Characteristics of IPF Patients Initiating Nintedanib, Pirfenidone or no Antifibrotic Treatment in the US
Conditions: Idiopathic Pulmonary Fibrosis
Sponsor: Boehringer Ingelheim

 
 
 
 
 
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Protocol for non-interventional studies based on existing data 
TITLE PAGE 
Note: This template complies with the European Medicines Agency requirements on format, layout and content 
of a Post Authorisation safety Study (PASS) protocol of 26 Sep 2012. Please do not change structure provided in 
this template. 
 
Document Number: 
 
BI Study Number: 
1199-0375 
BI Investigational 
Product(s): 
Not applicable (N/A) 
Title: 
Characteristics of IPF patients initiating nintedanib, pirfenidone or 
no antifibrotic treatment in the US  
Protocol version 
identifier: 
1.0 
Date of last version of 
protocol: 
 
PASS: 
No 
EU PAS register 
number: 
N/A 
Active substance: 
Nintedanib 
Medicinal product: 
Ofev (nintedanib)  
Product reference: 
N/A 
Procedure number: 
N/A 
Joint PASS: 
N/A 
Research question and 
objectives: 
To understand differences in characteristics of Idiopathic 
Pulmonary Fibrosis (IPF) patients who are prescribed nintedanib 
compared to those who are prescribed pirfenidone, and how 
these patients compare to patients who do not receive a 
prescription for antifibrotic treatment 
Country(-ies) of study: 
United States 
Author: 

Boehringer Ingelheim 
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holder(s): 
N/A 
MAH contact person: 
 
In case of PASS, add: 
<EU-QPPV:> 
N/A 
In case of PASS, add: 
<Signature of EU-
QPPV:> 
N/A 
Date: 
18 December 2018 
 
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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 ....................................................................... 9 
6. 
MILESTONES ..................................................................................................... 10 
7. 
RATIONALE AND BACKGROUND ................................................................ 11 
8. 
RESEARCH QUESTION AND OBJECTIVES ................................................. 13 
9. 
RESEARCH METHODS .................................................................................... 14 
9.1 
STUDY DESIGN ............................................................................................ 14 
9.2 
SETTING ........................................................................................................ 14 
9.3 
VARIABLES .................................................................................................. 17 
9.3.1 
Exposures ................................................................................................ 17 
9.3.2 
Outcomes ................................................................................................. 17 
9.3.2.1 Primary outcomes .................................................................................. 17 
9.3.2.2 Secondary outcomes .............................................................................. 18 
9.3.2.3 Further outcomes ................................................................................... 18 
9.3.3 
Covariates ................................................................................................ 18 
9.4 
DATA SOURCES ........................................................................................... 26 
9.5 
STUDY SIZE .................................................................................................. 28 
9.6 
DATA MANAGEMENT ................................................................................ 29 
9.7 
DATA ANALYSIS ......................................................................................... 29 
9.7.1 
Main analysis ........................................................................................... 29 
.................................................................................... 31 
9.8 
QUALITY CONTROL ................................................................................... 31 
9.9 
LIMITATIONS OF THE RESEARCH METHODS ...................................... 31 
9.10 
OTHER ASPECTS ......................................................................................... 32 
9.11 
SUBJECTS ...................................................................................................... 32 
9.11.1 
Cases ........................................................................................................ 32 
9.11.2 
Controls ................................................................................................... 32 
9.12 
BIAS ................................................................................................................ 32 

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10. 
PROTECTION OF HUMAN SUBJECTS .......................................................... 33 
11. 
MANAGEMENT AND REPORTING OF ADVERSE EVENTS/ADVERSE 
REACTIONS ....................................................................................................... 34 
12. 
PLANS FOR DISSEMINATING AND COMMUNICATING STUDY 
RESULTS ............................................................................................................ 35 
13. 
REFERENCES .................................................................................................... 36 
13.1 
PUBLISHED REFERENCES ......................................................................... 36 
13.2 
UNPUBLISHED REFERENCES ................................................................... 37 
ANNEX 1. LIST OF STAND-ALONE DOCUMENTS .............................................. 38 
ANNEX 2. ENCEPP ChECKLIST FOR STUDY PROTOCOLS ............................... 39 
ANNEX 3. ADDITIONAL INFORMATION .............................................................. 40 
 
 

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2. 
LIST OF ABBREVIATIONS 
ACR 
Albumin to creatinine ratio  
ALP 
Alkaline phosphatase  
ALT 
Alanine transaminase 
APTT 
Activated partial thromboplastin time  
ASD 
Absolute standardized differences 
AST 
Aspartate transaminase 
BMI 
Body mass index 
BP 
Blood pressure 
CCI 
Charlson comorbidity index  
CI 
Confidence interval 
COPD 
Chronic obstructive pulmonary disease 
DCCO 
Diffusing capacity of carbon monoxide  
EMR 
Electronic medical records 
ER 
Emergency room 
FEV1 
Forced expiratory volume in one second 
FVC 
Forced vital capacity 
GERD 
Gastroesophageal reflux disease 
GFR 
Glomerular filtration rate  
GGT 
Gamma-glutamyl transpeptidase  
HDL 
High-density lipoprotein  
HIPPA 
Health Insurance Portability and Accountability Act  
HRCT 
High resolution computed tomography 
ICD-9-CM  
The International Classification of Diseases, Ninth Revision, Clinical Modification 
ICD-10-CM  The International Classification of Diseases, Tenth Revision, Clinical Modification 
ILD 
Interstitial lung disease  
IPF 
Idiopathic pulmonary fibrosis 
IRB 
Institutional Review Board  
LDL 
Low-density lipoprotein  
OTC 
Over-the-counter 
PFT 
Pulmonary function test 
PS 
Propensity score 
PT 
Prothrombin time  
ROC 
Receiver operating characteristic 
SD 
Standard deviation 
SLB 
Surgical lung biopsy  
TLC 
Total lung capacity  

