The Empagliflozin vs. DPP-4 Inhibitors and GLP-1 Receptor Agonists Cost of Care Study: a German Claims Data Analysis
Conditions: Diabetes Mellitus, Type 2
Sponsor: Boehringer Ingelheim

001-MCS-90-124_RD-01 (4.0)
Protocol for non-interventional studies based on existing data
TITLE PAGE
Document Number:
BI Study Number:
1245.221
BI Investigational
Product(s):
Jardiance® (Empagliflozin)
Title:
Empagliflozin vs. DPP-4 inhibitors and GLP-1 Receptor 
Agonists Cost of Care (“EDGE-COS”) Study: a German 
claims data analysis
Protocol version 
identifier:
1.0
Date of the last version of 
the protocol:
20 January 2020
 
PASS:
No
EU PAS register 
number:
31950
Active substance:
A10BK03 Empagliflozin (“EMPA”)
A10BH Dipeptidyl peptidase 4 inhibitors (“DPP-4”) 

A10BH01 Sitagliptin (“SITA”)
A10BJ Glucagon-like peptide-1 receptor agonists (“GLP-1-RA”)
Medicinal product:
Jardiance
Product reference:
Europe: EMEA/H/C/002677
Procedure number:
N/A
Joint PASS:
No
Research question and 
objectives:
The main objective of this study is to compare healthcare cost 
associated with different antidiabetic drug treatments among the 
incident users of Empagliflozin, any DPP-4i (specifically 
Sitagliptin) or any GLP-1-RA, based on claims data covering up 
to four years of follow-up.
Countries of study:
Germany
Country-specific 
analyses: 
This protocol is the locally amended German protocol based on 
global master protocol template
Author:
 

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Marketing authorisation 
holder:
MAH contact person:
In case of PASS, add:
<EU-QPPV:>
N/A
In case of PASS, add:
<Signature
of 
EU-
QPPV:>
N/A
Date:
20 January 2020
 
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1.
TABLE OF CONTENTS
TITLE PAGE.................................................................................................................. 1
1.
TABLE OF CONTENTS....................................................................................... 3
2.
LIST OF ABBREVIATIONS................................................................................ 5
3.
RESPONSIBLE PARTIES.................................................................................... 6
4.
ABSTRACT........................................................................................................... 7
5.
AMENDMENTS AND UPDATES..................................................................... 10
6.
MILESTONES..................................................................................................... 11
7.
RATIONALE AND BACKGROUND................................................................ 12
8.
RESEARCH QUESTION AND OBJECTIVES ................................................. 13
8.1
PRIMARY OBJECTIVES.............................................................................. 13
8.2
SECONDARY OBJECTIVES........................................................................ 13
9.
RESEARCH METHODS .................................................................................... 14
9.1
STUDY DESIGN............................................................................................ 14
9.2
SETTING........................................................................................................ 15
9.2.1
Overview ................................................................................................. 15
9.2.2
Study population ..................................................................................... 16
9.2.3
Study period and follow-up..................................................................... 22
9.3
VARIABLES .................................................................................................. 23
9.3.1
Exposures: Study Drugs.......................................................................... 23
9.3.2
Covariates at baseline (Patient Characteristics) ...................................... 24
9.3.3
Outcomes................................................................................................. 25
9.3.4
Other variables required for analyses...................................................... 30
9.4
DATA SOURCES........................................................................................... 31
9.5
STUDY SIZE.................................................................................................. 32
9.6
DATA MANAGEMENT................................................................................ 33
9.7
DATA ANALYSIS......................................................................................... 33
9.7.1
Baseline characteristics / Descriptive statistics....................................... 33
9.7.2
Propensity score methodology ................................................................ 35
9.7.3
Primary outcomes.................................................................................... 38
9.7.1
Secondary outcomes................................................................................ 39
9.7.2
Sensitivity analyses ................................................................................. 39
9.8
QUALITY CONTROL................................................................................... 40
9.9
LIMITATIONS OF THE RESEARCH METHODS...................................... 41

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9.9.1
Data sources and variables...................................................................... 41
9.9.2
Identifying patients with T2DM.............................................................. 41
9.9.3
Drug exposure ......................................................................................... 41
9.9.4
Bias and confounding.............................................................................. 41
9.9.5
Analyses .................................................................................................. 41
9.10
OTHER ASPECTS ......................................................................................... 42
9.11
SUBJECTS...................................................................................................... 42
9.11.1
Cases........................................................................................................ 42
9.11.2
Controls................................................................................................... 42
9.12
BIAS................................................................................................................ 42
10.
PROTECTION OF HUMAN SUBJECTS .......................................................... 43
11.
MANAGEMENT AND REPORTING OF ADVERSE EVENTS/ADVERSE 
REACTIONS....................................................................................................... 44
12.
PLANS FOR DISSEMINATING AND COMMUNICATING STUDY 
RESULTS............................................................................................................ 45
13.
REFERENCES .................................................................................................... 46
Annex 1. DEFINITIONS FOR COVARIATES AT BASELINE ................................ 48
Annex 2. CHARLSON-COMORBIDITY-SCORE (CCI) AND IS COMPONENTS 
(EXCL: AGE FACTOR)..................................................................................... 52

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2.
LIST OF ABBREVIATIONS
aDCSI
Adapted Diabetes Complications Severity Index
ATC
Anatomical Therapeutic Chemical classification 
BI
Boehringer Ingelheim International GmbH
BMI
Body Mass Index
CABG
Coronary artery bypass grafting
CHF
Congestive heart failure
CI
Confidence interval
CCI
Charlson-Comorbidity-Score
CKD-EPI
Chronic Kidney Disease Epidemiology Collaboration
COPD
Chronic obstructive pulmonary disease
CV
Cardiovascular
DM
Diabetes mellitus
DPP-4i
Dipeptidyl peptidase-4 inhibitor
EMPA
Empagliflozin
ENCePP
European 
Network 
of 
Centres 
for 
Pharmacoepidemiology 
and 
Pharmacovigilance
ESRD
End-stage renal disease
FGS
“Facharztgruppenschlüssel”
GLM
Generalized linear model
GLP-1-RA
Glucagon-Like Peptide-1 Receptor Agonist
GP
General physician
HbA1c
Glycated hemoglobin A1c
HCRU
Healthcare resource utilization
ICD-10
International Classification of Diseases, 10th revision
KDIGO
Kidney Disease Improving Global Outcomes
MA
Marketing authorization
MET
Metformin
MI
Myocardial infarction
OPS
“Operationen- und Prozedurenschlüssel”
PS
Propensity score
PSM
Propensity score matching
PTCA
Percutaneous transluminal coronary angioplasty
SAP
Statistical analysis plan
SD
Standard deviation
SGLT-2i
Sodium-glucose cotransporter-2 inhibitor
SITA
Sitagliptin
STD.
Standardized difference
SU
Sulfonylureas
T1DM
Type 1 diabetes mellitus
T2DM
Type 2 diabetes mellitus

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3.
RESPONSIBLE PARTIES
Function
Name
Affiliation
Project 
Boehringer Ingelheim
Medical Advisor
tbd
Boehringer Ingelheim
Scientific Advisor
Scientific Advisor
Scientific 
Main Project Management
Claims Data Management

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4.
ABSTRACT
                                                
1 extended to 30/06/2019 in case this data can already be provided by 
 at time of data validation (end of 
Q1 2020)
Name of company:
Boehringer Ingelheim
Name of the finished medicinal 
product:
Jardiance®
Name of active ingredient:
Empagliflozin (A10BK03)
Protocol date:
Study 
number:
Version/Revision:
Version/Revision 
date:
21 May 2019
1245.221
1.0
14 September 2022
Title of study:
Empagliflozin vs. DPP-4 inhibitors and GLP-1 Receptor Agonists Cost 
of Care (“EDGE-COS”) Study: a German claims data analysis
Rationale and 
background:
In recent years, for most of the available SGLT-2 inhibitors (SGLT-2i), 
DPP-4 inhibitors (DPP-4i) and GLP-1 receptor agonists (GLP-1-RA), 
cardiovascular outcome studies have been conducted. However, much 
less is known about cost (both direct and indirect cost) and healthcare 
resource use (HCRU) associated with therapy of type 2 diabetes mellitus 
(T2DM) patients with SGLT-2i, DPP-4i and GLP-1-RA. Accordingly, 
the objective of this study is to collect these data.
Research question 
and objectives:
The overall objective of this study is to examine direct and indirect 
healthcare cost associated with use of Empagliflozin, any DPP-4i
(specifically Sitagliptin) or any GLP-1-RA among newly treated T2DM 
patients in Germany. Costs will be compared between Empagliflozin
and DPP-4 inhibitor (in particular Sitagliptin) or GLP-1-RA use.
Additional subgroup analyses will be conducted according to
observed/unobserved concomitant antidiabetic therapies (e.g. 
insulin or metformin use).
Study design:
This will be a non-interventional, retrospective study using claims data 
from (at least) one major German sick fund / sick-fund service organi-
zation (here: 
).
Population:
This study will include all continuously insured patients with at least one 
inpatient or outpatient diagnosis of T2DM (ICD-10 code: E11.-) in the 
 database between 2014 and 20181. The study population will 
consist of patients initiating use of either Empagliflozin, any DPP-4i or 
any GLP-1-RA. 
Variables:
Variables describing all-cause and T2DM-associated healthcare cost 
will be medication costs (based on list prices), costs for prescribed 
medical aids and remedies, costs related to outpatient visits, inpatient 
costs, rehabilitation costs and indirect costs caused by days absent from 

