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

Completed

• Conditions: Diabetes Mellitus, Type 2
• Interventions: Drug: linagliptin

• Registration Number: NCT02140645
• Lead Sponsor: Boehringer Ingelheim

Brief Summary

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.

Detailed Description

Purpose:

Study Timeline

• Study Start: 2014-05
• Study First Submitted: 2014-05-14
• Study First Submitted QC: 2014-05-14
• Study First Posted: 2014-05-16
• Primary Completion: 2015-03
• Study Completion: 2015-03
• Results First Submitted: 2016-03-29
• Status Verified: 2016-12
• Results First Submitted QC: 2016-12-15
• Last Update Submitted: 2016-12-15
• Results First Posted: 2017-02-08
• Last Update Posted: 2017-02-08

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 166613

Inclusion Criteria

Not provided

Exclusion Criteria

Not provided

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Linagliptin1	linagliptin	T2DM patients initiating Linagliptin (DPP-4 comparison)

Primary Outcome Measures

Name	Time	Method
Missing EMR Characteristic: BMI (Body Mass Index)	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 (Electronic Medical Record) Characteristic: Smoking	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: eGFR (Glomerular Filtration Rate)	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: Duration of Diabetes	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)	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 (Continuous)	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))	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: Total Cholesterol	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.
Binary EMR Characteristic: Nephropathy	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.
Missing EMR Characteristic: Systolic BP (Blood Pressure)	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.
Binary EMR Characteristic: Neuropathy	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: Retinopathy	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.
Missing EMR Characteristic: Diastolic BP	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: Pancreatitis	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.

Trial Locations

Locations (1)

Boehringer Ingelheim Investigational Site; 🇺🇸 Boston, Massachusetts, United States