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3. 
RESPONSIBLE PARTIES 
Principal investigator: 
 
Other investigators: 
 

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4. 
ABSTRACT 
Name of company: 
 
 
Boehringer Ingelheim (BI) 
Name of finished medicinal 
product:  
Ofev (BI)  
 
Name of active ingredient: 
Nintedanib  
 
Protocol date: 
Study 
number: 
Version/Revision: 
Version/Revision 
date: 
18 December 2018 
 
1.0 
18 December 2018 
Title of study: 
Characteristics of IPF patients initiating nintedanib, pirfenidone or no 
antifibrotic treatment in the US 
Rationale and 
background: 
Idiopathic Pulmonary Fibrosis (IPF) is a debilitating, life limiting 
condition with a median survival range of 2.5 to 3.5 years from the 
time of diagnosis and an estimated incidence of 6.8-16.3 per 100,000 
persons. Despite its rarity, the economic burden of IPF is significant, 
with the annual medical cost to the US healthcare system estimated at 
close to $2 billion in 2012. The goals of treatment for IPF are to stop 
disease progression, reduce symptoms, prevent acute exacerbations, 
and prolong survival. The treatment options for IPF were limited until 
the FDA approved two products in October 2014, Ofev (nintedanib, 
BI) and Esbriet (pirfenidone, Genentech), that slowed disease 
progression. BI is interested in conducting research to understand 
differences in characteristics of patients who are prescribed these two 
drugs, and those who do not receive a prescription for an antifibrotic 
treatment. 
Research question 
and objectives: 
To understand characteristics of patients treated with nintedanib and 
pirfenidone, as well as patients who do not receive a prescription for an 
antifibrotic treatment.  
The primary objective is: 
1. To describe and compare demographic, clinical, and other 
characteristics of IPF patients initiating nintedanib, 
pirfenidone, or not receiving prescription antifibrotic treatment 
The secondary objective is:  
2. To compare and contrast the probability of receiving 
nintedanib vs. pirfenidone and nintedanib or pirfenidone vs. 
untreated in IPF patients using baseline patient characteristics  
Study design: 
A retrospective cohort database study 
 
Population: 
This study will use data from 
GE Centricity electronic medical 
records (EMR) database obtained from a national network of primary 
care practices in the US. The study population will include patients (≥ 
40 years old) with ≥ 1 diagnosis of IPF in EMR records and newly 
prescribed nintedanib or pirfenidone or patients with no prescription for 

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antifibrotic treatment between October 1, 2014 and April 30, 2018. 
Patients will be divided into three mutually exclusive cohorts: (1) 
nintedanib initiators; (2) pirfenidone initiators; and (3) control cohort 
who received no prescription antifibrotic treatment during selection 
window.    
Variables: 
The following demographic and clinical characteristics as well as 
medical interventions for IPF during the 12-month baseline period 
(pre-treatment) will be evaluated and compared between cohorts 
 
Demographic characteristics (e.g., age, gender, race/ethnicity, 
geographic region, insurance type) 
 
Clinical characteristics (e.g., selected comorbidities, baseline 
weight and body mass index [BMI], blood pressure, smoking 
status, laboratory measures, pulmonary function tests) 
 
Pharmacological interventions (e.g., index medication dose, 
time from the earliest diagnosis of IPF to index medication, 
other IPF treatments, bronchodilators, corticosteroids) 
 
Non-pharmacological interventions (e.g., surgical procedures, 
supportive therapies, diagnostic procedures) 
Data sources: 
GE Centricity EMR database from October 2013 to April 
2018  
Study size: 
A preliminary analysis identified 868 IPF patients with pirfenidone, 
615 patients with nintedanib, and about 10,000 untreated patients. 
Data analysis: 
Patient demographic and clinical characteristics will be described 
using descriptive analyses. Counts and percentages for categorical 
variables and measures of central tendency (mean, median, standard 
deviation [SD], and min/max) for continuous variables will be 
reported. Differences between the treatment cohorts (compared to each 
other and to untreated patients) will be assessed using absolute 
standardized differences and p-values. The probabilities of receiving 
one treatment over another will be tested by propensity scores and 
using multivariable logistic regression adjusted for key baseline 
characteristics. The similarities and differences between two treatment 
cohorts will be assessed using balance diagnostics tests. A stratified 
descriptive analysis will also be conducted at different time points to 
assess if the characteristics of the two exposures changes over time. 
Milestones: 
Milestone 
Planned Date 
Start of data collection 
December 2018 (start of data analysis) 
End of data collection 
March 2019 (end of data analysis) 
Preliminary results 
January/February 2019 
Final report of study 
results 
June 2019 