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2 (or any other SGLT-2i in the EMPA group or any other DPP-4i in the SITA group)
work. These costs can be distinguished and directly T2DM-related and 
T2DM-unrelated costs based on documented primary diagnoses for each 
visit (identified via ICD-10 E11; E16), respectively ATCs/PZNs for 
medication and aid costs.
Variables related to healthcare resource use (HCRU) will be number of 
outpatient visits and number of (first and recurrent) hospitalizations,
rehabilitations, length of stay and days absent from work. The composite 
outcomes for overall direct and indirect costs will be based on these 
variables.
Other variables collected from the data sources, according to data 
availability, will include covariates at baseline on patient characteristics
like: age, sex, comorbidity level (using Charlson Comorbidity Index and
Top 10 comorbidities that are not covered by CCI), level of care, prior 
use of antidiabetic drugs, previously observed (T2DM- and non-T2DM 
related) healthcare costs and days absent from work.
Data sources:
This study will utilize a Germany-wide claims database with a probable 
size of two million insured people, provided by 
Study size:
The anticipated number of T2DM patients in the 
 database is 
expected to be approximately 200,000 – 250,000. 
Data analysis:
The propensity score matching (PSM) will be used to reduce 
confounding in the comparative cost analyses across study cohorts.
Individual patient characteristics that are relevant to treatment assign-
ment are combined into one item, the propensity score (PS), which 
measures the conditional probability that the patient is assigned to a 
treatment rather than the alternative one. Pairwise PS models between i) 
EMPA and DPP-4i and ii) EMPA and SITA as well as iii) EMPA and 
GLP-1-RA will be developed using logistic regression including 
appropriate covariates.
For all included patients, descriptive statistics will be generated 
separately in each cohort before and after matching. Continuous 
variables will be described by mean, standard deviation, median, 25th,
and 75th percentiles, minimum and maximum. Categorical variables 
and continuous variables that are also categorized will be described by 
proportion and frequency in each category. All-cause and T2DM-related
healthcare costs, as well as HCRU outcomes, will be analysed and 
compared across cohorts by cost/numbers per patient year (PY).
Additionally, subgroup analyses according to observed concomitant 
antidiabetic treatment will be conducted for 1) insulin-naïve patients, 2) 
patients receiving only a single antidiabetic drug (AD), 3) patients 
receiving a dual therapy with metformin, and 4) patients receiving 
insulin in addition to the respective study drug. Patients with a) 
discontinuation of drug of interest (DDD-based grace period of 90 days), 
b) switch to or c) treatment intensification with any drug of the two other 
study arms2 will be censored accordingly.
Milestones:
A first steering board meeting for discussion of the study design as
outlined in this protocol draft is planned for Q1 2020. Completion of 
data validation and statistical analysis is planned for Q2/Q3 2020 

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depending on the data delivery process. The final report will become
available end of Q3 2020.

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5.
AMENDMENTS AND UPDATES
Not applicable. 

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6.
MILESTONES
The following table estimates timelines for the milestones of this German study.
Milestone
Planned Date
Study protocol final (1st Steering board)
Q1 2020
Start of data validation/analysis
Q2 2020
Results presentation (2nd Steering board) and final report available
Q3 2020

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7.
RATIONALE AND BACKGROUND
The number of patients with type 2 diabetes mellitus (T2DM) is increasing all over the world 
[1]. In Europe, the number of patients with diabetes is estimated to be 55 million, and it is 
estimated to increase to 67 million by 2030. T2DM constitutes 90% of these numbers. The 
prevalence of T2DM in Germany ranges between 7.2% (population aged 18 to 79 years) based 
on health examination surveys of the 
 and 9.9% (among all age groups) 
based on statutory health insurance data [2].
According to the German National Disease Management Guideline on the Treatment of Type 
2 Diabetes, there are four stages in the treatment of T2DM [3]. When individualized HbA1c 
target is not achieved after three to six months treatment, the first line drug, metformin as 
monotherapy, is added to the first step lifestyle therapy (education, diet, physical activity). If 
metformin is not tolerated, DPP-4 inhibitors (DPP-4i), insulin, sulfonylurea or SGLT-2 
inhibitors (SGLT-2i) are used. The next step is a two-drug combination or insulin mono 
therapy.
SGLT-2i represent one of the most recent pharmacological treatment options of T2DM [7]. 
These drugs have been available since 2012. The globally available SGLT-2i include cana-
gliflozin, dapagliflozin, empagliflozin, and ertugliflozin. Additional SGLT-2i (e.g. ipragli-
flozin, tofogliflozin) are available also in Asia [7]. SGLT-2i are often used as a second-line 
treatment of T2DM [8]. In addition to the glucose-lowering effects, these drugs also have 
beneficial effects on weight, blood pressure, and potentially also lipids [7]. 
In recent years, for most of the available SGLT-2 inhibitors (SGLT-2i), DPP-4 inhibitors (DPP-
4i) and GLP-1 receptor agonists (GLP-1-RA), cardiovascular outcome studies have been 
conducted. CV events contribute to up to 50% of healthcare costs in the treatment of T2DM. 
The main cost drivers were hospitalization, drug, and outpatient care costs. However, much 
less is known about the drug-specific cost and healthcare resource use (HCRU) associated 
directly with therapy of type 2 diabetes mellitus (T2DM) patients with SGLT-2i, DPP-4i and 
GLP-1-RA. Accordingly, the objective of this study is to collect these data. The study results 
shall be used to enter discussions with stakeholders in the German healthcare system (payers, 
physicians´ associations, etc.) about the real-world use and relevance of Empagliflozin in the 
treatment of T2DM patients.

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8.
RESEARCH QUESTION AND OBJECTIVES
The overall objective of this study is to examine healthcare cost associated with the treatment 
with Empagliflozin versus any DPP-4i, Sitagliptin and any GLP-1-RA therapies in Germany.
8.1
PRIMARY OBJECTIVES 
Primary objective: Compare
a) all-cause
healthcare costs 
(excluding indirect costs of days absent from work)
b) T2DM-associated
c) CV-event-associated
a) all-cause
healthcare resource utilization (HCRU)
b) T2DM-associated
c) CV-event-associated
among patients newly treated with:
i.
Empagliflozin vs. any DPP-4i
ii.
Empagliflozin vs. Sitagliptin
iii.
Empagliflozin vs. any GLP-1-RA
8.2
SECONDARY OBJECTIVES 
Secondary objective 1: Compare all-cause, T2DM-associated and CV-event-associated
healthcare costs (direct costs and indirect costs of days absent from work3) between above 
defined patients (i – iii) for the following subgroups4:
a.
Only patients without concomitant insulin treatment
b. Only patients without any concomitant antidiabetic treatment
c.
Only patients with concomitant metformin treatment (only dual therapy)
d. Only patients with concomitant insulin treatment
Secondary objective 2: Compare all-cause as well as T2DM-associated healthcare resource 
utilization (HCRU) between above defined patients (i – iii) for the following subgroups:
a.
Only patients without concomitant insulin treatment
b. Only patients without any concomitant antidiabetic treatment
c.
Only patients with concomitant metformin treatment (only dual therapy)
d. Only patients with concomitant insulin treatment
                                                
3 Costs displayed for patients with confirmed status of employment only
4 Healthcare costs including indirect costs of days absent from work will also be compared for the overall 
population

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9.
RESEARCH METHODS
9.1
STUDY DESIGN
This will be a non-interventional study using existing data and including patients with a 
diagnosis of T2DM in German claims data. German claims data are a valid and often used 
source for health-economic analyses [2–4]. So far, the analysis is planned to be performed in 
collaboration with 
, a company providing services to several sickness 
funds. The strengths of a claims data analysis are the use of a large database and the unselected 
nature of the data and representativeness of the underlying population. Moreover, HCRU and 
cost are well covered in such a database. Generally, a claims dataset seems to be an appropriate 
data source to answer the study questions mentioned above. Therefore, claims data are 
extremely valuable for the effective examination of health care costs, and HCRU.
The dataset will cover the period 01/01/2014 – 31/12/20185 and include all T2DM patients who
initiated a treatment with either Empagliflozin, DPP-4i or GLP-1-RA (=index date); specific 
inclusion criteria are outlined below. Figure 1 below describes the main design.
Figure 1. Study inclusion and follow-up periods
                                                
5 extended to 30/06/2019 in case this data can already be provided by 
 at time of data validation (end of 
Q1 2020)

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9.2
SETTING
9.2.1
Overview
Claims data from several German public sickness funds (one uniform dataset provided by 
) with a probable size of two million insured people will be requested 
through a university-affiliated partner, 
 (coverage: 01/01/2014 – 31/12/20186). The 
database contains anonymized patient-based data regarding prescriptions, outpatient and 
inpatient visits (including procedures and diagnoses), and outpatient prescriptions and selected 
dispensed medications during inpatient treatment (only in case of drug-specific reimbursement) 
of the patients. A general overview of information that is available in German claims data is 
provided in Figure 2; a detailed description of variables that will be used in this study is 
provided in section 9.3.
Figure 2: Available variables in German claims databases
                                                
6 extended to 30/06/2019 in case this data can already be provided by 
at time of data validation (end of 
Q1 2020)

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9.2.2
Study population
Generally, only prevalent T2DM patients continuously insured by the respective sickness funds 
will be considered in this study and thus, the following basic selection criteria need to be met:
Inclusion criteria:

Continuous insurance by the sickness funds for the entire period (01/01/2014 –
31/12/2018; death of a patient is the only accepted exception from this rule)

At least two outpatient T2DM diagnoses (ICD E11.-) in two different quarters 
and/or at least one inpatient T2DM diagnosis (ICD E11.-) in the period: 12 months 
before index date – till 3 months after index date

At least one prescription of Empagliflozin, DPP-4i or GLP-1-RA in the inclusion 
period; first prescription date = index date (between 01/01/2015 – 31.12.2018)
Exclusion criteria:

Any prescription of SGLT-2i, DPP-4i or GLP-1-RA in the baseline (“washout”) 
period (01/01/2014 – 31/12/2014); depending on identified patient numbers in the 
empagliflozin vs. GLP-1-RA comparison, prior use of DPP-4i will be allowed 
within the period before the index date

Patients with two outpatient diagnoses or one inpatient – OR – any diagnosis of 
o
T1DM (E10),
o
Secondary diabetes

Malnutrition-related diabetes mellitus (E12)

Other specified diabetes mellitus (E13) or
o
Gestational diabetes (O24),
within the 1-year “lookback period” before index date

Patients with pre-existing diagnosis of neoplasms (C00-C97, D00-D48) or treated 
with cancer drugs, within the lookback period (ATC main group: L)

Patients with another pre-existing diagnosis of one of the top-5 most cost-intensive
comorbidities (according to the 80 most cost-intensive diseases as defined by the 
German Federal Insurance Office: “Hierarchisierte Morbiditätsgruppen”; HMGs)
These criteria ensure that only patients with T2DM, and not any other type of diabetes, are 
included in the study. In addition, the exclusion of patients with conditions that will likely skew 
the costs (e.g., cancer) ensures unbiased cost estimates. 

