were included as independent variables.
The estimated value represented is actually prediction accuracy defined by Cstatistics.
 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 claimsbased 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 claimsbased covariates were included as independent variables.
The estimated value represented is actually prediction accuracy defined by Cstatistics.
 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 claimsbased 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 claimsbased covariates were included as independent variables.
The estimated value represented is actually prediction accuracy defined by Cstatistics.
 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 claimsbased 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 claimsbased covariates were included as independent variables.
The estimated value represented is actually prediction accuracy defined by Cstatistics.
 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 claimsbased 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 claimsbased covariates were included as independent variables.
The estimated value represented is actually prediction accuracy defined by Cstatistics.
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 (DPP4 comparison)

Drug: linagliptin
nonrandomized

Other DPP4
T2DM patients initiating a nonlinagliptin DPP4 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:
Ages Eligible for Study:  18 Years and older (Adult, Senior) 
Sexes Eligible for Study:  All 
Accepts Healthy Volunteers:  No 
Sampling Method:  NonProbability Sample 
Inclusion criteria:
 Dispensing of an oral or noninsulin injected hypoglycemic medication between May 2011 and June 2012
 Diagnosis of type 2 diabetes mellitus
 Presence of electronic medical records (for the EMRbased 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
Please refer to this study by its ClinicalTrials.gov identifier: NCT02140645
United States, Massachusetts  
Boehringer Ingelheim Investigational Site  
Boston, Massachusetts, United States 
Study Chair:  Boehringer Ingelheim  Boehringer Ingelheim 
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 DipeptidylPeptidase IV Inhibitors Protease Inhibitors Enzyme Inhibitors Molecular Mechanisms of Pharmacological Action 
ClinicalTrials.gov processed this record on March 22, 2017