Validation of Predictors for Oral Anticoagulant Medication Choice Using EMR Data
Conditions: Atrial Fibrillation
Sponsor: Boehringer Ingelheim

ABCD
001-MCS-90-124_RD-01 (4.0)
TITLE PAGE
Protocol for non-interventional studies based on existing data: 
TITLE PAGE
Document Number:
c12696196-01
BI Study Number:
1160.219
BI Investigational
Product(s):
PRADAXA (dabigatran)
Title:
Validation of predictors for oral anticoagulant medication choice 
using EMR data
(Phase 3b of the BI/BWH Pradaxa study program)
Protocol version 
identifier:
1.0
Date of last version of 
protocol:
02 November 2016
PASS:
No
EU PAS register number:
Not available yet
Active substance:
Dabigatran etexilate
Medicinal product:
Pradaxa
Product reference:
EU/1/08/442/001-019
Procedure number:
EMEA/H/C/000829
Joint PASS:
Not applicable
Research question and 
objectives:
The research question is whether select patient characteristics that 
may be incompletely captured by insurance claims data, differ 
between dabigatran and warfarin initiators.
Objectives:
1) Descriptive: To identify select clinical covariates from 
electronic medical records that might be associated with 
initiation of oral anticoagulant medications (dabigatran or 
warfarin) in NVAF patients at risk for stroke and are not 
usually well captured in the associated claims database
2) Prediction Rule: To assess how well EMR-based clinical 
characteristics can be captured on the basis of claims data only
3) Assess Balance: To assess the potential for unmeasured 
confounding in dabigatran vs warfarin comparative 
effectiveness and safety studies based on administrative claims 

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databases
Country(-ies) of study:
US
Author:
Marketing authorisation 
holder(s):
Boehringer Ingelheim
MAH contact person:
Date:
02 November 2016
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1.
TABLE OF CONTENTS
TITLE PAGE.................................................................................................................. 1
1.
TABLE OF CONTENTS....................................................................................... 3
2.
LIST OF ABBREVIATIONS................................................................................ 5
3.
RESPONSIBLE PARTIES.................................................................................... 7
4.
ABSTRACT........................................................................................................... 8
5.
AMENDMENTS AND UPDATES..................................................................... 12
6.
MILESTONES..................................................................................................... 13
7.
RATIONALE AND BACKGROUND................................................................ 14
8.
RESEARCH QUESTION AND OBJECTIVES ................................................. 15
9.
RESEARCH METHODS .................................................................................... 16
9.1
STUDY DESIGN............................................................................................ 16
9.2
SETTING........................................................................................................ 16
9.3
VARIABLES .................................................................................................. 16
9.3.1
Exposures ................................................................................................ 16
9.3.2
Outcomes................................................................................................. 16
9.3.2.1
Primary outcomes..................................................................................17
9.3.2.2
Secondary outcomes..............................................................................17
9.3.2.3
Further outcomes...................................................................................17
9.3.3
Covariates................................................................................................ 20
9.4
DATA SOURCES........................................................................................... 32
9.5
STUDY SIZE.................................................................................................. 33
9.6
DATA MANAGEMENT................................................................................ 34
9.7
DATA ANALYSIS......................................................................................... 34
9.7.1
Main analysis........................................................................................... 34
9.7.2
Further analysis ....................................................................................... 38
9.8
QUALITY CONTROL................................................................................... 38
9.9
LIMITATIONS OF THE RESEARCH METHODS...................................... 38
9.10
OTHER ASPECTS ......................................................................................... 39
9.11
SUBJECTS...................................................................................................... 39
9.11.1
Study group ............................................................................................. 39
9.11.2
EMR-linked subset.................................................................................. 40
9.12
BIAS................................................................................................................ 42
10.
PROTECTION OF HUMAN SUBJECTS .......................................................... 43

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11.
MANAGEMENT AND REPORTING OF ADVERSE EVENTS/ADVERSE 
REACTIONS....................................................................................................... 44
12.
PLANS FOR DISSEMINATING AND COMMUNICATING STUDY 
RESULTS............................................................................................................ 45
13.
REFERENCES .................................................................................................... 46
13.1
PUBLISHED REFERENCES......................................................................... 46
13.2
UNPUBLISHED REFERENCES................................................................... 47
ANNEX 1. LIST OF STAND-ALONE DOCUMENTS.............................................. 48
ANNEX 2. ENCEPP CECKLIST FOR STUDY PROTOCOLS ................................. 49
ANNEX 3. ADDITIONAL INFORMATION.............................................................. 55

