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Completed

NCT Number: NCT02140645

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

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.

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Key information

Conditions

Diabetes Mellitus, Type 2 Diabetes Mellitus Endocrine System Diseases Glucose Metabolism Disorders Metabolic Diseases Nutritional and Metabolic Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Boehringer Ingelheim Investigational Site

Boston, Massachusetts, United States

About this study

Purpose:

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Linagliptin

Drug

non-randomized

Primary outcomes

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

Sponsors and collaborators

Lead sponsor

Boehringer Ingelheim

Industry

Collaborators

  • Eli Lilly and Company

Registry information

Official study title

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

Important dates

Study start
2014
Primary completion
2015
Study completion
2015
First posted
May 16, 2014
Registry last updated
Feb 8, 2017

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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