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Based on all patients fulfilling the basic criteria mentioned above, different analysis samples 
will be selected to answer specific research questions. Table 1 provides an overview of the 
analysis samples used in this study as well as the respective selection criteria.
Table 1. Overview on analysis (sub-)samples
Sample
Abbreviation
Selection criteria
Usage
Newly treated 
Empagliflozin patients
EMPA
All patients with at least one 
prescription of Empagliflozin during 
the inclusion period (index date = 
date of 1st prescription of 
Empagliflozin)
No prescription of DPP-4i, GLP-1-
RA or any other SGLT-2i before 
index date, except prior use of DPP-
4i being allowed in the EMPA vs. 
GLP-1 comparison (in case of small 
sample size)
PRIMARY 
OBJECTIVE
-
Basis for PSM 
Newly treated DPP-4i 
patients
DPP-4
All patients with at least one 
prescription of any DPP-4i during 
the inclusion period (index date = 
date of 1st prescription of any DPP-
4i)
No prescription of GLP-1-RA or 
SGLT-2i before index date
PRIMARY 
OBJECTIVE
-
Basis for PSM 
Newly treated Sitagliptin
patients
SITA
All patients with at least one 
prescription of Sitagliptin during the 
inclusion period (index date = date 
of 1st prescription of Sitagliptin)
No prescription of SGLT-2i, GLP-1-
RA or any other DPP-4i before 
index date
PRIMARY 
OBJECTIVE
-
Basis for PSM 
Newly treated GLP-1-RA 
patients
GLP-1
All patients with at least one 
prescription of GLP-1-RA during 
the inclusion period (index date = 
date of 1st prescription of GLP-1-
RA)
No prescription of any DPP-4i or 
SGLT-2i before index date, except 
prior use of DPP-4i being allowed in 
the EMPA vs. GLP-1 comparison
(in case of small sample size)
PRIMARY 
OBJECTIVE
-
Basis for PSM 
Excluding unmatched patient for pairwise PSM comparisons:

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Newly treated 
Empagliflozin patients 
matched to newly treated 
DPP-4i patients
EMPA matched 
to DPP-4
Only EMPA patients with similar 
propensity scores when compared to 
the DPP-4 cohort
PRIMARY 
OBJECTIVE
-
Comparison 
for costs
-
Comparison 
for HCRU 
Newly treated 
Empagliflozin patients 
matched to newly treated 
Sitagliptin patients
EMPA matched 
to SITA
Only EMPA patients with similar 
propensity scores when compared to 
the SITA cohort
PRIMARY 
OBJECTIVE
-
Comparison 
for costs
-
Comparison 
for HCRU 
Newly treated 
Empagliflozin patients 
matched to newly treated 
GLP-1-RA patients
EMPA matched 
to GLP-1
Only EMPA patients with similar 
propensity scores when compared to 
the GLP-1 cohort
PRIMARY 
OBJECTIVE
-
Comparison 
for costs
-
Comparison 
for HCRU
Newly treated DPP-4i 
patients matched to newly 
treated Empagliflozin
patients
DPP-4 matched 
to EMPA
Only DPP-4 patients with similar 
propensity scores when compared to 
the EMPA cohort
PRIMARY 
OBJECTIVE
-
Comparison 
for costs
-
Comparison 
for HCRU 
Newly treated Sitagliptin
patients matched to newly 
treated Empagliflozin
patients
SITA matched 
to EMPA
Only SITA patients with similar 
propensity scores when compared to 
the EMPA cohort
PRIMARY 
OBJECTIVE
-
Comparison 
for costs
-
Comparison 
for HCRU
Newly treated 
Empagliflozin patients 
matched to newly treated 
Empagliflozin patients
GLP-1 matched 
to EMPA
Only GLP-1 patients with similar 
propensity scores when compared to 
the EMPA cohort
PRIMARY 
OBJECTIVE
-
Comparison 
for costs
-
Comparison 
for HCRU
Sub-cohorts for “EMPA” (for pairwise comparisons unmatched patients will be excluded)7:
                                                
7 Including PS-matched sub-cohorts for each comparison: i) matched to DPP-4; ii) matched to SITA; iii) 
matched to GLP-1

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EMPA without 
concomitant insulin 
treatment
EMPA w/o 
insulin
No insulin prescription within 3 
months before/after index date is 
observed
SECONDARY 
OBJECTIVES
-
Comparison 
for costs
-
Comparison 
for HCRU
EMPA without 
concomitant antidiabetic 
treatment
EMPA – mono
No antidiabetic prescription within 3 
months before/after index date is 
observed
SECONDARY 
OBJECTIVES
-
Comparison 
for costs
-
Comparison 
for HCRU
EMPA with concomitant 
metformin treatment (only 
dual therapy)
EMPA – dual
At least one metformin prescription 
within 3 months before/after index 
date;
No further prescription of other 
antidiabetics within 3 months 
before/after index date is observed
SECONDARY 
OBJECTIVES
-
Comparison 
for costs
-
Comparison 
for HCRU
EMPA with concomitant 
insulin treatment
EMPA – insulin
At least one insulin prescription 
within 3 months before/after index 
date
SECONDARY 
OBJECTIVES
-
Comparison 
for costs
-
Comparison 
for HCRU
Sub-cohorts for “DPP-4” (for pairwise comparisons unmatched patients will be excluded):
DPP-4 without
concomitant insulin 
treatment
DPP-4 – w/o 
insulin
No insulin prescription within 3 
months before/after index date
SECONDARY 
OBJECTIVES
-
Comparison 
for costs
-
Comparison 
for HCRU
DPP-4 without 
concomitant antidiabetic 
treatment
DPP-4 – mono
No antidiabetic prescription within 3 
months before/after index date is 
observed
SECONDARY 
OBJECTIVES
-
Comparison 
for costs
-
Comparison 
for HCRU

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DPP-4 with concomitant 
metformin treatment (only 
dual therapy)
DPP-4 – dual
At least one metformin prescription 
within 3 months before/after index 
date;
No further prescription of other 
antidiabetics within 3 months 
before/after index date is observed
SECONDARY 
OBJECTIVES
-
Comparison 
for costs
-
Comparison 
for HCRU
DPP-4 with concomitant 
insulin treatment
DPP-4 – insulin
At least one insulin prescription 
within 3 months before/after index 
date
SECONDARY 
OBJECTIVES
-
Comparison 
for costs
-
Comparison 
for HCRU
Sub-cohorts for “SITA” (for pairwise comparisons unmatched patients will be excluded):
SITA without concomitant 
insulin treatment
SITA w/o 
insulin
No insulin prescription within 3 
months before/after index date
SECONDARY 
OBJECTIVES
-
Comparison 
for costs
-
Comparison 
for HCRU
SITA without concomitant 
antidiabetic treatment
SITA - mono
No antidiabetic prescription within 3 
months before/after index date is 
observed
SECONDARY 
OBJECTIVES
-
Comparison 
for costs
-
Comparison 
for HCRU
SITA with concomitant 
metformin treatment (only 
dual therapy)
SITA - dual
At least one metformin prescription 
within 3 months before/after index 
date;
No further prescription of other 
antidiabetics within 3 months 
before/after index date is observed
SECONDARY 
OBJECTIVES
-
Comparison 
for costs
-
Comparison 
for HCRU
SITA with concomitant 
insulin treatment
SITA – insulin
At least one insulin prescription 
within 3 months before/after index 
date
SECONDARY 
OBJECTIVES
-
Comparison 
for costs
-
Comparison 
for HCRU
Sub-cohorts for “GLP-1” (for pairwise comparisons unmatched patients will be excluded):
GLP-1 without
concomitant insulin 
treatment
GLP-1 w/o 
insulin
No insulin prescription within 3 
months before/after index date
SECONDARY 
OBJECTIVES

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-
Comparison 
for costs
-
Comparison 
for HCRU
GLP-1 without 
concomitant antidiabetic 
treatment
GLP-1 - mono
No antidiabetic prescription within 3 
months before/after index date is 
observed
SECONDARY 
OBJECTIVES
-
Comparison 
for costs
-
Comparison 
for HCRU
GLP-1 with concomitant 
metformin treatment (only 
dual therapy)
GLP-1 - dual
At least one metformin prescription 
within 3 months before/after index 
date;
No further prescription of other 
antidiabetics within 3 months 
before/after index date is observed
SECONDARY 
OBJECTIVES
-
Comparison 
for costs
-
Comparison 
for HCRU
GLP-1 with concomitant 
insulin treatment
GLP-1 - insulin
At least one insulin prescription 
within 3 months before/after index 
date
SECONDARY 
OBJECTIVES
-
Comparison 
for costs
-
Comparison 
for HCRU

