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

Study Overview
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Brief Summary
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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
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Purpose:

Official Title
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Association of Clinical Covariates With Non-insulin Diabetes Medication Initiation Using Electronic Medical Records (EMR)

Conditions
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Diabetes Mellitus, Type 2

Intervention / Treatment
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* Drug: linagliptin


Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Linagliptin1<br>T2DM patients initiating Linagliptin (DPP-4 comparison) | Drug: linagliptin<br>* non-randomized<br>|
| Other DPP4<br>T2DM patients initiating a non-linagliptin DPP-4 inhibitor | |
| Linagliptin2<br>T2DM patients initiating Linagliptin (glitizaone comparison) | |
| Glitazones<br>T2DM patients initiating Thiazolidinediones (glitazones) | |
| Sulfonylurea<br>T2DM patients initiating any medication in the Sulfonylurea class | |
| Linagliptin3<br>T2DM patients initiating Linagliptin (Sulfonylurea comparison) | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Missing EMR (Electronic Medical Record) Characteristic: Smoking | 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. | Up to 20 months | 
| Missing EMR Characteristic: Duration of Diabetes | 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. | Up to 20 months | 
| Missing EMR Characteristic: Duration of Diabetes (Continuous) | 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. | Up to 20 months | 
| Missing EMR Characteristic: BMI (Body Mass Index) | 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. | Up to 20 months | 
| Missing EMR Characteristic: BMI (Continuous) | 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. | Up to 20 months | 
| Missing EMR Characteristic: HbA1c (Hemoglobin A1c (Glycosylated Hemoglobin)) | 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. | Up to 20 months | 
| Missing EMR Characteristic: eGFR (Glomerular Filtration Rate) | 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. | Upto 20 months | 
| Missing EMR Characteristic: Total Cholesterol | 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. | Up to 20 months | 
| Missing EMR Characteristic: Systolic BP (Blood Pressure) | 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. | Up to 20 months | 
| Missing EMR Characteristic: Diastolic BP | 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. | Up to 20 months | 
| Binary EMR Characteristic: Neuropathy | 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. | Up to 20 months | 
| Binary EMR Characteristic: Nephropathy | 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. | Upto 20 months | 
| Binary EMR Characteristic: Retinopathy | 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. | Up to 20 months | 
| Binary EMR Characteristic: Pancreatitis | 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. | Up to 20 months |