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2.
LIST OF ABBREVIATIONS
ACCP
American College of Chest Physicians
ACEI
Angiotensin-converting Enzyme Inhibitor
AES
Advanced Encryption Standard
AF
Atrial Fibrillation
ALT
Serum Alanine Aminotransferase
ARB
Angiotensin Receptor Blocker
ASCVD
Atherosclerotic Cardiovascular Disease
BB
Beta Blocker
BI
Boehringer Ingelheim
BMI
Body Mass Index
BWH
Brigham and Women’s Hospital
CABG
Coronary Artery Bypass Graft
CAD
Coronary Artery Disease
CCB
Calcium Channel Blocker
CHA2DS2-VASc
Congestive Heart Failure, Hypertension, Age > 75, Diabetes 
Mellitus, Prior Stroke or Transient Ischemic Attack, Vascular 
Disease, Age 65-74, Sex Category
CHADS2
Congestive Heart Failure, Hypertension, Age> 75, Diabetes Mellitus, 
Prior Stroke or Transient Ischemic Attack
CHF
Congestive Heart Failure
CI
Confidence Interval
CONSORT
Consolidated Standards for Reporting Trials
CPT
Current Procedural Terminology
Cr
Creatinine
DM
Diabetes Mellitus
DVT
Deep Venous Thromboembolism
Dx
Diagnosis
EMR
Electronic Medical Records
ESC
European Society of Cardiology
GFR
Glomerular Filtration Rate
GI
Gastrointestinal
H2 Receptor
Histamine H2 Receptor
HAS-BLED
Hypertension, Abnormal Liver/Renal function, Stroke, Bleeding 
history or predisposition, Labile INR, Elderly (Age >65), Drugs-
Alcohol usage
HbA1c
Hemoglobin A1C
HCPC
Healthcare Common Procedure Coding
hdPS
High-dimensional Propensity Score
HR
Hazard Ratio
ICD-9
International Classification of Diseases, Ninth Revision, Clinical 
Modification
ICH
Intracerebral Hemorrhage
INR
International Normalized Ratio
ITT
Intent-To-Treat
IRB
Institutional Review Board
ISF
Investigator Site File

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LDL
Low Density Lipoprotein
LOS
Length of Stay
MDRD
Modification of Diet in Renal Disease
MI
Myocardial Infarction
NOAC
New Oral Anticoagulant
NSAID
Non-steroidal Anti-inflammatory Drug
NVAF
Non-valvular Atrial Fibrillation
PAD
Peripheral Artery Disease
PCI
Percutaneous Coronary Intervention
PE
Pulmonary Embolism
PGP
P-glycoprotein
PPI
Proton Pump Inhibitor
PPV
Positive Predictive Value
PS
Propensity Score
PTCA
Percutaneous Transluminal Coronary Angioplasty
PVD
Peripheral Vascular Disease
PY
Person-Year
RE-LY
Randomized Evaluation of Long-Term Anticoagulation Therapy
RR
Relative Risk
SAH
Subarachnoid Hemorrhage
SBP
Systolic Blood Pressure
SCr
Serum Creatinine
SD
Standard deviation
TIA
Transient Ischemic Attack
t.i.d.
ter in die (3 times a day)
US
United States
VTE
Venous Thromboembolism

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4.
ABSTRACT
Name of company:
Boehringer Ingelheim
Name of finished medicinal product:
Pradaxa® (Dabigatran etexilate), or
warfarin
Name of active ingredient:
Dabigatran etexilate or warfarin
Protocol date:
Study number:
Version/Revision:
Version/Revision 
date:
02 November 2016
1160.219
1.0
Title of study:
Association of select EMR-based covariates with oral anticoagulant 
medication selection
Version and date: Version 1.0, 02 November 2016
 
 
Rationale and 
background:
A study of safety/effectiveness of dabigatran is being conducted in health 
insurer claims databases that may incompletely capture select variables 
representing covariates. This validation study seeks to ascertain a select set 
of covariates in electronic medical records (EMRs) linked to a subset of 
patients within the insurance claims data in order to assess the potential for 
unmeasured confounding in observational studies based on insurance 
claims databases only.
Research question 
and objectives:
The research question is whether select patient characteristics that may be 
incompletely captured by insurance claims data differ between dabigatran 
and warfarin initiators.
Objectives:
1)
Descriptive: To identify select clinical covariates from electronic 
medical records that might be associated with initiation of oral 
anticoagulant medications (dabigatran or warfarin) in patients with NVAF 
at risk for stroke and are not usually well captured in the associated claims 
database.
2)
Prediction Rule: To quantify the association between EMR-based 
clinical characteristics and patterns of insurance claims