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9.2.3
Study period and follow-up
The inclusion period for all four treatment groups will begin on 01/01/20158. The follow-up 
for the included patients will begin on the day when the first prescription of the above defined 
agents is observed (index date) and continue as the most recent data are available from the 
 database (max. follow-up of 4 years; until 31/12/2018). Sensitivity analyses for patients 
with a minimum follow-up time of 12 and 24 months will be conducted additionally. During 
follow-up, patients will be censored if they die before 31/12/2018, discontinue their initiated 
treatment with Empagliflozin, DPP-4i or GLP-1-RA or initiate a concomitant use of any 
SGLT-2i, DPP-4i or GLP-1-RA (including free or fixed-dose combinations).
In the main analyses, the follow-up will end on the date of the first occurrence of any of the 
following events:
-
death,
-
discontinuation of the initiated study drug (DDD + grace period of 180 days; see 9.3.1),
-
prescription for any of the following antidiabetic therapies, if these were not initially 
used: any SGLT-2 inhibitor, any DPP-4 inhibitor (if Sitagliptin was initiated, any 
further DPP-4i will lead to censoring), any GLP-1-RA,
-
or end of data availability.
In subgroup analysis additional events for censoring will be included, as described in Table 2
Table 2. Censoring criteria for subgroup analysis during the follow-up period
Samples
Censoring criteria (events after index date)
EMPA, DPP-4, SITA and GLP-1 without 
concomitant insulin treatment
none
Sensitivity analysis: Any prescription of 
insulin
EMPA, DPP-4, SITA and GLP-1 without 
concomitant antidiabetic treatment
none
EMPA, DPP-4, SITA and GLP-1 with
concomitant metformin treatment (only 
dual therapy)
none
EMPA, DPP-4, SITA and GLP-1 with
concomitant insulin treatment
none
Sensitivity analysis: Discontinuation of 
insulin (DDD + grace period of 90 days; see 
9.3.1),
                                                
8 01/07/2015 in case of availability of data set 

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9.3
VARIABLES
9.3.1
Exposures: Study Drugs
Main exposures: Empagliflozin
The main exposures in this study are incident use of Empagliflozin (ATC codes: A10BK03, 
formerly A10BX12; A10BD20). The available SGLT-2 inhibitors in Germany to be excluded
in any Empagliflozin sub-cohort are presented in Table 3. Fixed-dose combination drugs (e.g. 
Empagliflozin and metformin) will be included.
Main comparators: any DPP-4 inhibitor, Sitagliptin only and any GLP-1-RA,
Incident use of any DPP-4 inhibitor, Sitagliptin in particular and any GLP-1-RA are the main 
comparators in the analyses. The available DPP-4 inhibitors and GLP-1 receptor agonists in 
Germany are presented in Table 3. Fixed-dose combination drugs with the above defined 
comparators and another drug other than an SGLT-2 inhibitor (main exposure) will be 
included. GLP-1 receptor agonists, DPP-4 inhibitors as well as Sitagliptin specifically will be 
used as the active comparator sub-cohorts because they are used in the treatment of the same 
disease stage of T2DM as SGLT-2 inhibitors [17]. Some DPP-4 inhibitors might increase the 
risk of heart failure, which might cause increased indirect healthcare costs. Therefore, this
study will include the utilization of Sitagliptin in a separate cohort, since it most likely does 
not affect HF risk and consequently not increase indirect, CV event-associated costs.
Concomitant use of any SGLT-2 inhibitor, DPP-4 inhibitor or GLP-1 receptor agonist excluded 
As described in section 9.2.2, the inclusion and exclusion criteria require that patients have no 
previous use of any SGLT-2 inhibitor, any GLP-1-RA or any DPP-4 inhibitor (as listed in 
Table 3, in a free or fixed-dose combination). During follow-up, patients are censored if they 
switch to any SGLT-2 inhibitor, GLP-1-RA or DPP-4 inhibitor or initiate a concomitant use of 
any SGLT-2 inhibitors, GLP-1-RA and DPP-4 inhibitors (free or fixed-dose combinations), as 
described in section 9.2.3. 
Definition of exposure periods
Drug exposure periods will be defined based on available data on prescription and dispensation
data of drugs as provided by 
. Drug exposure will be assumed to begin on the date of a 
dispensation. Patients are assumed to consume prescribed drugs according to the respective 
DDD without stock piling. Once no further drug prescription is observed, a grace period of 90 
days is used after the latest date of dispensation. Discontinuation date is the date when this 90-
day grace period ends.

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Table 3. Study drugs form sub-cohorts. 
Sub-cohorts and study drugs 
ATC code
SGLT-2 inhibitor (including only Empagliflozin fixed-dose combinations with 
other drugs than DPP-4 inhibitor or GLP-1-RA)
Empagliflozin
2014 – 2016: A10BX12
A10BK03
Empagliflozin and metformin
A10BD20
Dapagliflozin (listed for exclusion only)
2013 – 2016: A10BX09
A10BK01
Dapagliflozin and metformin (listed for exclusion 
only)
A10BD15
Any GLP-1 receptor agonist (including fixed-dose combinations with insulin)
Exenatide
2013 – 2016: A10BX04
A10BJ01
Liraglutide
2013 – 2016: A10BX07
A10BJ02
Liraglutide 
and 
Insulin 
degludec
(starting 
01/05/2015, until 01/08/2016)
A10AE56
Lixisenatide (until 01/04/2014)
2014 – 2016: A10BX10
A10BJ03
Dulaglutide (starting 01/02/2015)
2015 – 2016: A10BX14
A10BJ05
Any DPP-4 inhibitor (including fixed-dose combinations of a DPP-4 inhibitor and 
another drug than SGLT-2 inhibitor or GLP-1-RA)
Sitagliptin
A10BH01
Sitagliptin and metformin
A10BD07
Vildagliptin (until 01/07/2014; starting 01/12/2018)
A10BH02
Vildagliptin and metformin (until
01/07/2014; 
starting 01/12/2018)
A10BD08
Saxagliptin
A10BH03
Saxagliptin and metformin
A10BD10
ATC=Anatomical 
Therapeutic 
Chemical; 
DPP-4=dipeptidyl 
peptidase-4; 
SGLT-2=sodium-glucose 
cotransporter-2.
9.3.2
Covariates at baseline (Patient Characteristics)
The types of covariates at baseline (baseline patient characteristics) utilized in this study and 
their measurements periods are listed in Table 4. The complete list of covariates, representing 
each covariate type, and their operational definitions are available in Annex 1. 

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Table 4. Types of covariates at baseline
Covariate1 at baseline
Measurement period
Sociodemographic and lifestyle
characteristics
12 months preceding index date (or Index)
Cardiovascular complications
12 months preceding index date (inclusive)
Other comorbidities (incl. CCI)
12 months preceding index date (inclusive)
Prior use of other antidiabetic drugs
12 months preceding index date (inclusive)
Prior use of CV-related drugs
12 months preceding index date (inclusive)
Healthcare resource utilization covariates
12 months preceding index date (inclusive)
Inpatient and outpatient cost
12 months preceding index date (inclusive)
AD cost
12 months preceding index date (inclusive)
All other drug cost
12 months preceding index date (inclusive)
1 The complete list of covariates, representing each covariate type, and their operational definitions are available 
in Annex 1.
9.3.3
Outcomes
The following Table 5 provide an overview about available variables and/or related endpoints 
that will be analyzed within the study, and how they will be measured based on the claims 
dataset.
Table 5. Primary and secondary outcomes included in this study.
Outcomes corresponding to 
each objective
Variable(s)
Definition
Primary outcome 1: Direct healthcare costs (excluding indirect costs of days absent from work)
Primary outcome 1a: 
All-cause direct healthcare 
costs (excluding indirect costs 
of days absent from work)
Total costs for 
hospitalizations
Total costs based on DRG reimbursement 
codes associated to all hospitalizations with 
any diagnosis ICD-10 code during the post-
index period
Total costs for outpatient 
physician visits
Total costs based on EBM outpatient 
settings9 during the post-index period
Total costs for outpatient 
prescriptions
Costs based on pharmacy sales price at 
prescription date during the post-index period
Total costs for medical 
aids and remedies
Costs based on pharmacy sales price at 
prescription date during the post-index period
Total costs for 
rehabilitation stay
Cost directly extracted from the database 
related to rehabilitation visits with any
diagnosis ICD-10 code during the post-index 
period
                                                
9 The majority of services are not invoiced directly by means of a monetary value but by a system of weighted 
points. To assess the monetary payment in the outpatient setting, weighted points are usually multiplied by a 
uniform orientation value, which is defined by the National Association of Statutory Health Insurance 
Physicians.

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Outcomes corresponding to 
each objective
Variable(s)
Definition
Primary outcome 1b:  
T2DM-associated direct 
healthcare costs (excluding 
indirect costs of days absent 
from work)
Costs for DM-related 
hospitalizations
Associated to main or primary diagnosis 
ICD-10 codes: 
T2DM: E11.-, 
Hypoglycemia: E16.-
Costs for DM-related 
outpatient physician 
visits
Total costs based on EBM outpatient settings 
for outpatient visits associated to primary 
diagnosis ICD-10 codes E11.-, E16.-
documented by a physician with the 
following codes 
(“Facharztgruppenschlüssel”; FGS):
General physician (GP): FGS 1/2/3
Diabetologist: FGS 25
Costs for outpatient 
prescriptions of 
antidiabetic drugs
Costs for antidiabetic drugs (identified by 
ATC/PZN) based on pharmacy sales price at 
prescription date during the post-index period
Costs for DM-related 
medical aids and 
remedies
Costs based on prescribed aids and remedies: 
needles, lancets and strips (identified by 
respective PZNs)
Costs for DM-related 
rehabilitation stays
Associated to main or primary diagnosis 
ICD-10 codes: E11.-, E16.-
Primary outcome 1c:  
CV-associated direct healthcare 
costs (excluding indirect costs 
of days absent from work)
Costs for CV-related 
hospitalizations
Associated to main or primary diagnosis 
ICD-10 codes: 
Heart failure: I11.0, I13.0, I13.2 or I50
Coronary artery disease: I25
Myocardial infarction: I21
Ischemic stroke: I63
Costs for CV-related 
outpatient physician 
visits
Total costs based on EBM outpatient settings 
for outpatient visits associated to primary 
diagnosis ICD-10 codes:
Heart failure: I11.0, I13.0, I13.2 or I50
Coronary artery disease: I25
Myocardial infarction: I21
Ischemic stroke: I63
Costs for outpatient 
prescriptions of CV 
drugs
Costs for CV drugs (identified by ATC/PZN) 
based on pharmacy sales price at prescription 
date during the post-index period
Costs for CV-related 
medical aids and 
remedies
Costs based on prescribed aids and remedies
Costs for CV-related 
rehabilitation stays
Associated to main or primary diagnosis 
ICD-10 codes: 
Heart failure: I11.0, I13.0, I13.2 or I50
Coronary artery disease: I25
Myocardial infarction: I21
Ischemic stroke: I63
Primary outcome 2: Healthcare resource utilization
Primary outcome 2a: 
Total count of 
hospitalizations 
Total number of admissions per observed 
patient year during the post-index period

