Diabetes Mellitus, Type 2NCT02140645

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

An observational study of Diabetes Mellitus, Type 2, run by Boehringer Ingelheim at 1 site in United States.

Completed

Finished. The study has ended and is not enrolling. Results may be posted on the official record.

Official record

Search all trials

Trial summary

Who is it for?: Ages 18 years and older
Conditions the study is for: Diabetes Mellitus, Type 2
What is being tested?: linagliptin
How many people: 166,613 participants
Where: 1 siteUnited States
Study dates: May 2014to Mar 2015
Phase: Observational
Are there results?: Yes, posted in the registrySummary in the doctors' view

Key requirements

Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.

Ages 18 years and older
All sexes
No healthy volunteers
Adult, Older adult

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

Who may be able to join

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)

Who cannot join

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

Trial locations and contacts

1 site in 1 country.

United States1 site

Boehringer Ingelheim Investigational Site
Boston, Massachusetts

Other trials in United States

About this study

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

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.

Read the detailed description

Purpose:

Conditions: Diabetes Mellitus, Type 2
Being tested: linagliptin
Medical subject headings: Diabetes Mellitus, Type 2

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

linagliptinDrug
non-randomized

Study groups in NCT02140645
Study group	Role	What they receive
Linagliptin1		T2DM patients initiating Linagliptin (DPP-4 comparison) Receives: 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)

What is being measured

The primary outcome is the main question the study is designed to answer.

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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: Up to 20 months

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

May 2014Mar 2015

First posted16 May 2014 (estimated)
Study startMay 2014
Primary completionMar 2015
Study completionMar 2015

Resources

Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
Posted resultsParticipant flow, outcome measures and adverse events reported by the sponsor.
Other trials for Diabetes Mellitus, Type 2Studies of the same condition, open and under way.
More trials run by Boehringer IngelheimThe sponsor's other registered studies.
What the trial phases meanHow a treatment moves from first tests in people to approval.

More details

For doctors and researchers: how the study is designed, who runs it, and its registry IDs.

Study design

Study type: Observational
Observational model: Cohort
Time perspective: Retrospective
Enrolment: 166,613 participants (actual)

IDs, dates and publications

ClinicalTrials.gov ID: NCT02140645
Sponsor's protocol ID: 1218.162
Status last verified: Dec 2016
First submitted: 14 May 2014
Last updated: 8 Feb 2017
Results posted: Yes, on ClinicalTrials.gov

Want to know more?

The sponsor has not listed a contact. The official record may have more details.

Talk to your doctor first and take this page with you. Only the study team can confirm whether you can join.

Questions to ask the study team

What is the study trying to find out?
What would I need to do, and how often would I visit?
Could I receive a placebo instead of the treatment?
What are the possible risks and benefits for me?
Will it cost me anything? Are travel or time paid for?
Can I keep seeing my own doctor during the study?
What happens when the study ends?

See locations

Not yet recruitingPhase 3
Efficacy and Safety of Once-Weekly SHR-3167 Compared With Once-Daily Insulin Glargine in Adults With Insulin-Naive Type 2 Diabetes
Type 2 Diabetes
Active, not recruiting
Digital Reinforcement of Motivational Interviewing Micro-Skills Among Primary Care Healthcare Professionals
Hypertension (Without Type 2 Diabetes Mellitus)
Recruiting
Investigating Vascular Properties of HEMI and SPG Signals in Individuals With or at Risk for Chronic Kidney Disease
Chronic Kidney Disease, Hypertension (HTN)
Not yet recruitingPhase 3
A Study to Evaluate the Effectiveness and Safety of the Drug AZD6234 for Managing Weight in Adults Who Are Obese or Overweight. These Participants May or May Not Also Have Type 2 Diabetes
Obesity, Overweight
Not yet recruiting
Impact of Endoscopic Sleeve Gastroplasty (ESG) in Obese Adults With Type 2 Diabetes (T2D)
Type 2 Diabetes Mellitus (T2D), Obesity (BMI>30)

All Diabetes Mellitus, Type 2 trials

Study at a glance

From the registry record, last updated by the sponsor on 8 Feb 2017. Recruitment status is as the sponsor reported it: confirm with the study team.