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Name of company:
Boehringer Ingelheim
Name of finished medicinal product:
Pradaxa® (Dabigatran etexilate), or 
warfarin
Name of active ingredient:
Dabigatran etexilate or warfarin
Protocol date:
Study number:
Version/Revision:
Version/Revision 
date:
02 November 2016
1160.219
1.0
Research question 
and objectives:
3)
Assess Balance: To assess the potential for unmeasured 
confounding in dabigatran vs warfarin comparative effectiveness and 
safety studies based on administrative claims databases
Study design:
A validation study
Population:
Patients from an US health insurance database (electronic medical records)
with a recorded diagnosis of atrial fibrillation without evidence of valvular 
etiology and at risk for stroke who initiate dabigatran or warfarin between 
October 2010 and December 2014 (This date range corresponds to the 
available matched cohort date range).
Inclusion criteria:
•
A recorded diagnosis of atrial fibrillation.
•
Initiation of anticoagulant medication (dabigatran or warfarin).
•
At least 18 years of age on the date of anticoagulant initiation.
•
CHA2DS2-VASc score ≥ 1
•
Presence of electronic medical records (for the EMR-based subset)
Exclusion criteria:
•
Patients with missing or ambiguous age or sex information.
•
Patients with evidence of valvular disease.
•
Patients with less than 12 months enrolment preceding the date of 
anticoagulant initiation.
•
Patients with  a dispensing of any oral anticoagulant during the 12 
months preceding the date of anticoagulant initiation
•
Patients with a nursing home stay during baseline
•
Unmatched dabigatran/warfarin initiators

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Name of company:
Boehringer Ingelheim
Name of finished medicinal product:
Pradaxa® (Dabigatran etexilate), or 
warfarin
Name of active ingredient:
Dabigatran etexilate or warfarin
Protocol date:
Study number:
Version/Revision:
Version/Revision 
date:
02 November 2016
1160.219
1.0
Variables:
Patient characteristics derived from insurance claims (claims-based 
covariates)
This study will characterize patients initiating oral anticoagulant 
medications with respect to select characteristics, some of which might be 
reflected differently in claims data compared to electronic medical records 
prior to and including the date of initiation. These characteristics from 
EMR data are usually treated as covariates but will be considered 
outcomes in this validation study.
These characteristics include:
Primary Outcomes
•
Obesity 
•
Smoking 
•
Alcohol consumption
•
Abnormal renal function 
•
Bleeding history or predisposition
•
Renal function (estimated GFR)
•
Serum Creatinine
•
Abnormal liver function
Secondary Outcomes
•
Duration of atrial fibrillation 
•
History of adherence
•
History/duration of hypertension
•
Uncontrolled Hypertension (for HAS-BLED)
•
History/duration of CHF 
•
Prior TIA 
•
Diabetes
•
Hyperlipidemia

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Name of company:
Boehringer Ingelheim
Name of finished medicinal product:
Pradaxa® (Dabigatran etexilate), or 
warfarin
Name of active ingredient:
Dabigatran etexilate or warfarin
Protocol date:
Study number:
Version/Revision:
Version/Revision 
date:
02 November 2016
1160.219
1.0
Variables:
•
HAS-BLED Score
•
Use of antiplatelets or NSAIDs (needed for HAS-BLED)
Data sources:
This study will be conducted within MarketScan data linked to electronic 
medical records.
Study size:
Study cohort: approximately 15,000 dabigatran initiators PS-matched  to 
15,000 warfarin initiators 
EMR-linked subset: approx. 5.5% of the study cohort.
Data analysis:
•
Analyses will describe the presence in the claims data of each 
EMR-based clinical characteristic among initiators of dabigatran 
and initiators of warfarin.  
•
A prediction algorithm to identify in the claims data each of the 
EMR-based clinical characteristic will be developed. The 
prediction algorithm will be estimated using a regression model 
that uses each of the EMR characteristic as the model outcome and 
all available claims-based covariates as predictors.  
•
Strength of association between each EMR-based covariate and 
medication choice will guide quantitative assessments of potential 
confounding of the association between anticoagulant and clinical 
outcomes.
Milestones:
Draft Protocol: September 2013
Final Protocol: November 2016
Draft Report: July 2017
Final Report: September 2017

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5.
AMENDMENTS AND UPDATES
None.

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6.
MILESTONES
Milestone
Planned Date
Contract
June 2012
Draft protocol
September 2013
Final protocol
November 2016
Start of data collection
15 November 2016
End of data collection
15 May 2017
First draft report
July 2017
Registration in the EU PAS 
register
November 2016
Final report of study results:
September 2017

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7.
RATIONALE AND BACKGROUND
A number of new oral anticoagulants are being developed and marketed to replace vitamin K 
antagonists, one of the most important drugs in modern medicine. [P11-11875] Unlike 
vitamin K antagonists, these new drugs do not require dose titration involving intensive 
therapeutic monitoring of prothrombin time to achieve target anticoagulation within a narrow 
therapeutic range. In Phase III studies, these drugs were found to be therapeutically
advantageous or non-inferior over warfarin. In the coming years, as many as six new 
anticoagulants could be on the market and a lack of valid comparative evidence will hinder 
prescriber and payor decision-making.
Pharmacoepidemiologic studies based on large administrative claims databases are 
increasingly utilized for that purpose; however, these data are collected for reasons unrelated 
to research, and as a result, might not contain sufficient information on important 
confounders, such as body mass index, smoking, family history, or alcohol consumption, 
leading to residual confounding. Several approaches have been proposed to address the issue 
of unmeasured confounding, and internal validation studies, based on additional data 
obtained for a subset of participants in the main study population is one of them. Such studies 
offer specific advantages since they are more representative of the main study population and 
allow application of methods that do not require assumptions about the direction of 
confounding. [R13-2768]
This protocol is for a validation study conducted within a cohort of patients initiating 
dabigatran or warfarin during October 2010 – December 2014 time period and matched on 
propensity score (PS). The study will involve targeted data ascertainment from linked
patients for enhanced covariate assessment via extraction from electronic medical records
using existing electronic data including pre-defined variables, with the goal of describing the 
presence of covariates not typically present or insufficiently measured in claims data in 
dabigatran users as compared to users of warfarin and assessing the potential for confounding 
in the comparative safety research that will be undertaken in other phases of the study 
program.