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Outcomes corresponding to 
each objective
Variable(s)
Definition
All-cause healthcare resource 
utilization
Length of hospital stay 
(LOS)
I.
Average length of stay per admission 
during the post-index period
Total number of days in hospital per 
observed patient year during the post-index 
period
Total count of quarters 
with outpatient GP visits
Outpatient visits documented by a GP 
(quarterly counted)
Total count of quarters 
with outpatient 
diabetologist visits
Outpatient visits documented by a 
diabetologist (quarterly counted)
Total count of quarters 
with outpatient visits of 
other specialist 
physicians
Outpatient visits documented by other 
specialist physicians (quarterly counted)
Total count of days 
absent from work
Based on number of all days absent from 
work during the post-index period
Total count of 
rehabilitation stays
Total number of rehabilitations stays per 
observed patient year during the post-index 
period
Primary outcome 2b: T2DM-
associated healthcare resource
utilization
Count of DM-related 
hospitalizations 
Number of admissions with main or primary 
diagnosis ICD-10 codes: E11.- or E16.- per 
observed patient year during the post-index 
period
Length of DM-related 
hospital stay (LOS)
I.
Average length of stay per admission 
with main or primary diagnosis ICD-
10 codes: E11.- or E16.- during the 
post-index period
II.
Total number of days in hospital with 
main or primary diagnosis ICD-10 
codes: E11.- or E16.- per observed 
patient year during the post-index 
period 
Total count of quarters 
with/without DM-related 
outpatient GP visits
Outpatient visits with main or primary 
diagnosis ICD-10 codes: E11.- or E16.
documented by a GP (quarterly counted)
Total count of quarters 
with/without DM-related 
outpatient diabetologist 
visits
Outpatient visits with main or primary 
diagnosis ICD-10 codes: E11.- or E16.
documented by a diabetologist (quarterly 
counted)
Total count of quarters 
with/without DM-related 
outpatient visits of other 
specialist physicians
Outpatient visits with main or primary 
diagnosis ICD-10 codes: E11.- or E16.
documented by other specialist physicians
(quarterly counted)
Count of DM-related 
days absent from work
Based on number of all days absent from 
work associated to main or primary diagnosis 
ICD-10 codes: E11.- or E16.- during the 
post-index period
Count of DM-related 
rehabilitation stays 
Total number of rehabilitations stays with 
main or primary diagnosis ICD-10 codes: 
E11.- or E16.- per observed patient year 
during the post-index period

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Outcomes corresponding to 
each objective
Variable(s)
Definition
Primary outcome 2c: CV-
associated healthcare resource
utilization
Count of CV-related 
hospitalizations 
Number of admissions with main or primary 
diagnosis ICD-10 codes: E11.- or E16.- per 
observed patient year during the post-index 
period
Length of CV-related 
hospital stay (LOS)
I.
Average length of stay per admission 
during the post-index period with main 
or primary diagnosis ICD-10 codes:

Heart failure: I11.0, I13.0, I13.2 
or I50

Coronary artery disease: I25

Myocardial infarction: I21

Ischemic stroke: I63
II.
Total number of days in hospital per 
observed patient year during the post-
index period with main or primary 
diagnosis ICD-10 codes: 

Heart failure: I11.0, I13.0, I13.2 
or I50

Coronary artery disease: I25

Myocardial infarction: I21

Ischemic stroke: I63
Total count of quarters 
with/without CV-related 
outpatient GP visits
Outpatient visits with main or primary 
diagnosis ICD-10 codes: 

Heart failure: I11.0, I13.0, I13.2 
or I50

Coronary artery disease: I25

Myocardial infarction: I21

Ischemic stroke: I63
documented by a GP (quarterly counted)
Total count of quarters 
with/without CV-related 
outpatient diabetologist 
visits
Outpatient visits with main or primary 
diagnosis ICD-10 codes: 

Heart failure: I11.0, I13.0, I13.2 
or I50

Coronary artery disease: I25

Myocardial infarction: I21

Ischemic stroke: I63
documented by a diabetologist (quarterly 
counted)
Total count of quarters 
with/without CV-related 
outpatient visits of other 
specialist physicians
Outpatient visits with main or primary 
diagnosis ICD-10 codes: 

Heart failure: I11.0, I13.0, I13.2 
or I50

Coronary artery disease: I25

Myocardial infarction: I21

Ischemic stroke: I63
documented by other specialist physicians
(quarterly counted)

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Outcomes corresponding to 
each objective
Variable(s)
Definition
Count of CV-related 
days absent from work
Associated to main or primary diagnosis 
ICD-10 codes:

Heart failure: I11.0, I13.0, I13.2 
or I50

Coronary artery disease: I25

Myocardial infarction: I21

Ischemic stroke: I63
Count of CV-related 
rehabilitation stays 
Total number of rehabilitations stays per 
observed patient year during the post-index 
period with main or primary diagnosis ICD-
10 codes:

Heart failure: I11.0, I13.0, I13.2 
or I50

Coronary artery disease: I25

Myocardial infarction: I21

Ischemic stroke: I63
Secondary outcome 1:  Direct and indirect healthcare costs (including indirect costs of days absent from 
work10)
Secondary outcome 1a: All-
cause (direct and indirect) 
healthcare costs
Total costs for 
hospitalizations
See above (Primary outcome 1a)
Total costs for outpatient 
physician visits
Total costs for outpatient 
prescriptions
Total costs for medical 
aids and remedies
Total costs for 
rehabilitation stay
Indirect costs of days 
absent from work
Based on number of days absent from work 
multiplied with average gross salary
Secondary outcome 1b: T2DM-
associated (direct and indirect) 
healthcare costs
Costs for DM-related 
hospitalizations
See above (Primary outcome 1b)
Costs for DM-related 
outpatient physician 
visits
Costs for outpatient 
prescriptions of 
antidiabetic drugs
Costs for DM-related 
medical aids and 
remedies
Costs for DM-related 
rehabilitation stays
                                                
10 Costs displayed for patients with confirmed status of employment only

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Outcomes corresponding to 
each objective
Variable(s)
Definition
DM-related indirect 
costs of days absent 
from work
Associated to main or primary diagnosis 
ICD-10 codes: E11.-, E16.-
Secondary outcome 1c: CV-
event-associated (direct and 
indirect) healthcare costs
Costs for CV-related 
hospitalizations
See above (Primary outcome 1c)
Costs for CV-related 
outpatient physician 
visits
Costs for outpatient 
prescriptions of CV 
drugs
Costs for CV-related 
medical aids and 
remedies
Costs for CV-related 
rehabilitation stays
CV-related indirect costs 
of days absent from 
work
Associated to main or primary diagnosis 
ICD-10 codes:
Heart failure: I11.0, I13.0, I13.2 or I50
Coronary artery disease: I25
Myocardial infarction: I21
Ischemic stroke: I63
9.3.4
Other variables required for analyses
In addition, the variables listed below will be collected for performing the alternative and 
sensitivity analyses (see section 9.7): 

History of any CV disease in the 12 months prior to the index date (Annex 1)

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9.4
DATA SOURCES
This study will be based on the 
 claims database. Health insurance is 
mandatory in Germany, with approximately 85% of the population (about 70 million persons) 
being covered by German Statutory Health Insurance (SHI). German health insurance 
companies provide nearly full coverage of all healthcare-related services (except for co-
payments such as the costs for over-the-counter medication). Information on patient socio-
demographics as well as diagnoses, procedures, and costs from primary care data, 
pharmaceuticals, hospitalizations, and inability to work are available.
 is a service provider for about 
 German-wide smaller sickness funds. 
The 
dataset contains administrative data for approximately two million insured persons. 
All analyses will be conducted locally in Germany. 
 will involve the 
 
), a university-affiliated 
partner with longstanding experience in conducting claims data analyses. 
 will close a 
contract with 
 (conditional on approval of study protocol), which will define inclusion/ex-
clusion criteria for patient selection, all variables and formats to be used in the data extraction 
process, and the data protection measures needed to be implemented by 
. 
 will 
participate in the steering board of the study and will co-author all future publications of study 
results.

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9.5
STUDY SIZE
Figure 3 shows the sample size required to detect a 5% or 10% difference in the average cost
with 80% power and 5% alpha level. Varying expected differences for mean costs (125-250
Euros in assumed cost average of 2,500 Euros per patient-year) are presented assuming two 
equally numbered groups for comparison.
Figure 3: Minimum sample sizes for a 0.05 significance level and 0.8 power level of a 
two-sample comparison of average costs per patient year
Differences in means 
(assumed cost average; 
per patient year in Euro)
125
(5%)
150
(6%)
175
(7%)
200
(8%)
225
(9%)
250
(10%)
Sample required, per 
cohort (after matching); 
assuming SD = 1,000
1006
699
514
394
312
253
Sample required, per 
cohort (after matching); 
assuming SD = 2,000
4020
2792
2052
1571
1242
1006
SD = Standard Deviation

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For example, with assuming diabetes-associated costs of 2,500 € per patient year (SD = 1,000)
for two equally numbered groups, the analyses are sufficiently powered (>80%) to detect a cost 
difference (effect size) of 250 € (10%) decrease in costs per patient year between these groups
when 253 patients per cohort, i.e., 506 patients in total, can be included.
9.6
DATA MANAGEMENT
The study organization/research organization (
.) will conduct the analysis according 
to all relevant legal requirements (EC data protection guidelines). All data will be checked for 
their validity using the median, arithmetic mean, max, and min for each variable as criteria. 
Patients with any implausible/counter-intuitive data will be excluded from the dataset.
9.7
DATA ANALYSIS
9.7.1
Baseline characteristics / Descriptive statistics
For all included patients, descriptive statistics will be generated separately in each sub-cohort. 
Continuous covariates will be described by the mean, standard deviation (SD), median, 
interquartile range (IQR), minimum and maximum. Categorical covariates and continuous 
covariates that were also categorized will be described by proportion and frequency in each 
category. 
Patient characteristics at cohort entry for the main exposure cohort and the comparator cohort
patients and the standardized difference before and after matching will be presented as in Table 
6 below.