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5. 
AMENDMENTS AND UPDATES 
None 

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6. 
MILESTONES 
 
Milestone 
Planned Date 
Start of data collection 
December 2018 (start of data analysis) 
End of data collection 
March 2019 (end of data analysis) 
Preliminary results 
January/February 2019 
Final report of study results 
June 2019 
 

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7. 
RATIONALE AND BACKGROUND 
Idiopathic Pulmonary Fibrosis (IPF) is a specific form of chronic, progressive fibrosing 
interstitial pneumonia of unknown cause, occurring primarily in older adults, and limited to the 
lungs.1 IPF is a debilitating, life limiting condition with a median survival range of 2.5 to 3.5 
years from the time of diagnosis.2 A study using insurance claims data from the United States 
estimated the incidence of IPF to be between 6.8 and 16.3 per 100,000 persons.3 The same 
study yielded a prevalence estimate of between 14.0 and 42.7 per 100,000 persons, depending 
on the case definition used.3 The economic burden of IPF is significant, with the annual medical 
cost (hospitalization, emergency room [ER] visits, outpatient visits, and use of procedures and 
tests) to the US healthcare system estimated at close to $2 billion in 2012.4 
 
The goals of treatment for IPF are to stop disease progression, reduce symptoms, prevent acute 
exacerbations, and prolong survival.5 Pulmonary rehabilitation and oxygen treatment are 
commonly utilized as supportive treatments, and lung transplant surgery may be considered in 
some patients.5 Many pharmacological treatments (i.e. anticoagulants, combination of N-
acetylcysteine, prednisone and azathioprine, selective endothelin receptor) have been evaluated 
in IPF patients; however, international guidelines recommend against the use of these 
treatments due to insufficient evidence.6 The treatment options for patients with IPF changed 
significantly in October 2014 when the FDA approved two products that slowed disease 
progression, Ofev (nintedanib, Boehringer Ingelheim) and Esbriet (pirfenidone, Genentech).7,8 
Recently updated American Thoracic Society/European Respiratory Society/Japanese 
Respiratory Society, and Latin American Thoracic Association clinical practice guidelines 
gave conditional recommendations for the use of nintedanib and pirfenidone in the majority of 
patients with IPF.6 
 
Several clinical trials have been conducted to evaluate the efficacy and safety of these two 
emerging treatments. A recent systematic review and meta-analysis of ten randomized 
controlled trials showed that both pirfenidone and nintedanib reduced the progression of IPF 
in terms of FVC (forced vital capacity) decline and had similar safety profiles.9 However, only 
nintedanib significantly reduced the risk of all-cause and respiratory death. Pirfenidone and 
nintedanib also reduced the risk of a ≥10% decline in FVC over 12 months, although only 
nintedanib protected against the risk of acute exacerbations.9  
 
Patients selected into clinical trials usually present with fewer comorbidities, fall between 
certain age limits, and the severity of their disease is defined.10 Therefore, clinical trial 
populations do not always reflect the entire population affected by IPF in the real-world setting.   
Understanding patient characteristics is essential when assessing the real-world effectiveness 
of a treatment. Unlike being randomly assigned to a treatment in clinical trials, patients in the 
real world may choose between similar treatments based on existing comorbidities and 
concomitant medications, differentiating the effectiveness of the medications in certain 
patients. When comparing nintedanib and pirfenidone, it is therefore important to understand 
the differences in clinical characteristics of patients who are prescribed nintedanib compared 
to patients who are prescribed pirfenidone, and how these patients compare to IPF patients who 
do not receive antifibrotic treatment.  
 

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In order to help identify the profile of patients and specific characteristics associated with 
initiating treatment for IPF, BI is conducting a retrospective database study to understand 
differences in characteristics of patients who are prescribed pirfenidone or nintedanib, and 
those who do not receive a prescription for an antifibrotic treatment. This study will also help 
identify patient characteristics driving the initiation of nintedanib or pirfenidone by assessing 
the probability of receiving the treatments. 

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8. 
RESEARCH QUESTION AND OBJECTIVES 
The overall aim of this retrospective database study is to understand differences in 
characteristics of IPF patients who are prescribed nintedanib compared to those who are 
prescribed pirfenidone, and how these patients compare to patients who do not receive a 
prescription for antifibrotic treatment.  
 
The primary objective is: 
 
1. To describe and compare demographic, clinical, and other characteristics of IPF 
patients initiating nintedanib, pirfenidone, or not receiving prescription antifibrotic 
treatment  
 
The secondary objective is: 
 
2. To compare and contrast the probability (by propensity score) of receiving nintedanib 
vs. pirfenidone and nintedanib or pirfenidone vs. no treatment in IPF patients using 
baseline patient characteristics   
 

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9. 
RESEARCH METHODS 
9.1 
STUDY DESIGN 
To address the objectives above, this retrospective database study will use a new user cohort 
design, where cohorts are defined based on the exposures of interest (i.e., prescription for 
nintedanib or pirfenidone or no antifibrotic treatment). The study population will consist of the 
following three mutually exclusive cohorts: 
1. Nintedanib initiators, consisting of adult IPF patients who newly initiated nintedanib 
treatment  
2. Pirfenidone initiators, consisting of adult IPF patients who newly initiated pirfenidone 
treatment 
3. Untreated cohort, consisting of adult IPF patients without any prescription for 
antifibrotic treatment (i.e., no prescription for nintedanib nor pirfenidone) 
 