Official title: Association of Clinical Covariates With Non-insulin Diabetes Medication Initiation Using Electronic Medical Records (EMR)
ClinicalTrials.gov ID: NCT02140645
Acronym / sponsor ID: 1218.162
Conditions: Diabetes Mellitus, Type 2
Study type: Observational
Recruitment status: Completed
Enrolment: 166,613 participants (actual)
Lead sponsor: Boehringer IngelheimIndustry (pharma, biotech, devices)
Collaborators: Eli Lilly and Company
Study start: May 2014
Primary completion: Mar 2015 (actual)
Study completion: Mar 2015 (actual)
First posted: 16 May 2014 (estimated)
Results: Posted 8 Feb 2017

Clinical summary

The research question as the sponsor registered it. This describes what the study plans to test, not what it found.

Detailed description

Purpose:

Study design

Observational model: Cohort
Time perspective: Retrospective
Number of arms: 6

Arm	Type	Receives
Linagliptin1	–	Drug: linagliptinT2DM patients initiating Linagliptin (DPP-4 comparison)
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)

Who may qualify

Ages: 18 years and older
Sex: All
Healthy volunteers: Not accepted
Age groups: Adult, Older adult
Study population: T2DM patients aged 18 or older, initiating antidiabetic treatment after at least 6 months of continuous enrollment

Key inclusion criteria (3 listed)

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)

Key exclusion criteria (5 listed)

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

Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.

Interventions

Intervention	Type	Arms
linagliptinnon-randomized	Drug	Linagliptin1

Primary outcome

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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.

Results and safety

Results posted to the registry on 8 Feb 2017, as reported by the sponsor. Figures are copied from the record, not re-analysed. Full results on ClinicalTrials.gov.

Participant flow

Group	Started	Completed
Linagliptin 1	5,732	243
Any Other DPP-4	64,695	3,041
Linagliptin 2	4,441	205
Pioglitazone	14,363	530
Linagliptin 3	3,436	150
Second Generation Sulfonylurea	73,946	3,050

Primary outcome: Missing EMR (Electronic Medical Record) Characteristic: Smoking

Time frame: Up to 20 months. Number. Unit: Percentage of participants

Measure	Linagliptin 1 n = 243	Any Other DPP-4 n = 3041	Linagliptin 2 n = 205	Pioglitazone n = 530	Linagliptin 3 n = 150	Second Generation Sulfonylurea n = 3050
Current	9.1	7.6	10.7	7.9	11.3	9.4
Past	12.3	12.2	12.2	10.2	10.7	10.9
Never	32.9	35.5	32.7	30.8	34.7	33.3
Unknown	5.8	4.9	6.3	5.1	6.0	6.5

Linagliptin 1 vs Any Other DPP-4 vs Linagliptin 2 vs Pioglitazone vs Linagliptin 3 vs Second Generation Sulfonylurea: C-statistics: 0.624

Population analysed: All subjects in MarketScan cohort meeting inclusion/exclusion criteria. EMR-linked subset: From the study group we identified patients who have EMR data available.

Primary outcome: Missing EMR Characteristic: Duration of Diabetes

Time frame: Up to 20 months. Number. Unit: Percentage of participants

Measure	Linagliptin 1 n = 243	Any Other DPP-4 n = 3041	Linagliptin 2 n = 205	Pioglitazone n = 530	Linagliptin 3 n = 150	Second Generation Sulfonylurea n = 3050
Less than 1 year	11.9	13.7	12.7	11.1	16.0	15.4
1.00-2.99 years	11.5	14.2	11.2	10.0	12.7	13.6
3.00-4.99 years	7.8	7.8	7.8	8.3	7.3	8.4
5.00-6.99 years	4.9	5.1	3.9	5.7	4.0	4.8
7+ years	5.3	5.3	5.4	5.8	5.3	4.8

Linagliptin 1 vs Any Other DPP-4 vs Linagliptin 2 vs Pioglitazone vs Linagliptin 3 vs Second Generation Sulfonylurea: C-statistcs: 0.597

Population analysed: All subjects in MarketScan cohort meeting inclusion/exclusion criteria. EMR-linked subset: From the study group we identified patients who have EMR data available.

Primary outcome: Missing EMR Characteristic: Duration of Diabetes (Continuous)

Time frame: Up to 20 months. Mean (Standard Deviation). Unit: Months

Measure	Linagliptin 1 n = 243	Any Other DPP-4 n = 3041	Linagliptin 2 n = 205	Pioglitazone n = 530	Linagliptin 3 n = 150	Second Generation Sulfonylurea n = 3050
Value	3.5 (3.7)	3.1 (3.3)	3.4 (3.7)	3.5 (3.1)	3.0 (3.4)	2.9 (3.0)

Linagliptin 1 vs Any Other DPP-4 vs Linagliptin 2 vs Pioglitazone vs Linagliptin 3 vs Second Generation Sulfonylurea: R-squared: 0.0858

Population analysed: All subjects in MarketScan cohort meeting inclusion/exclusion criteria. EMR-linked subset: From the study group we identified patients who have EMR data available.