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Table 6. Illustration of baseline characteristics table. Among the main exposure cohorts (Empagliflozin cohort) and the comparator 
cohorts, baseline characteristics variables and their standardized difference (Std.) will be presented before and after matching. 
Before matching
After matching
Main 
exposure 
sub-cohort
Comparator sub-
cohort
Std. 
Main exposure sub-
cohort
Comparator sub-
cohort
Std.
Continuous variable 1 (e.g. age) that has been also categorized
Category 1 (e.g. <39 years) N (%)
n (p.pp)
n (p.pp)
-
n (p.pp)
n (p.pp)
-
Category 2 (e.g. 40-60 years) N (%)
n (p.pp)
n (p.pp)
-
n (p.pp)
n (p.pp)
-
Category 3 (e.g. >60 years) N (%)
n (p.pp)
n (p.pp)
-
n (p.pp)
n (p.pp)
-
Mean (SD)
Median (IQR)
Min, Max
x.xx (x.xx)
x.xx (x.xx)
x.xx, x.xx
x.xx (x.xx)
x.xx (x.xx)
x.xx, x.xx
x.xx
(based 
on 
mean)
x.xx (x.xx)
x.xx (x.xx)
x.xx, x.xx
x.xx (x.xx)
x.xx (x.xx)
x.xx, x.xx
x.xx
(based on mean)
Categorical variable 1 (e.g. sex)
Category 1 (e.g. male) N (%)
n (p.pp)
n (p.pp)
x.xx
n (p.pp)
n (p.pp)
x.xx
Category 2 (e.g. female) N (%)
n (p.pp)
n (p.pp)
-
n (p.pp)
n (p.pp)
-
Continuous variable 2 that has not been categorized
Mean (SD)
Median (IQR)
Min, Max
x.xx (x.xx)
x.xx (x.xx)
x.xx, x.xx
x.xx (x.xx)
x.xx (x.xx)
x.xx, x.xx
x.xx
(based 
on 
mean)
x.xx (x.xx)
x.xx (x.xx)
x.xx, x.xx
x.xx (x.xx)
x.xx (x.xx)
x.xx, x.xx
x.xx
(based on mean)
Std.=standardized difference; SD=standard deviations; Q1=first quartile; Q3=third quartile.

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9.7.2
Propensity score methodology
As mentioned above, a propensity-score matching (PSM) is planned to answer the primary 
analysis question. When comparing individuals receiving different treatments, the question of 
comparability due to systematic differences across treatment groups needs to be addressed. 
PSM is a common approach to adjust for patients’ characteristics. The idea is to combine 
several individual characteristics that are relevant to treatment assignment into one item, the 
propensity score, which measures the conditional probability that the patient is assigned to a 
treatment rather than the alternative one. Patients with the same propensity score have 
theoretically the same probabilistic distribution over other covariates, independently of the 
treatment they received. Therefore, outcomes of patients with similar propensity scores can be 
compared to determine the treatment effect on each outcome. Based on the estimated 
propensity scores, patients initiating Empagliflozin will be matched to respective comparator 
cohorts [i) DPP-4; ii) SITA; iii) GLP-1 as well as sub-cohorts as described in Table 1] using a 
nearest-neighbor 1:1 matching algorithm with maximum caliper of 0.001. While a limited 
number of studies implemented a 1: n matching procedure [33], most studies in the literature 
use 1:1 matching [6, 16, 30, 34] which reduces the estimation bias. In practice, propensity 
scores will be estimated using logistic regression models, where the dependent variable is a 
binary indicator of group affiliation (Empagliflozin versus respective comparator). Covariates 
in the logistic regression should include any relevant patient characteristic or condition which 
is relevant to treatment assignment. This will include following variables, which refer to the 
index date or the related 12-month pre-index period:
-
Age in years,
-
Gender (female = 1; male = 0),
-
Charlson Comorbidity Index (CCI),
-
Top 10 comorbidities that are not covered by the CCI,
-
Kidney disease (by stage)
o
Stage 1 (Y/N)
o
Stage 2 (Y/N)
o
Stage 3 (Y/N)
o
Unspecified (Y/N)
-
Level of care,
-
Antidiabetic treatment
o
insulin (Y/N)
o
metformin (Y/N)
o
sulfonylureas (Y/N)
o
other antidiabetic drugs except study drugs (e.g. thiazolidinediones; Y/N)
o
Number of antidiabetic drugs (prescribed in parallel)
-
CV-related therapies:
o
Anticoagulant/ Platelet aggregation inhibition (Y/N)
o
RAAS inhibitors(Y/N)
o
Beta-blocking agents(Y/N)
o
Calcium-Channel blockers (Y/N)
o
Lipid modifying agents (Y/N)
-
T2DM-related costs (cost per observed patient year)
o
Inpatient costs
o
Outpatient costs

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o
Medication costs
o
Rehabilitation
o
Cost of remedies / aids
-
Non-T2DM-related costs (all-cause costs minus T2DM-related costs)
o
Inpatient costs
o
Outpatient costs
o
Medication costs
o
Rehabilitation
o
Cost of remedies / aids
-
Number of days absent from work
o
T2DM-related
o
not T2DM-related
Each covariate included in the PSM analysis is expected to affect both treatment assignment 
and the outcomes of interest. PSM quality will be assessed in two ways:
1) Standardized differences (STD) between comparison groups will be used to assess the 
balance of covariates after matching (number of variables with – still – significant 
differences (> 0.1) between Empagliflozin and comparators). Table 7 lists the 
statistics that will be separately reported for cost and HCRU variables in each group 
comparison.
2) We will compare the propensity score distributions before and after matching as 
illustrated in Figure 4.
Table 7. Statistics reported for HCRU / cost variables
Variable name
Statistics
All-cause, T2DM-related and CV-related HCRU
All-cause hospitalizations and 
hospitalizations related to T2DM 

Number (%) of patients with at least 1 hospitalization 

Number of hospitalizations per PY: Mean, SD, Median 

Number of hospitalization days per PY: Mean, SD, 
Median 
Outpatient GP visits (any visit, related 
to T2DM and related CV diseases as 
described in Table 5)

Number (%) of patients with at least 1 visit

Number of visits per PY: Mean, SD, Median
Outpatient diabetologist visits (any 
visit, related to T2DM and related CV 
diseases as described in Table 5)

Number (%) of patients with at least 1 visit

Number of visits per PY: Mean, SD, Median
Rehabilitation stays (any visit, related 
to T2DM and related CV diseases as 
described in Table 5)

Number (%) of patients with at least 1 rehabilitation stay

Number of rehabilitation days per PY: Mean, SD, Median 
All-cause days absent from work, 
related to T2DM and related CV 
diseases as described in Table 5

Number (%) of patients with at least 1 absence

Number of days per PY: Mean, SD, Median 
Top-10 Aids and remedies 

Number (%) of patients with at least 1 aid or remedy

Number of aids or remedies per PY: Mean, SD, Median

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Variable name
Statistics
All-cause, T2DM-related and CV-related costs
Costs for hospitalizations 

Mean, SD, Median per PY
Costs for outpatient physician visits 
Costs for outpatient prescriptions 
Costs for rehabilitation stays
Costs for medical aids and remedies 
Indirect costs for days absent from 
work 

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Figure 4. Illustration of propensity score distribution among the main exposure cohort 
(“EMPA”) and among the comparator cohorts (e.g. “SITA”) before and after matching.
9.7.3
Primary outcomes
For the primary outcomes, average direct cost in Euros per observed patient year with 
corresponding standard deviations (SD) will be calculated, separately for each pairwise 
comparison of treatments and subgroups (see Table 1 for subsample definitions). The mean 
difference of the cost parameters between the matched samples will be calculated and reported 
as outlined in Table 8.
Table 8. Reporting of average cost per observed patient year
Type of cost
EMPA 
matched to SITA
SITA
matched to EMPA
Effect size
Cohort size
(n = XXX)
(n = XXX)
Outpatient care
Mean (SD)
Mean (SD)
difference of means (p-value)
Inpatient care
Mean (SD)
Mean (SD)
difference of means (p-value)
Pharmaceuticals/Outpatient 
prescriptions
Mean (SD)
Mean (SD)
difference of means (p-value)
Rehabilitation
Mean (SD)
Mean (SD)
difference of means (p-value)
Medical aids and remedies
Mean (SD)
Mean (SD)
difference of means (p-value)
Total
Mean (SD)
Mean (SD)
difference of means (p-value)

