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

NCT02140645 | Completed Results

• 1 other identifier: 1218.162
• Study Type: observational
• Target: 166,613
• Locations: 1 country
• Sites: 1

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.

Conditions

Diabetes Mellitus, Type 2

Outcome Measures

Primary Outcomes (14)

• 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

Study Arms (6)

Linagliptin1

T2DM patients initiating Linagliptin (DPP-4 comparison)

Drug: linagliptin

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)

Interventions

linagliptin DRUG

non-randomized

Linagliptin1

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

T2DM patients aged 18 or older, initiating antidiabetic treatment after at least 6 months of continuous enrollment

You may qualify if:

• 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)

You may not qualify if:

• 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

Sponsors & Collaborators

• Boehringer Ingelheim: lead
• Eli Lilly and Company: collaborator

Study Sites (1)

Boehringer Ingelheim Investigational Site

Boston, Massachusetts, United States

Location

MeSH Terms

Conditions

Diabetes Mellitus, Type 2

Interventions

Linagliptin

Condition Hierarchy (Ancestors)

Diabetes Mellitus; Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Endocrine System Diseases

Intervention Hierarchy (Ancestors)

Purines; Heterocyclic Compounds, 2-Ring; Heterocyclic Compounds, Fused-Ring; Heterocyclic Compounds; Quinazolines

Results Point of Contact

• Title: Boehringer Ingelheim Call Center
• Organization: Boehringer Ingelheim (BI)

clintriage.rdg@boehringer-ingelheim.com

1800-243-0127

Study Officials

• Boehringer Ingelheim

  Boehringer Ingelheim

  STUDY CHAIR

Publication Agreements

• PI is Sponsor Employee: No
• Restriction Type: OTHER
• Restrictive Agreement: Yes

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: May 14, 2014
• First Posted: May 16, 2014
• Study Start: May 1, 2014
• Primary Completion: March 1, 2015
• Study Completion: March 1, 2015
• Last Updated: February 8, 2017
• Results First Posted: February 8, 2017

Record last verified: 2016-12

Locations

US (1)