Primary outcome: Missing EMR Characteristic: BMI (Body Mass Index)

Time frame: Up to 20 months. Number. Unit: Percentage of participants

Measure	Linagliptin 1 n = 243	Any Other DPP-4 n = 3041	Linagliptin 2 n = 205	Pioglitazone n = 530	Linagliptin 3 n = 150	Second Generation Sulfonylurea n = 3050
Underweight	0.4	0.1	0.5	0.0	0.7	0.1
Normal	2.1	3.9	2.0	3.4	2.7	4.0
Overweight	11.5	11.8	13.7	15.5	10.7	14.0
Obese	35.4	35.1	35.1	29.6	36.0	34.2
Severe Obesity	16.5	15.5	16.1	10.9	16.0	15.4

Linagliptin 1 vs Any Other DPP-4 vs Linagliptin 2 vs Pioglitazone vs Linagliptin 3 vs Second Generation Sulfonylurea: C-statistics: 0.623

Population analysed: All subjects in MarketScan cohort meeting inclusion/exclusion criteria. EMR-linked subset: From the study group we identified patients who have EMR data available.

Adverse events

Other adverse events reported at 5% frequency or more

Group	Serious adverse events	Other adverse events	Deaths
MarketScan	0 of 0	0 of 0	Not reported

Loading the posted results from ClinicalTrials.gov…

Research links

Official record on ClinicalTrials.govLast updated by the sponsor 8 Feb 2017.
Posted resultsParticipant flow, baseline, all outcome measures and adverse events.
Related InfoLink provided by the sponsor.

Related trials for Diabetes Mellitus, Type 2

Not yet recruitingPhase 3
Efficacy and Safety of Once-Weekly SHR-3167 Compared With Once-Daily Insulin Glargine in Adults With Insulin-Naive Type 2 Diabetes
Type 2 Diabetes
Active, not recruiting
Digital Reinforcement of Motivational Interviewing Micro-Skills Among Primary Care Healthcare Professionals
Hypertension (Without Type 2 Diabetes Mellitus)
Recruiting
Investigating Vascular Properties of HEMI and SPG Signals in Individuals With or at Risk for Chronic Kidney Disease
Chronic Kidney Disease, Hypertension (HTN)
Not yet recruitingPhase 3
A Study to Evaluate the Effectiveness and Safety of the Drug AZD6234 for Managing Weight in Adults Who Are Obese or Overweight. These Participants May or May Not Also Have Type 2 Diabetes
Obesity, Overweight
Not yet recruiting
Impact of Endoscopic Sleeve Gastroplasty (ESG) in Obese Adults With Type 2 Diabetes (T2D)
Type 2 Diabetes Mellitus (T2D), Obesity (BMI>30)

Referral and contact

AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.

Boehringer Ingelheim study chair

Boehringer Ingelheim

1 site in 1 country: see every site and its status.

Questions about this trial

What is this clinical trial studying?

An observational study of Diabetes Mellitus, Type 2, run by Boehringer Ingelheim at 1 site in United States.

The full registered description is in the About this study section and the official record.

Who may be eligible to take part?

The sponsor lists 3 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.

Is this clinical trial currently recruiting?

The record says: finished, as of the sponsor's last update on 8 Feb 2017. Status can change between updates, so confirm with the study contact before making plans.

Where is the study taking place?

The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.

What are the possible benefits and risks?

They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.

Are results available for this study?

Yes. Results are posted in the registry (first posted 8 Feb 2017). A summary is in the For doctors view, and the full results are on ClinicalTrials.gov.

How can a doctor refer a patient or contact the research team?

The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.

What does the primary outcome measure?

Missing EMR (Electronic Medical Record) Characteristic: Smoking (time frame: Up to 20 months). The study lists 14 primary outcomes in all. It is what the study was designed to measure, not a finding.

Where can I read the original study information?

On the ClinicalTrials.gov record for NCT02140645.

Does a listing mean the treatment is approved or proven to work?

No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.

How often is this information updated?

AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 8 Feb 2017; AzTrials last refreshed its copy on 8 Nov 2026.