The primary study endpoint is the absolute standardized differences (ASD) and p-value 
comparing patient characteristics between the two treatment cohorts and between each 
treatment cohort and the untreated cohort, where an ASD of at least 10% will be considered a 
meaningful difference. The secondary endpoint is the probability (estimated using propensity 
score) of receiving nintedanib vs. pirfenidone and that of receiving nintedanib or pirfenidone 
vs. no treatment based on patient characteristics; to identify baseline characteristics that drive 
initiation of a treatment while minimizing prescription bias. 
 
These analyses will utilize real-world data to understand differences in patient characteristics 
between those prescribed nintedanib versus pirfenidone and those not receiving any antifibrotic 
treatments. The proposed data source, EMR data, allows exploration of several clinical 
characteristics (e.g., laboratory measures, medications, behavioral measures) in patients with 
IPF in a setting outside of a clinical trial.  
9.2 
SETTING 
This retrospective database study will leverage the GE EMR data from October 1, 2013 to April 
30, 2018. The EMR consists of patient data from ambulatory care records in the US and is 
representative of the US population receiving healthcare in the ambulatory setting. Patients will 
be included in one of three study cohorts based on prescription history from October 1, 2014 
to April 30, 2018, allowing for a 12-month pre-index period to describe patient demographic, 
clinical and treatment characteristics. 
 
Index date 
 
The index date for the nintedanib (or pirfenidone) cohort will be the date of the first nintedanib 
(or pirfenidone) prescription during the selection window (from October 1, 2014 to April 30, 
2018). The index date for the untreated cohort will be the date of the earliest IPF diagnosis or 
a randomly assigned date between October 1, 2013 and April 30, 2018. 
 
Pre-index period 
 

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The pre-index period for each cohort will be the 12 months prior to the index date. Patients will 
be required to have ≥ 1 diagnosis of IPF during the pre-index period (see additional inclusion 
criteria described below).  
 
Post-index period 
 
The post-index period for each cohort will start from the index date. Since the study aims to 
evaluate patient characteristics during the pre-index period, no minimum follow-up will be 
required for post-index period and no study measures will be assess during the post-index 
period.  
 
Patient selection is illustrated in Figure 1. 
 
Figure 1. Patient Selection 
 
 
 
Nintedanib cohort  
 
Inclusion criteria: 
• 
With ≥ 1 diagnosis for IPF (the International Classification of Diseases, Ninth Revision, 
Clinical Modification [ICD-9-CM] codes 516.3, 516.31, 515,11 or ICD-10-CM codes 
J84.112) in the EMR between October 1, 2013 to April 30, 2018  
• 
With ≥ 1 prescription for nintedanib between October 1, 2014 and April 30, 2018 (the 
selection window) 
o The date of the first prescription will be defined as the index date 
• 
With ≥ 1 record in the EMR database during the 12 months prior to the index date (the 
pre-index period) 
• 
With ≥ 1 diagnosis of IPF during the 12 months prior to the index date 
• 
Age ≥ 40 on the index date 
• 
 will explore also requiring ≥ 1 chest CT scan before first IPF diagnosis during 
the pre-index period12 
 
Exclusion criteria: 
• 
With ≥ 1 diagnosis of other known causes of interstitial lung disease (ILD) on the date 
of or after the first IPF diagnosis during the pre-index period12,13 

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o Other known causes of ILD include conditions such as systemic sclerosis, 
rheumatoid arthritis, systemic lupus erythematosus, dermatomyositis, polymyositis, 
Sjögren disease, and hypersensitivity pneumonitis (ICD-9-CM codes 135, 237.7, 
272.7, 277.3, 277.8, 446.21, 446.4, 495, 500–505, 506.4, 508.1, 508.8, 516.0, 516.1, 
516.32–516.37, 516.2, 516.8, 516.9, 517.0, 517.2, 517.8, 518.3, 555, 710.0, 710.0–
710.4, 714.0, 714.81, 720, and 759.5, or ICD-10-CM equivalent codes)12,13 
• 
With ≥ 1 prescription for nintedanib prior to the index date 
• 
With ≥ 1 prescription for pirfenidone prior to or on the index date 
 
Pirfenidone cohort 
 
Inclusion criteria: 
• 
With ≥ 1 diagnosis for IPF (ICD-9-CM codes 516.3, 516.31, 515,11 or ICD-10-CM 
codes J84.112) in the EMR between October 1, 2013 to April 30, 2018  
• 
With ≥ 1 prescription for pirfenidone between October 1, 2014 and April 30, 2018 (the 
selection window)  
o The date of the first prescription will be defined as the index date 
• 
With ≥ 1 record in the EMR database during the 12 months prior to the index date  
• 
With ≥ 1 diagnosis of IPF during the 12 months prior to the index date 
• 
Age ≥ 40 on the index date 
• 
 will explore also requiring ≥ 1 chest CT scan before first IPF diagnosis during 
the pre-index period12 
 