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8.
RESEARCH QUESTION AND OBJECTIVES
The objectives of this study are:
1) Descriptive: To identify select clinical covariates from electronic medical records that 
might be associated with initiation of oral anticoagulant medications (dabigatran or 
warfarin) in patients with NVAF at risk for stroke and are not usually well captured in 
the associated claims database.
2) Prediction Rule: To quantify the association between EMR-based clinical 
characteristics and patterns of insurance claims
3) Assess Balance: To assess the potential for unmeasured confounding in dabigatran vs 
warfarin comparative effectiveness and safety studies based on administrative claims 
databases

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9.
RESEARCH METHODS
9.1
STUDY DESIGN
This study represents a form of internal validation study that will involve selection of a 
patient sample, extraction of medical record data, and analyses that will have implications for 
potential confounding in comparative studies of dabigatran effect.
This study will be conducted within a cohort of NVAF patients at risk for stroke initiating
dabigatran or warfarin during October 2010 – December 2014 time period identified in 
MarketScan research claims database based on Protocol 1160.207 (Sequential Expansion of 
Comparative Effectiveness of Oral Anticoagulants). 
9.2
SETTING
The data source for this project will be MarketScan, a research claims database from 
commercial employer-sponsored health plans.
 
 
 
 
 
 
 
 
 
9.3
VARIABLES
9.3.1
Exposures
The primary exposure is new initiation of warfarin or dabigatran.  Initiators are defined as no 
anticoagulation with any oral anticoagulant in the 12 months before the index prescription. 
Comparison between these two groups of initiators will be made with respect to variables 
obtained from the EMR among patients who have been matched on the propensity score 
derived from insurance claims data.
9.3.2
Outcomes
EMR-based clinical characteristics that are typically used as covariates will be treated as 
outcomes in this validation study. Unless otherwise noted, all of these outcomes will have an 
indicator for being recorded in EMR (Present, Y/N). Index date is the date of dabigatran or 

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warfarin initiation. All characteristics will be assessed prior and including the medication 
initiation date (index date).  The characteristics presented in Table 1 reflect likely availability 
in the EMR.
9.3.2.1
Primary outcomes
•
Obesity 
•
Smoking 
•
Alcohol consumption
•
Abnormal renal function
•
Bleeding history or predisposition
•
Renal function (estimated GFR)
•
Serum Creatinine
•
Abnormal liver function
9.3.2.2
Secondary outcomes
•
Duration of atrial fibrillation 
•
History of adherence
•
History/duration of hypertension
•
Uncontrolled Hypertension (for HAS-BLED)
•
History/duration of CHF 
•
Prior TIA 
•
Diabetes
•
Hyperlipidemia
•
HAS-BLED Score
•
Use of antiplatelets or NSAIDs (needed for HAS-BLED)

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Outcome definitions
Table 1
Outcome
Definition
Present
Categories if present
Primary
Obesity 
Calculated BMI >30 or 
recorded “obesity”
Y/N
Obese, not-obese based on 
note of obesity or recorded 
BMI>30.
Smoking
Any note on smoking 
Y/N
Current & past combined and 
nonsmoker & not noted 
combined.
Alcohol consumption
Any note on alcohol use
Y/N
Alcohol use (any);
No use
Abnormal renal function
Any note of:
Dialysis, renal transplant
Serum Creatinine >1.3 mg/dL
Y/N
Bleeding history or 
predisposition
Any note of:
Major bleeding requiring 
hospitalization or blood 
transfusion or causing a 
decrease in hemoglobin  level 
of > 2 g/L
Y/N
Renal function
Estimated GFR closest to index 
dispensing
Y/N
The value
Serum Creatinine
Closest to the index date
Y/N
The value
Abnormal liver function
Any note of:
Liver disease, cirrhosis,
Active hepatitis C
Active hepatitis B
Active hepatitis A
AST/ALT >3X upper limit of 
normal 
Absence of any note would be 
considered as absence of the 
disease
Y/N