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9.7.4
Secondary outcomes 
For secondary cost outcomes, indirect costs of days absent from work11 will be added to direct 
costs analyzed in the respective primary outcomes (separately for all-cause and T2DM-related 
costs) (section 9.7.3). The analysis of secondary HCRU outcomes will be conducted in a similar 
way as the analysis of primary outcomes. The analysis of HCRU outcomes will include the 
description of the number of outcome events. The mean difference of the HCRU parameters 
between the matched samples will be calculated and reported as outlined in Table 9.
Table 9. Reporting of mean healthcare resource utilization per patient year
Type of resource utilization
EMPA matched 
to SITA
SITA matched to 
EMPA
Effect size
(n = XXX)
(n = XXX)
Number of outpatient visits
Mean (SD)
Mean (SD)
difference of means (p-value)
Number of hospitalizations
Mean (SD)
Mean (SD)
difference of means (p-value)
Number of days in hospital 
Mean (SD)
Mean (SD)
difference of means (p-value)
Days absent from work (sick leave)
Mean (SD)
Mean (SD)
difference of means (p-value)
Days of rehabilitation
Mean (SD)
Mean (SD)
difference of means (p-value)
9.7.5
Sensitivity analyses
The core analyses for the primary outcome, as described in section 9.7.3, will be re-performed 
for patients with a minimum follow-up time of 12 and 24 months post-index, respectively.
Furthermore, patients with history of any cardiovascular (CV) disease (see Annex 1) in the 12 
months prior to the index date will be excluded in addition. Since applying these criteria will 
change the study population, the PS matching and balance analyses will also be re-performed.
In case that relevant differences in the medication costs and/or HCRU between Empagliflozin
and GLP-1-RA would be identified, separate considerations of Dulaglutide and Liraglutide will 
be carried out in the context of a sensitivity analysis with respect to the primary outcomes.
                                                
11 Costs displayed for patients with confirmed status of employment only

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9.8
QUALITY CONTROL
The study will be conducted as specified in this protocol. The principal investigator, the co-
investigators and the sponsors of the study must approve all revisions to the protocol. All 
changes must be documented as protocol amendments. 
The study team must maintain adequate and accurate records to enable the conduct of the study 
to be fully documented, including but not limited to the protocol and protocol amendments, 
and documentation of IRB and Health Authority approval/notification (if applicable). The 
study team ensures that the datasets and statistical programs used for generating the data 
included in the final research report are kept in electronic format and are available for auditing 
and inspection. Records and documents pertaining to the conduct of this activity must be 
retained for at least 15 years after completion of the activity, or for the length of time required 
by relevant national or local health authorities, whichever is longer. After that period, the 
documents may be destroyed, subject to local regulations. No records may be disposed of 
without the written approval of the research initiator.
This study protocol has been written following the Code of Conduct by the European Network 
of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP) [23]. The protocol 
also follows the key elements of the Guideline for Good Pharmacoepidemiology Practices by 
International Society for Pharmacoepidemiology. TFS Trial Form Support, Quantify Research, 
the principal investigator, co-investigators, the Sponsors and individuals acting on their behalf 
commit to adhere to the rules of the ENCePP Code of Conduct in their entirety.

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9.9
LIMITATIONS OF THE RESEARCH METHODS
Claims data are extremely valuable for the effective examination of health care outcomes, 
treatment patterns, health care resource utilization, and costs. Validity of recording and coding 
can be evaluated as high in these databases. This is especially the case for any inpatient and 
prescription data as this data is directly relevant for reimbursement of hospitals/pharmacies by 
sickness funds. The strengths of a claims data analysis are the use of a large database and the 
elimination of a patient/study site bias. However, all claims databases have certain inherent 
limitations because the claims are collected for the purpose of payment and not research:
9.9.1
Data sources and variables
Information available in claims data is limited to parameters which are generally relevant for 
reimbursement purposes and thus, the following variables will not be available: duration since 
first T2DM diagnosis, specific prescribed daily dosages, reasons for prescribing/not 
prescribing specific agents, laboratory values such as HbA1c
9.9.2
Identifying patients with T2DM
The presence of a diagnosis code on a medical claim is not positive presence of disease, as the 
diagnosis code may be incorrectly coded (i.e. wrong ICD-10 code) or included as rule-out 
criteria rather than actual disease (i.e. wrong extension of ICD-10 code: “a” for “ausgeschlos-
sen” (excluded diagnosis) vs. “g” for “gesichert” (secured diagnosis). In addition, the beginning 
of T2DM symptoms cannot be determined in this study setting. Therefore, the duration of the 
disease cannot be determined. 
9.9.3
Drug exposure
A claim for a filled prescription is not an indication the medication was consumed or taken as 
prescribed, and the available follow-up data will be limited to up to four years. Furthermore, 
physician-provided samples, samples taken as part of a clinical trial or over-the-counter 
medications will not be observed in claims data.
9.9.4
Bias and confounding
Missing data, e.g. only observing patient characteristics for a subset of the population, could 
introduce bias into the study if the data are systematically missing for selected samples. 
However, health care claims are generally required to contain the relevant pieces of information 
in order for the claim to be processed. All medical claims are required to have a diagnosis code, 
and all pharmacy claims will have a corresponding National Drug Code (German PZN) code. 
Thus, there will be residual confounding related to, for example, unobservable differences in 
patient characteristics, which may influence the findings of this study. However, these 
differences are expected to be reflective of the populations outside of the controlled setting of 
a clinical trial.
9.9.5
Analyses
This will be an observational study and therefore, the results will indicate a correlation between 
drug use and selected outcomes. Causality cannot be determined in observational studies.

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9.10
OTHER ASPECTS
None specified. 
9.11
SUBJECTS
Not applicable as this is a claims data study. 
9.11.1
Cases
Not applicable as this is a claims data study. 
9.11.2
Controls
Not applicable as this is a claims data study. 
9.12
BIAS
This study will include new users of Empagliflozin, any GLP-1 receptor agonist, and any DPP-
4 inhibitor. Immortal time bias [24] will be minimal as the follow-up will begin at the initiation 
of drug use. Adherence to drug use will be not specified. Potential bias from exposure 
misclassification will be addressed with grace periods. 
Comparing the outcomes between new users of Empagliflozin and any DPP-4 inhibitor or 
GLP-1 receptor agonist will decrease confounding by indication, as patients in the 
Empagliflozin cohorts and patients in the comparison cohorts will have a similar indication to 
drug use [16]. Another approach of decreasing confounding by indication is a restriction of the 
study sample only to T2DM patients without additionally documented T1DM diagnosis.  
The compared cohorts will be matched on the basis of propensity-scores (PS). The PS will be 
computed on the basis of a wide range of covariates related to sociodemographic charac-
teristics, lifestyle, diabetes complications, other comorbidities and prior/concomitant use of 
other antidiabetic drugs. The PS matching will decrease the systematic differences between the 
cohorts and, thus, decrease confounding. 

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10.
PROTECTION OF HUMAN SUBJECTS
Per design, this non-interventional study utilizes secondary data. Thus, the study does not affect 
the treatment or health outcomes of the study individuals. The study individuals will not be 
contacted in any phase of the study.

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11.
MANAGEMENT 
AND 
REPORTING 
OF 
ADVERSE 
EVENTS/ADVERSE REACTIONS
Not applicable. As stated in section VI.C.1.2.1. of BI GVP Module VI, adverse-reactions 
reporting is not required for non-interventional study designs which are based on secondary 
use of data.
There is no potential that any employee of BI or agent working on behalf of BI will access 
individual patient data in which the patient may be identified during data compilation, data 
reporting or data analysis.

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12.
PLANS FOR DISSEMINATING AND COMMUNICATING 
STUDY RESULTS
Manuscripts describing this work will be submitted for publication in peer-review journals. 
Results may also be submitted for presentation at scientific conferences.