Exclusion criteria: 
• 
With ≥ 1 diagnosis of other known causes of interstitial lung disease (ILD) on the date 
of or after the first IPF diagnosis during the pre-index period12,13 
o Other known causes of ILD include conditions such as systemic sclerosis, 
rheumatoid arthritis, systemic lupus erythematosus, dermatomyositis, polymyositis, 
Sjögren disease, and hypersensitivity pneumonitis (ICD-9-CM codes 135, 237.7, 
272.7, 277.3, 277.8, 446.21, 446.4, 495, 500–505, 506.4, 508.1, 508.8, 516.0, 516.1, 
516.32–516.37, 516.2, 516.8, 516.9, 517.0, 517.2, 517.8, 518.3, 555, 710.0, 710.0–
710.4, 714.0, 714.81, 720, and 759.5, or ICD-10-CM equivalent codes) 12,13 
• 
≥ 1 prescription for pirfenidone prior to the index date 
• 
≥ 1 prescription for nintedanib prior to or on the index date 
 
Untreated cohort 
 
Inclusion criteria: 
• 
With ≥ 1 diagnosis for IPF (ICD-9-CM codes 516.3, 516.31, 515,11 or ICD-10-CM 
codes J84.112) in the EMR between October 1, 2013 to April 30, 2018  
o Index date will be defined as the date of the earliest IPF diagnosis or a randomly 
assigned date based on the exploratory analyses to be performed on the two 
treatment cohorts (exploring time from IPF diagnosis to treatment initiation) 
 
will explore requiring ≥ 2 diagnoses for IPF that are ≥ 30 days 
apart to improve the identification of IPF patients12,13 
• 
With ≥ 1 record in the EMR database during the 12 months prior to the index date (the 
pre-index period) 

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• 
Age ≥ 40 on the index date 
• 
 will explore also requiring ≥ 1 chest CT scan before first IPF diagnosis during 
the pre-index period12 
 
Exclusion criteria: 
• 
With ≥ 1 diagnosis of other known causes of interstitial lung disease (ILD) on the date 
of or after the first IPF diagnosis during the pre-index period 
o Other known causes of ILD include conditions such as systemic sclerosis, 
rheumatoid arthritis, systemic lupus erythematosus, dermatomyositis, polymyositis, 
Sjögren disease, and hypersensitivity pneumonitis (ICD-9-CM codes 135, 237.7, 
272.7, 277.3, 277.8, 446.21, 446.4, 495, 500–505, 506.4, 508.1, 508.8, 516.0, 516.1, 
516.32–516.37, 516.2, 516.8, 516.9, 517.0, 517.2, 517.8, 518.3, 555, 710.0, 710.0–
710.4, 714.0, 714.81, 720, and 759.5, or ICD-10-CM equivalent codes) 12,13 
• 
With prescription for nintedanib or pirfenidone anytime during the data window  
 
9.3 
VARIABLES 
9.3.1 
Exposures 
Both nintedanib (Ofev, BI) and pirfenidone (Esbriet, Genentech) were approved by the US 
FDA on October 15, 2014 (reflected by start of selection window in this study). Nintedanib 
initiators will be identified using prescription records for nintedanib, with the index date being 
the first prescription in the EMR during the selection window. Similarly, pirfenidone initiators 
will be identified using prescription records for pirfenidone; index date is first prescription. 
New users of these medications will be selected into the study, excluding those who initiated 
both medications on index. The number of patients who initiated both medications on index 
will be reported during the patient attrition. Since the purpose of this study is to identify 
predictors of IPF treatment initiation, no post-index follow-up measures will be reported. 
Patients who switched between the treatments during the pre-index period will be excluded 
because of the new user design, and those who switch after the index date will not be a concern 
as no study measures will be evaluated during the post-index period for the objectives of this 
study. No validation will be performed on the exposures of interest as exposure ascertainment 
relies on evidence of ≥ 1 prescription record.     
9.3.2 
Outcomes 
9.3.2.1 
Primary outcomes 
The primary outcome will be the ASD and p-value comparing baseline patient characteristics 
(as described in section 9.3.3) between the following cohorts: 
• 
Nintedanib vs. pirfenidone 
• 
Nintedanib vs. untreated 
• 
Pirfenidone vs. untreated  

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9.3.2.2 
Secondary outcomes 
The secondary outcomes will be the probabilities (propensity scores) of receiving one treatment 
over another (or probability of receiving antifibrotic treatment) between the following cohorts: 
• 
Nintedanib vs. pirfenidone 
• 
Treated patients (i.e., nintedanib or pirfenidone) vs. untreated 
 
9.3.2.3 
Further outcomes 
None. 
9.3.3 
Covariates 
The following measures will be evaluated during the 12-month baseline period, including index 
date, unless otherwise noted. Measures will be reported separately for each of the three cohorts.  
 