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Outcome definitions
Table 1 (cont'd)
Outcome
Definition
Present
Categories if present
Secondary
Duration of atrial 
fibrillation 
Years/months prior to initiation 
of dabigatran/warfarin
Y/N
Number of months prior to 
index date for the earliest note
History of adherence
Any note of patient 
adherence/lack thereof in EMR
Y/N
Non-adherent
Adherent
History/duration of 
hypertension
Any note of:
Hypertension
SBP >120 mmHg
Hypertension drugs
Y/N
Number of months prior to 
index date for the earliest date
Uncontrolled 
Hypertension (for 
HASBLED)
SBP >160 mmHg using the 
most recent information prior 
to index date
Y/N
History/duration of CHF 
Any note of:
CHF
Y/N
Number of months prior to 
index date for the earliest note
Prior TIA 
Any note of TIA
Y/N
Also, number of months prior 
to index date if noted
Diabetes
Any note of:
Diabetes
Absence of any note of diabetes 
will be considered as absence 
of the disease
Y/N
Hyperlipidemia
Any note of;
Hyperlipidemia, dyslipidemia, 
LDL>130 mg/dl
HAS Bled Score
As recorded (if recorded) or 
calculated from EMR data. 
 
Recorded/
calculated
/none
The value
Use of antiplatelets or 
NSAIDs (needed for 
HASBLED)
Use of aspirin, clopidogrel, 
prasugrel, ticagrelor or NSADs 
(within 1 month or on the 
index date) 
Y/N
INR
Closest to the index date
Y/N
The value

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Components of HAS-BLED bleeding risk score*
Table 2
HAS-BLED item
Points
Hypertension (uncontrolled)
1
Abnormal renal and liver function (1 point each)
1 or 2
Stroke
1
Bleeding history or predisposition (anemia)
1
Labile INR
1
Elderly
1
Drugs or alcohol (1 point each)
1 or 2
*HAS-BLED score is calculated by adding the specified points for each of the conditions listed. [R10-6394]

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9.4
DATA SOURCES
The data source for this project will be data from MarketScan, a research claims database 
from commercial employer-sponsored health plans, through December 2014. 
 
 
 
  
 
 
 
 
 

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9.5
STUDY SIZE
This study will identify covariates that represent potential confounding variables among 
dabigatran and warfarin users within a sampled population. The ability to identify a covariate 
and the precision for estimating potential confounding from the covariate will depend on the 
numbers of patients from whom electronic medical record data is extracted, and features of 
the covariate itself, such as how certain it is to be recorded in the medical record and whether 
binary (yes/no) or other. 
The PS-matched dabigatran and warfarin initiators during Oct 2010– December 2013 period 
are approximately 20,000 each. Assuming that 5.5% of this study group will be linked to 
EMR, the EMR-linked subset up to Dec 2013 will consist of approximately 1100 patients in 
each exposure group. 
The precision of estimated prevalence for a covariate obtained from the EMR depends on the 
prevalence of the covariate, and the sample size. With 10,000 dabigatran initiators, this study 
will estimate a patient characteristic that has a 10% prevalence with considerable precision 
(the 95% confidence interval will be 9.4% to 10.6%) (Table 7). A characteristic not captured 
in claims, but present in the medical record with a prevalence of 10% to 20% is plausibly a 
confounding variable and so this table enumerates characteristics with these prevalences. The 
table has not been extended below 10% since such lower prevalence characteristics have 
progressively less ability to produce meaningful confounding, so that while the study will 
have less precision to estimate such a characteristic, validation of the presence for such a low 
prevalence characteristic becomes unnecessary. At the other end of the range presented in the 
table, a characteristic with a prevalence above 20% will be identified with even greater 
precision, so that it also does not need to be depicted in the table.

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Precision of Estimates for Proportion Measures
Table 7
Numerator
Denominator
Prevalence
95% CI
Precision (s.e.)
10
100
10%
4.9%-17.6%
3.0%
100
1,000
10%
8.2%-12.0%
0.9%
1,000
10,000
10%
9.4%-10.6%
0.3%
10,000
100,000
10%
9.8%-10.2%
0.1%
100,000
1,000,000
10%
9.9%- 10.1%
0.003%
20
100
20%
12.7%-29.2%
4.0%
200
1,000
20%
17.6%-22.6%
1.3%
2,000
10,000
20%
19.2%-20.8%
0.4%
  20,000
  100,000
   20%
   19.8%-20.2%
   0.1%
200,000
1,000,000
20%
19.9%- 20.1%
0.004%
9.7
DATA ANALYSIS
9.7.1
Main analysis
Representativeness of EMR
We will assess whether the subpopulation with linked EMR data is a representative sample of 
the full MarketScan cohort of oral anticoagulant medication initiators. In the full MarketScan
cohort, the proportion of each exposure group with linked EMR data available will be 
determined. We will evaluate the claims-based covariates in the linked and non-linked 
subpopulations, separately by exposure group as well as overall, by examining prevalences 

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for categorical covariates and means and standard deviations for continuous covariates. These 
data will be summarized as in the example Table 8 and will allow us to determine if there are 
any patient characteristics that are over-represented or under-represented in the EMR 
subpopulation. If EMR linkage is only available for a subset with considerably different 
characteristics than the full cohort or among a subset where the propensity score balance is 
not retained, then further remedies might be applied such as stratification or weighting.
Association between claims-based covariates and EMR linkage.
Table 8
Claims-based covariate
Dabigatran initiators
Warfarin initiators
N=
N=
Linked
Not linked
Linked
Not linked
Proportion (N)
Age
Age category 18-35
Age category 36-64
Age category 65-74
Age category 75+
Sex
…
Objective 1: Descriptive
The analyses for this objective will occur within the subpopulation of individuals with linked 
EMR data and will focus on describing the EMR-based clinical characteristics for the 
dabigatran and warfarin initiators. This table will provide an evaluation of the association 
between the EMR clinical characteristics and initiation of dabigatran or warfarin.
 