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13.
REFERENCES
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2 
Heidemann C, Scheidt-Nave C. Prevalence, incidence and mortality of diabetes mellitus 
in adults in Germany – A review in the framework of the Diabetes Surveillance. Journal 
of Health Monitoring 2017;2:98–121.
3 
Pfeiffer AFH, Klein HH. The Treatment of Type 2 Diabetes. Dtsch Arztebl Int
2014;111:69–82. doi:10.3238/arztebl.2014.0069
4 
Einarson TR, Acs A, Ludwig C, et al. Prevalence of cardiovascular disease in type 2 
diabetes: a systematic literature review of scientific evidence from across the world in 
2007-2017. Cardiovasc Diabetol 2018;17:83. doi:10.1186/s12933-018-0728-6
5 
Kang YM, Kim Y-J, Park J-Y, et al. Mortality and causes of death in a national sample of 
type 2 diabetic patients in Korea from 2002 to 2013. Cardiovascular Diabetology
2016;15:131. doi:10.1186/s12933-016-0451-0
6 
Einarson TR, Acs A, Ludwig C, et al. Economic Burden of Cardiovascular Disease in 
Type 2 Diabetes: A Systematic Review. Value Health;21:881–90. 
doi:10.1016/j.jval.2017.12.019
7 
Hsia DS, Grove O, Cefalu WT. An update on sodium-glucose co-transporter-2 inhibitors 
for the treatment of diabetes mellitus. Curr Opin Endocrinol Diabetes Obes 2017;24:73–
9. doi:10.1097/MED.0000000000000311
8 
International Diabetes Federation. IDF Clinical Practice Recommendations for managing 
Type 2 Diabetes in Primary Care. 
2017.https://www.idf.org/component/attachments/attachments.html?id=1270&task=down
load (accessed 25 Sep 2018).
9 
Scheen AJ. Cardiovascular Effects of New Oral Glucose-Lowering Agents: DPP-4 and 
SGLT-2 Inhibitors. Circ Res 2018;122:1439–59. 
doi:10.1161/CIRCRESAHA.117.311588
10 Persson F, Nyström T, Jørgensen ME, et al. Dapagliflozin is associated with lower risk of 
cardiovascular events and all-cause mortality in people with type 2 diabetes (CVD-REAL 
Nordic) when compared with dipeptidyl peptidase-4 inhibitor therapy: A multinational 
observational study. Diabetes Obes Metab 2018;20:344–51. doi:10.1111/dom.13077
11 Kosiborod M, Cavender MA, Fu AZ, et al. Lower Risk of Heart Failure and Death in 
Patients Initiated on Sodium-Glucose Cotransporter-2 Inhibitors Versus Other Glucose-
Lowering Drugs: The CVD-REAL Study (Comparative Effectiveness of Cardiovascular 
Outcomes in New Users of Sodium-Glucose Cotransporter-2 Inhibitors). Circulation
2017;136:249–59. doi:10.1161/CIRCULATIONAHA.117.029190
12 Kosiborod M, Lam CSP, Kohsaka S, et al. Cardiovascular Events Associated With 
SGLT-2 Inhibitors Versus Other Glucose-Lowering Drugs: The CVD-REAL 2 Study. J 
Am Coll Cardiol 2018;71:2628–39. doi:10.1016/j.jacc.2018.03.009
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inhibitors versus other glucose-lowering drugs (CVD-REAL Nordic): a multinational 
observational analysis. Lancet Diabetes Endocrinol 2017;5:709–17. doi:10.1016/S2213-
8587(17)30258-9
14 Patorno E, Pawar A, Franklin J, et al. Empagliflozin And The Risk Of Heart Failure 
Hospitalization In Routine Clinical Care: A First Analysis From The Empagliflozin
Comparative Effectiveness And Safety (EMPRISE) Study. Chicago, Illinois: : American 
Heart Association 
15 Psaty, BM, Koepsell, TD, Lin, D, et al. Assessment and Control for Confounding by 
Indication in Observational Studies. J Am Geriatr Soc;47:749–54.
16 Lund JL, Richardson DB, Stürmer T. The active comparator, new user study design in 
pharmacoepidemiology: historical foundations and contemporary application. Curr 
Epidemiol Rep 2015;2:221–8. doi:10.1007/s40471-015-0053-5
17 Association AD. Pharmacologic Approaches to Glycemic Treatment: Standards of 
Medical Care in Diabetes—2018. Diabetes Care 2018;41:S73–85. doi:10.2337/dc18-
S008
18 Kidney International Supplements | KDIGO 2012 Clinical Practice Guideline for the 
Evaluation and Management of Chronic Kidney Disease | ScienceDirect.com. 
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(accessed 13 Mar 2019).
19 Zinman B, Wanner C, Lachin JM, et al. Empagliflozin, Cardiovascular Outcomes, and 
Mortality in Type 2 Diabetes. New England Journal of Medicine 2015;373:2117–28. 
doi:10.1056/NEJMoa1504720
20 Wang SV, Schneeweiss S, Rassen JA. Optimal Matching Ratios in Drug Safety 
Surveillance: Epidemiology 2014;25:772–3. doi:10.1097/EDE.0000000000000148
21 Austin PC. Assessing balance in measured baseline covariates when using many-to-one 
matching on the propensity-score. Pharmacoepidemiology and Drug Safety
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Annex 1. DEFINITIONS FOR COVARIATES AT BASELINE
Covariate
Definition (when applicable)
ICD-10-GM / OPS-2019
Sociodemographic characteristics (at index date)
Age 
By year
Sex 
female, male
Covariates related to lifestyle (within 12 months before index date) 
Smoking 
–
F17, Z72.0, T65.2
Alcohol abuse or dependence
–
F10, K70, G62.1, I42.6, Z71.4
Drug abuse or dependence 
–
F11; F12, F13, F14, F15, F16, 
F18, F19, Z71.5
Comorbidities (12 months preceding index date)
CCI
See Annex 2
See Annex 2
Major cardiovascular disease
Combined endpoint of diagnosis 
with myocardial infarction, heart 
failure, coronary artery disease or 
ischemic stroke

I11.0

I13.0 or  I13.2

I21

I25

I50

I63
Kidney disease (acute/chronic)
Based on documented ICD-10 
diagnoses, separated by stage 1-3

N17

N18

N19
Sepsis
Based on the definition for the 80 
most cost-intensive diseases
(HMG) by the German Federal 
Insurance Office

A02.1

A20.7

A22.7

A26.7

A32.7

A39.1

A39.2

A39.3

A39.4

A40.0

A40.1

A40.2

A40.3

A40.8

A40.9

A41.0

A41.1

A41.2

A41.3

A41.4

A41.5

A41.51

A41.52

A41.58

A41.8

A41.9

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
A42.7

B00.7

B37.7

O88.3

P36.0

P36.1

P36.2

P36.3

P36.4

P36.5

P36.8

P36.9

R57.2

R65.0

R65.1

R65.2

R65.3

R65.9
HIV / AIDS
Based on the definition for the 80 
most cost-intensive diseases 
(HMG) by the German Federal 
Insurance Office

B20

B21

B22

B23.0

B23.8

B24

O98.7

U60.1

U60.2

U60.3

U60.9

U61.1

U61.2

U61.3

U61.9

U85

Z21
Opportunistic infections 
(caused by pathogens)
Based on the definition for the 80 
most cost-intensive diseases 
(HMG) by the German Federal 
Insurance Office

A31.0

A81.2

B25.0

B25.1

B25.2

B25.8

B25.80

B25.88

B25.9

B37.1

B37.81

B42.0

B44.0

B44.1

B44.2

B44.7

B44.8

B44.9

B45.0

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
B45.1

B45.3

B45.7

B45.8

B45.9

B46.0

B46.1

B46.2

B46.4

B46.5

B46.8

B59

U83
Acquired hemolytic anemia
Based on the definition for the 80 
most cost-intensive diseases 
(HMG) by the German Federal 
Insurance Office

D59.0

D59.1

D59.2

D59.3

D59.4

D59.5

D59.6

D59.8

D59.9

D63.0

D63.8
Aplastic or sideroblastic 
anemia
Based on the definition for the 80 
most cost-intensive diseases 
(HMG) by the German Federal 
Insurance Office

D60.0

D60.1

D60.8

D60.9

D61.0

D61.1

D61.10

D61.18

D61.19

D61.2

D61.3

D61.8

D61.9

D64.0

D64.1

D64.2

D64.3
Prior/concomitant use of other antidiabetic drugs (12 months preceding index date and at index date)
Covariate
Definition (when applicable)
ATC
Previous diabetes-related drug 
treatment (documented 
antidiabetic prescriptions 
including ATC/PZN codes in 
the 12-months pre-index 
period, including prescribed 
defined daily dosages (DDDs) 
per observed patient year),
Use of insulin, metformin, 
sulfonylureas and other 
antidiabetics (e.g. 
thiazolidinediones) as identified by 
respective ATC codes
Number of antidiabetic drugs 
(prescribed in parallel)

A10A-, insulins

A10BA-, MET

A10BB-, SU

A10B-, other AD;

A10X-, other drugs used 
in DM

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AD=Antidiabetics; ATC=Anatomical Therapeutic Chemical;
CABG=coronary artery bypass grafting;
CHF=congestive heart failure; CKD=chronic kidney disease; COPD=chronic obstructive pulmonary disease;
CV=cardiovascular; DM=diabetes mellitus; ICD-10=International Classification of Diseases, 10th revision; 
MET=metformin; MI=myocardial infarction; PTCA=percutaneous transluminal coronary angioplasty; 
SU=sulfonylureas.
Treatment cost (12 months preceding index date
Covariate
Definition (when applicable)
ATC/ ICD-10
Inpatient cost 
Associated to main or primary 
diagnosis ICD-10 codes:

any visit

related to T2DM 

related CV 

Any ICD-10 code

E11 oder E16

I11.0, I13.0, I13.2, I21, 
I25, I50 or I63
Outpatient cost 
Total costs based on EBM outpatient 
settings for outpatient visits 
associated to primary diagnosis ICD-
10 codes related to

any visit 

T2DM 

CV

Any ICD-10 code

E11 oder E16

I11.0, I13.0, I13.2, I21, 
I25, I50 or I63
Rehabilitation cost 
Associated to main or primary 
diagnosis ICD-10 codes:

any visit 

related to T2DM 

related CV 

Any ICD-10 code

E11 oder E16

I11.0, I13.0, I13.2, I21, 
I25, I50 or I63
Cost of remedies / aids
Costs based on prescribed aids and 
remedies: 
TD2M related: needles, lancets and 
strips (identified by respective PZNs)
-/-
Medication cost
AD cost
Medication costs of prescribed 
antidiabetic drug based on list prices
A10
CV drug cost
Medication costs based on 
pharmaceutical list prices for the 
following therapies:

Antithrombotic agents 

Beta blocking agents

Calcium channel blockers

RAAS inhibitors

lipid modifying agents

B01

C07

C08

C09

C10
All other drug 
cost
Any other agent
Any ATC except the ones listed 
above

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Annex 2. CHARLSON-COMORBIDITY-SCORE (CCI) AND
ITS
COMPONENTS (EXCL: AGE FACTOR)
COMORBIDITY
ICD-10 CODE
CHARLSON 
SCORE
Coronary artery disease
I20-, I21-, I22-, I23-, I24-, I25-
1
Congestive heart failure
I11-, I50-
1
Peripheral vascular disease
I73-, I74-, I77-
1
Cerebrovascular disease
G45-, G46-, I6-
1
Dementia
F00-, F01-, F02-, F03-, G30-
1
Chronic pulmonary disease
J4-, J6- w/o J67-, J68-, J69-
1
Connective tissue disorder
M05-, M06-, M07-, M08-, M3-
1
Peptic ulcer disease
K25-, K26-, K27-, K28-
1
Mild liver disease
B18-, K70-, K73-, K75-
1
Diabetes mellitus without complications
E109-, E119-, E129-, E139-, E149-
1
Hemiplegia
G81-, G82-
2
Moderate or severe renal disease
N17-, N18-, N19-
2
Diabetes mellitus with end-organ damage
E10-, E11-, E12-, E13-, E14-
2
Tumor without metastases, leukemia, 
lymphoma, multiple myeloma
C-, w/o metastatic solid tumor
[C77-, C78-, C79-, C80-]
2
Moderate or severe liver disease
K72-, K74-, I85-
3
Metastatic solid tumor
C77-, C78-, C79-, C80-
6
AIDS
B20-, B21-, B22-, B23-, B24-
6