For nintedanib and pirfenidone cohorts only: 
 
• 
Index medication (n, %) 
o Nintedanib  
o Pirfenidone  
• 
Average daily dose of the index prescription  
o Nintedanib (mean, SD, median, min, max) and categories (n, %) 
 100 mg 
 150 mg 
 Unknown 
o Pirfenidone (mean, SD, median, min, max) and categories (n, %) 
 267 mg 
 Unknown  
• 
Time from the earliest diagnosis for IPF to index (mean, SD, median, min, max) 
• 
Prescribing physician specialty (n, %) 
o Primary care 
o Internal medicine 
o Pulmonologist 
o Other 
 
For all cohorts: 
 
Demographic characteristics  
• 
Age (mean, SD, median, min, max) and age categories on index date (n, %) 
o 40-49  
o 50-59  

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001-MCS-90-124_RD-01 (4.0) 
o 60-69  
o ≥70  
• 
Gender (n, %) 
o Male 
o Female 
• 
Index year (n, %) 
o 2014 
o 2015 
o 2016 
o 2017 
o 2018 
 Region (n, %)   
o Northeast  
o Midwest  
o South  
o West 
 Race/ethnicity (n, %) (expected to be missing for about 40% of patients) 
o White 
o African American 
o Hispanic 
o Asian 
o Other 
o Unknown 
 Payer type (n, %)  
o Commercial 
o Medicaid  
o Medicare 
o Self-Insured 
o Other/Unknown  
 
Clinical characteristics (based on ICD-9/10-CM procedure and diagnosis codes, laboratory 
test codes, CPT codes and NDC/RxNorm (to be included in Appendix), as well as other 
information in the EMR encounter files) 
o Body weight (lbs; mean, SD, median, min, max) 
o Body mass index (BMI; kg/m2; mean, SD, median, min, max) 
o BMI category, defined using BMI field (n, %) 
 Underweight (<18.5 kg/m2) 
 Normal weight (18.5 to 24.99 kg/m2) 
 Overweight (25 to 29.99 kg/m2) 
 
Obese (30 to 34.99 kg/m2) 
 
Very obese (≥ 35 kg/m2) 

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o Blood pressure (BP)14 (n, %) 
 Diastolic BP  
- Normal (<80 mmHg) 
- High (≥80 mmHg) 
 Systolic BP  
- Normal (<120 mmHg) 
- Elevated (≥120 and < 129 mmHg) 
- High (≥130 mmHg), further broken down as follows if data allow 
• 
Stage 1 (≥130 and < 139 mmHg) 
• 
Stage 2 (≥140 mmHg) 
o Smoking status at index (n, %); excluding smokeless tobacco; categories to be 
finalized during analysis 
 Smoker 
 Non-smoker, further broken down as follows if data allow 
- Previous smoker 
- Never smoker 
 Unknown/missing 
o Alcohol consumption (n, %) 
 Yes 
 No 
 Unknown/missing 
o Charlson comorbidity index (CCI) 15 (mean, SD, median, min, max) and categories 
(n, %), defined using ICD diagnosis codes 
 0  
 1  
 2  
 3  
 
4+  
o Respiratory comorbidities16 (n, %), defined using ICD diagnosis codes 
 COPD (Chronic obstructive pulmonary disease), includes chronic bronchitis 
and emphysema 
 Pulmonary hypertension 
 Obstructive sleep apnea 
 Lung cancer 
 Pulmonary embolism 
 Asthma 
 Acute respiratory distress syndrome 
 Pulmonary edema 
 Cough  
 Upper respiratory tract infection (includes acute bronchitis) 
 Bronchitis (not specified as acute or chronic) 

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 Sinusitis 
o Gastroesophageal reflux disease (GERD) (n, %), defined using ICD diagnosis codes 
o Metabolic disorders16 (n, %), defined using ICD diagnosis codes 
 Obesity (defined as ICD-9 code 278.0 or ICD-10 code E66 or BMI ≥ 30 
kg/m2) 
 Diabetes 
- Any diabetes 
- Type 1 diabetes 
- Type 2 diabetes16 
 Dyslipidemia (includes hypercholesterolemia and hyperlipidemia) 
o Cardiovascular disorders16 (n, %), defined using ICD diagnosis codes 
 Arrhythmia 
 Atrial fibrillation 
 Heart failure 
 Ischemic heart disease 
 Stroke 
 Hypertension (includes systemic arterial hypertension) 
 Peripheral arterial diseases 
 Cardiomyopathy 
o Mental disorders (n, %), defined using ICD diagnosis codes 
 Depression 
 Anxiety 
 Schizophrenia 
 Dementia 
 Bipolar disorders 
o Renal impairment (n, %), defined using ICD diagnosis codes 
o Hepatic impairment (n, %), defined using ICD diagnosis codes 
 
• 
Laboratory measures (to the extent that the data allow; tests will be identified by 
procedure codes and EMR data fields; if repeated measurements of a test are observed, 
the one closest to the index date will be reported, with the exception of several renal 
and liver measures (where noted) when any evidence of patient history of the condition 
during baseline will take precedent over the measure closest to index) 
o Anticoagulation tests (n, %) 
 Prothrombin time (n, %) 
- High (>14 seconds) 
- Normal (10-14 seconds) 
- Low (<10 seconds) 
 Activated partial thromboplastin time (aPTT; n, %) 
- Normal (30-40 seconds) 
- Elevated (41-70 seconds) 