The prevalence of each category for categorical characteristics and the mean and standard 
deviation for continuous characteristics will be calculated (as shown in the example Table 9).  
This analysis will aid in identifying EMR characteristics that are unbalanced across exposure 
groups and are therefore potential unmeasured confounders after PS matching.

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EMR clinical characteristics in matched dabigatran and warfarin 
Table 9
initiators
EMR clinical characteristic
Dabigatran 
initiators
Warfarin 
initiators
N=
N=
Smoking
Alcohol consumption
Obesity
…
Note: anticoagulant initiation
 are based on claims data
We will seek to define variables derived from the EMR in a manner that reduces missingness. 
Accordingly, most variables will be defined as presence or non-presence in the EMR, which 
we will take as a proxy of being medically-recognized. For example, if smoking is noted in 
the medical record, it will be recorded as present, if it is not present, it will be assumed that 
the patient does not smoke. We will also evaluate the patterns of missing data in the EMR 
clinical characteristics.
Objective 2: Prediction Rule
Within the EMR linked subpopulation, a prediction model will be developed 
with particular focus on covariates that are found to represent potentially 
important unmeasured confounding in the primary analysis.  Specifically, a logistic 
regression model (potentially a multinomial logistic regression, depending on the number of 
categories for the EMR characteristic) will be estimated 
 that 
uses the EMR covariate as the model outcome and all available claims-based covariates as 
predictors.
 
 
For 
continuous EMR-based characteristic, a 
linear regression model will be estimated.  Coefficients from these models will be reported as 
well as prediction accuracy, defined by the R2 statistic.

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Objective 3: Potential Unmeasured Confounding Assessment
To assess the potential for confounding, we will conduct a series of logistic regression 
analyses that predict exposure (dabigatran versus warfarin) from covariates.  
 
 
 
 
 
 
 
 
 
 

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9.7.2
Further analysis
None defined.
9.8
QUALITY CONTROL
All aspects of data analysis will be conducted according to standard procedures of the 
Division of Pharmacoepidemiology. Programming for this project will be conducted by a 
primary analyst and validated by a separate analyst (validation analyst). For all data 
processing and analysis steps, the validation analyst will review the program along with input 
and output data sets, and for select steps of the project will employ double programming 
techniques to reduce the potential for programming errors.
9.9
LIMITATIONS OF THE RESEARCH METHODS
This is a validation study and we are not assessing the association between EMR-based 
patient characteristics and clinical outcomes.
The source claims data include limitations with respect to certainty of the capture of 
exposure, covariates, and outcomes. As a comprehensive insurance database, essentially all 
billable medical services will result in claims for reimbursement, so that the certainty of 
capture is tied to likelihood of a claim being submitted to the insurer.
Although more information on covariates is captured in EMR, some data will be missing in 
EMR as well. In addition, information is assessed during routine medical care (not in a 
standardized way) and some variation in assessment persists but cannot be studied within the 
scope of the study.
As in any EMR source, there could be potentially small number of dabigatran initiators and 
they might not represent dabigatran initiators in the general population. In addition, the data 
sources for this study are commercial US data sources (based on employed people and their 
dependents), so they tend to over-represent working-age people. The data are supplemented 
with elderly through patients with certain types of Medicare coverage (Medicare Advantage); 
however, elderly with Medicare Advantage tend to be healthier and more likely to still be 
employed as compared to elderly with other types of Medicare. These features of data may 
further limit the generalizability in that the results will be most generalizable to US people
who are employed. [P13-03077, P13-03078]

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9.10
OTHER ASPECTS
Funding: Boehringer Ingelheim GmbH
9.11
SUBJECTS
9.11.1
Study group
The main cohort will consist of patients with NVAF at risk for stroke initiating warfarin and 
dabigatran, from October 2010 through December 2014.  
Inclusion criteria: 

Initiation of an oral anticoagulant is defined by a dispensing of an anticoagulant 
medication (warfarin or dabigatran) in the source data, with no dispensing of any oral 
anticoagulant in the prior 12 months. The date of that dispensing will be defined as 
the index date for that initiation.

At least one ICD-9 diagnosis code of 427.31 (atrial fibrillation) at any time prior to 
and including the index date 

At least 12 months (365 days) continuous enrollment, defined as ≤32 days enrollment 
gap using enrollment and disenrollment dates, preceding the index date 

18 years of age and older at index date

CHA2DS2-VASc score ≥ 1
Exclusion criteria: 

Patients with a nursing home stay during baseline

Patients with missing or ambiguous age or sex information.

Patients with a dispensing of any oral anticoagulant during the 12 months preceding 
and including the index date

Patients with documented evidence of valvular disease defined as at least 1 inpatient 
or outpatient ICD-9 Dx code of [R11-4334] 
394.x (diseases of mitral valve)
395.x (diseases of aortic valve)
396.x (diseases of mitral and aortic valve)
397.x (diseases of other endocardial structures)
398.9x (other and unspecified rheumatic heart diseases)
V42.2 (heart valve replaced by transplant)
V43.3 (heart valve replaced by a mechanical device/prosthesis)
OR 
ICD-9 procedure code 35.1x (open heart valvuloplasty without replacement), 35.2x 
(replacement of heart valve) [R03-1232]
OR 
one of the following CPT codes:
33660-33665 (atrioventricular valve repair)
33400-33403 (aortic valve valvuloplasty)
33420-33430 (mitral valve repair/valvuloplasty/replacement)
33460 (valvectomy, tricuspid valve, with cardiopulmonary bypass)
33463-33468 (tricuspid valve repair/valvuloplasty/replacement)
33475 (replacement, pulmonary valve)

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33496 (prosthetic valve dysfunction repair)
0257T (implantation of catheter-delivered prosthetic aortic heart valve; open thoracic 
approach)
0258T (transthoracic cardiac exposure for catheter-delivered aortic valve replacement; 
without cardiopulmonary bypass)
0259T (transthoracic cardiac exposure for catheter-delivered aortic valve replacement; 
with cardiopulmonary bypass)
0262T (implantation of catheter-delivered prosthetic pulmonary valve, endovascular 
approach)
at any time prior to and including the index date.
Cohort formation
Initiators of dabigatran and warfarin will be matched on their PS to initiate the treatment of 
interest (dabigatran) and the calendar quarter of initiation. PS will be derived from predicted 
probabilities of treatment initiation with dabigatran as compared to warfarin estimated in a 
logistic regression model that includes all claims-based covariates identified in this protocol 
 The PS will be 
used to match dabigatran initiators to warfarin initiators on a 1:1 fixed ratio basis using a 
nearest-neighbor algorithm. 
9.11.2
EMR-linked subset
From the PS-matched cohorts we will identify patients who have EMR data available. Most 
analyses will be performed in this group. We expect that the EMR-linked subset is a random 
subset so that the covariate balance achieved by propensity score matching in the overall 
cohorts will be retained in the EMR subsets. However, due to chance, some imbalances in
baseline characteristics may occur. We will check the average PS among the linked subset 
and inspect covariates among them prior to proceeding with the analysis.  In the event of 
imbalances, we will proceed with analyses outlined after stratifying by the propensity score.

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Figure 1
Patient Selection Flow Chart
We will provide Truven with linkage IDs and exposure indicators. Within the EMR-linked 
subset, Truven will evaluate the balance of the EMR clinical characteristics across exposure 
groups (see section 9.7 Data Analysis for detailed description).

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9.12
BIAS
Various design and analysis methods will be implemented to reduce the potential for bias in 
the study. We will employ new-user cohorts of dabigatran and warfarin in order to address 
differences that might arise in the comparison of newer and older treatments, such as survivor 
bias and attrition of susceptibles. The comparator cohort will be formed warfarin initiators 
with similar clinical profiles to dabigatran initiators to reduce confounding by indication.  We 
will employ propensity score matching of dabigatran and warfarin cohorts in order to 
improve the balance of the cohorts with respect to numerous variables.  
As a validation study, the aim of this study is to assess the magnitude of potential 
confounding that might be present in the main cohort study as a result of non-claims 
characteristics that are both associated with choice of anticoagulant and predictive of study 
outcomes.

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10.
PROTECTION OF HUMAN SUBJECTS
This study will be submitted to the Institutional Review Board (IRB) of 
 

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11.
MANAGEMENT AND REPORTING OF ADVERSE 
EVENTS/ADVERSE REACTIONS
As an observational study based on existing data all patient data will be de-identified and 
analyzed in aggregate. Individual patient safety related information will not be captured 
during this study. Thus, individual safety reporting is not applicable for this study.

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12.
PLANS FOR DISSEMINATING AND COMMUNICATING 
STUDY RESULTS
The interim and final reports will consist of a description of the methods, including patient 
selection and variable definitions along with tabular summaries of cohort characteristics, 
numbers of patients receiving each anticoagulant and associated follow-up time.  Counts of 
outcomes and corresponding rates and measures of association will be presented. The tabular 
results will be followed by an interpretive summary along with a discussion of the findings 
and implications.
Manuscripts describing this work will be submitted for publication in peer-review journals. 
Findings may also be submitted for presentation at scientific conferences.