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- Critical (>70 seconds) 
 Bleeding time (n, %) 
- Normal (1-9 minutes) 
- Platelet dysfunction (10-15 minutes) 
- Critical (>15 minutes) 
o Hematologic markers  
 Blood hemoglobin (mean, SD, median, min, max) 
 Blood hematocrit (mean, SD, median, min, max) 
 Red blood cell count (mean, SD, median, min, max) 
 Platelet count (n, %) 
- Low (<150,000 platelets/mcL) 
- Normal (150,000-450,000 platelets/mcL) 
- High (>450,000 platelets/mcL) 
 Conventional C - reactive protein (n, %) 
- Normal (≤5.0 mg/L) 
- High (>5.0 mg/L) 
o Liver function tests (n, %) 
 Alanine transaminase (ALT; n, %); patient history of high ALT level 
suggesting hepatic impairment will be reported and take precedent to ALT 
value closest to index 
- Low (<7 U/L) 
- Normal (7 – 56 U/L) 
- High (>56 U/L) 
 Aspartate transaminase (AST; n, %); patient history of high AST level 
suggesting hepatic impairment will be reported and take precedent to AST 
value closest to index 
- Low (<5 U/L) 
- Normal (5 – 40 U/L) 
- High (>40 U/L) 
 Alkaline phosphatase (ALP; n, %) 
- Low (<44 IU/L) 
- Normal (44 – 147 IU/L) 
- High (>147 IU/L) 
 Gamma-glutamyl transpeptidase (GGT; n, %) 
- Low (<9 U/L) 
- Normal (9-48 U/L) 
- High (>48 U/L) 
 Total bilirubin (n, %); patient history of high bilirubin level suggesting 
hepatic impairment will be reported and take precedent to value closest to 
index 
- Low (<0.2 mg/dL) 

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- Normal (0.2-1.2 mg/dL) 
- High (>1.2 mg/dL) 
o Kidney function tests (n, %) 
 Albumin to creatinine ratio (ACR; n, %) 
- Normal (<30) 
- Microalbuminuria (30-300) 
- Macroalbuminuria (>300) 
 Glomerular filtration rate (GFR; n, %); patient history of low GFR 
suggesting renal impairment will be reported and take precedent to value 
closest to index 
- Low (<90 mL/min/1.73m2) 
- Normal (90 – 120 mL/min/1.73m2) 
- High (>120 mL/min/1.73m2) 
 Serum creatinine (mean, SD, median, min, max); patient history of high 
serum creatinine level suggesting renal impairment will be reported and take 
precedent to value closest to index 
o Cholesterol levels (n, %) 
 Total cholesterol (mg/dL; n, %) 
- Normal (<200 mg/dL) 
- Borderline high (200-239 mg/dL) 
- High (≥240 mg/dL) 
 Triglyceride (mg/dL; n, %) 
- Normal (<150 mg/dL) 
- Borderline high (150-199 mg/dL) 
- High (≥200 mg/dL) 
 Cholesterol in high-density lipoprotein (HDL; n, %) 
- Normal (≥40 mg/dL) 
- Abnormal (<40 mg/dL) 
 Cholesterol in low-density lipoprotein (LDL; n, %) 
- Normal (<100 mg/dL) 
- Borderline high (100 – 159 mg/dL) 
- High (≥160 mg/dL) 
• 
Pulmonary function tests (PFTs) indicative of IPF severity (n, %) 
o FVC (n, %) 
 Normal (≥80%) 
 Abnormal (<80%) 
o Forced expiratory volume in one second (FEV1) /FVC ratio (n, %) 
 Normal (≥70%) 
 Abnormal (<70%) 
o Total lung capacity (TLC) (mean, SD, median, min, max) 
o Carbon monoxide diffusing capacity of the lung (DLCO; n, %) 

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 Normal (>75% of predicted) 
 Mild to moderate decrease (40-74% of predicted) 
 Severe decrease (<40% of predicted) 
o Oxygen saturation (n, %) 
 Normal (95-100%) 
 Below normal (<95%) 
o GAP index18 (n, %), if data allow 
 Calculated using information on age, gender and FVC  
 Stage I (0-3 points) 
 Stage II (4-5 points) 
 Stage III (6-8 points) 
 
Treatment history during the pre-index period (not including index date): 
• 
Pharmacological interventions (n, %) 
o Other treatments (as per ATS/ERS/JRS/ALAT guidelines19)  
 Selective tyrosine kinase inhibitor against platelet derived growth factor 
(Imatinib) 
 Prednisone 
 Azathioprine 
 N-acetylcysteine 
 Selective endothelin receptor antagonist (ambrisentan) 
 Phosphodiesterase-5 inhibitor (sildenafil) 
 Dual endothelin receptor antagonists (macitentan, bosentan) 
o Corticosteroids  
 Inhaled corticosteroids 
 Oral corticosteroids 
o Bronchodilators 
 SABA 
 LABA 
 Anticholinergics 
 Other beta agonists 
o GERD therapy 
 H-2 receptor blockers  
 Proton pump inhibitors  
 Antacids  
o Cardiovascular drugs 
 Antianginals 
 Antihypertensives 
 Antiarrhythmics 
 Antiplatelets 
 Anticoagulants 