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13.
REFERENCES
13.1
PUBLISHED REFERENCES
P11-11875
Potpara TS, Lip GY. New anticoagulation drugs for atrial fibrillation. 
Clin Pharmacol Ther 2011;90:502-6.
R13-2768
Sturmer T, Glynn RJ, Rothman KJ, Avorn J, Schneeweiss S. 
Adjustments for unmeasured confounders in pharmacoepidemiologic 
database studies using external information. Med Care 2007;45:S158-65.
R11-4334
Birman-Deych E, Waterman AD, Yan Y, Nilasena DS, Radford MJ, 
Gage BF. Accuracy of ICD-9-CM codes for identifying cardiovascular 
and stroke risk factors. Med Care 2005;43:480-5.
R03-1232
Go AS, Hylek EM, Borowsky LH, Phillips KA, Selby JV, Singer DE. 
Warfarin use among ambulatory patients with nonvalvular atrial 
fibrillation: the anticoagulation and risk factors in atrial fibrillation 
(ATRIA) study. Ann Intern Med 1999;131:927-34.
P06-10925
Gage BF, Waterman AD, Shannon W, Boechler M, Rich MW, Radford 
MJ. Validation of clinical classification schemes for predicting stroke: 
results from the National Registry of Atrial Fibrillation. JAMA 
2001;285:2864-70.
R10-5332
Lip GY, Nieuwlaat R, Pisters R, Lane DA, Crijns HJ. Refining clinical 
risk stratification for predicting stroke and thromboembolism in atrial 
fibrillation using a novel risk factor-based approach: the euro heart 
survey on atrial fibrillation. Chest 2010;137:263-72.
R10-6394
Pisters R, Lane DA, Nieuwlaat R, de Vos CB, Crijns HJ, Lip GY. A 
novel user-friendly score (HAS-BLED) to assess 1-year risk of major 
bleeding in patients with atrial fibrillation: the Euro Heart Survey. Chest 
2010;138:1093-100.
P12-01676
You JJ, Singer DE, Howard PA, et al. Antithrombotic therapy for atrial 
fibrillation: Antithrombotic Therapy and Prevention of Thrombosis, 9th 
ed: American College of Chest Physicians Evidence-Based Clinical 
Practice Guidelines. Chest 2012;141:e531S-75S.
R11-4190
Granger CB, Alexander JH, McMurray JJ, et al. Apixaban versus 
warfarin in patients with atrial fibrillation. N Engl J Med 2011;365:981-
92.
P09-11669
Connolly SJ, Ezekowitz MD, Yusuf S, et al. Dabigatran versus warfarin 
in patients with atrial fibrillation. N Engl J Med 2009;361:1139-51.
R11-4223
Patel MR, Mahaffey KW, Garg J, et al. Rivaroxaban versus warfarin in 
nonvalvular atrial fibrillation. N Engl J Med 2011;365:883-91.

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P14-04526
Camm AJ, Lip GY, De Caterina R, et al. 2012 focused update of the ESC 
Guidelines for the management of atrial fibrillation: An update of the 
2010 ESC Guidelines for the management of atrial fibrillation * 
Developed with the special contribution of the European Heart Rhythm 
Association. Europace 2012;14:1385-413.
R13-4176
Olesen JB, Lip GY, Hansen PR, et al. Bleeding risk in 'real world' 
patients with atrial fibrillation: comparison of two established bleeding 
prediction schemes in a nationwide cohort. J Thromb Haemost 
2011;9:1460-7.
R13-0521
Molnar AO, Coca SG, Devereaux PJ, et al. Statin use associates with a 
lower incidence of acute kidney injury after major elective surgery. J Am 
Soc Nephrol 2011;22:939-46.
R11-4329
Winkelmayer WC, Schneeweiss S, Mogun H, Patrick AR, Avorn J, 
Solomon DH. Identification of individuals with CKD from Medicare 
claims data: a validation study. Am J Kidney Dis 2005;46:225-32.
P12-05791
FDA draft guidance for industry on Drug Interaction Studies - Study 
Design, Data Analysis, and Implications for Dosing and Labelling. Feb 
2012.
R12-0541
Friberg L, Rosenqvist M, Lip GY. Evaluation of risk stratification 
schemes for ischaemic stroke and bleeding in 182 678 patients with atrial 
fibrillation: the Swedish Atrial Fibrillation cohort study. Eur Heart J 
2012;33:1500-10.
P13-03077
West SL, Strom BL, Freundlich B, Normand E, Koch G, Savitz DA. 
Completeness of prescription recording in outpatient medical records 
from a health maintenance organization. J Clin Epidemiol 1994;47:165-
71.
P13-03078
West SL, Savitz DA, Koch G, Strom BL, Guess HA, Hartzema A. Recall 
accuracy for prescription medications: self-report compared with 
database information. Am J Epidemiol 1995;142:1103-12.
13.2
UNPUBLISHED REFERENCES
None

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ANNEX 1. LIST OF STAND-ALONE DOCUMENTS
None

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ANNEX 3. ADDITIONAL INFORMATION
None