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o Antidiabetes drugs 
o Anti-infectives 
o Anticonvulsants 
o Anti-anxiety drugs 
o Antidepressants 
o Antipsychotics 
o Antihyperlipidemics 
• Non-pharmacological interventions (n, %) 
o Surgical procedures (n, %) 
 Lung transplantation  
 Esophageal sphincter reinforcement  
 Lung cancer surgeries 
o Supportive therapy (n, %) 
 Oxygen therapy 
 Pulmonary rehabilitation 
o Diagnostic procedures (n, %) 
 High resolution computed tomography (HRCT) 
 Surgical lung biopsy  
 Chest X-rays 
 6-minute walk test/pulmonary stress test 
 Spirometry 
 Bronchoalveolar lavage 
 
Treatment on the index date (pharmacological interventions only): 
• 
For each of the following treatments, the number and proportion of patients with and 
without the treatment on the index date will be reported (days’ supply to be considered 
when exploring treatment use on index) 
o Other treatments (as per ATS/ERS/JRS/ALAT guidelines19; n, %)  
 Selective tyrosine kinase inhibitor against platelet derived growth factor 
(Imatinib) 
 Prednisone 
 Azathioprine 
 N-acetylcysteine 
 Selective endothelin receptor antagonist (ambrisentan) 
 Phosphodiesterase-5 inhibitor (sildenafil) 
 Dual endothelin receptor antagonists (macitentan, bosentan) 
o Corticosteroids (n, %) 
 Inhaled corticosteroids 
 Oral corticosteroids 
o Bronchodilators (n, %) 
 SABA 

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001-MCS-90-124_RD-01 (4.0) 
 LABA 
 Anticholinergics 
 Other beta agonists 
o GERD therapy (n, %) 
 H-2 receptor blockers  
 Proton pump inhibitors  
 Antacids  
o Cardiovascular drugs (n, %) 
 Antianginals 
 Antihypertensives 
 Antiarrhythmics 
 Antiplatelets 
 Anticoagulants 
o Antidiabetes drugs (n, %) 
o Anti-infectives (n, %) 
o Anticonvulsants (n, %) 
o Anti-anxiety drugs (n, %) 
o Antidepressants (n, %) 
o Antipsychotics (n, %) 
o Antihyperlipidemics (n, %) 
 
9.4 
DATA SOURCES 
 GE Centricity EMR database will be used for this study. This is an anonymized Health 
Insurance Portability and Accountability Act of 1996 (HIPPA) compliant database populated 
with patient data from ambulatory care records. The EMR database is obtained from a national 
network of primary care practices (through GE Centricity EMR System) whose providers allow 
their de-identified patient-level data to be available for research. Clinical data are captured from 
more than 725 member institutions, more than 43,000 providers and currently captures activity 
on over 42 million patients from 48 states & the District of Columbia. The EMR had 42 million 
active patients as of January 2018 and the data continues to grow. Of special interest for this 
study, there are about 456 Pulmonologist providing their patient data. Additionally, there are 
about 78 million records from patients covered by Medicare, 69 million from Medicaid, and 
58 million records from commercially insured patients.   
 
The size and geographic representation of the data network are illustrated in Figure 2. 
 
 Figure 2. Geographic representation of 
 GE Centricity EMR Database 

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001-MCS-90-124_RD-01 (4.0) 
 
 
At the individual patient-level, data are processed to provide researchers with demographic 
information, clinical characteristics (International Classification of Diseases, Ninth Revision, 
Clinical Modification (ICD-9-CM) and ICD-10 based medical diagnoses, patient complaints), 
diagnostic tests/results, procedures, insurance information (commercial, Medicare, Medicaid, 
etc.), and prescription details. Information from specialty healthcare providers (e.g., 
endocrinologists), and laboratory test orders/results are also available. The data are organized 
by practice and provide longitudinal medical records for each patient. Comparisons of the EMR 
patient population to the general population of the United States on demographics (US Census), 
outpatient visits related to acute and chronic conditions (NCHS-NAMCS), and disease 
prevalence (NCHS-NHANES) show the EMR is reflective of the US population receiving 
healthcare in the ambulatory setting.20,21  
 
The medical record in the EMR database provides patient level information including the 
following key analysis variables:  
• 
Age  
• 
Gender  
• 
Race/ethnicity 
• 
Geographic region  
• 
BMI, height, weight  
• 
Systolic BP, Diastolic BP, Pulse  

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001-MCS-90-124_RD-01 (4.0) 
• 
Procedures performed in outpatient settings (e.g. HRCT, SLB, chest X-rays, 
spirometry) 
• 
Laboratory values, when recorded (e.g. anticoagulant test, blood counts, kidney and 
liver function tests)  
• 
Behavioral measures (e.g. smoking status) 
• 
Prescribing physician specialty  
• 
Health insurance details 
• 
Comorbid conditions  
• 
Concomitant medication usage  
• 
Outpatient encounters 
9.5 
STUDY SIZE 
A preliminary feasibility assessment of the most recent EMR data indicates the presence of 
2,499 patients newly starting on an IPF treatment (Table 1). 64% of patients are 40 years or 
above, of which 76% had at least one pulmonary function test. An early feasibility assessment 
estimated that about 10,000 IPF patients were not treated with antifibrotics.  
 
Considering the orphan nature of IPF and recent FDA approval of these two drugs (within last 
three years), the EMR database is a good source to extract such a population as it continues to 
grow. The IPF patients counts are reflective of national estimates (6.8–8.8 per 100,000; 
<0.01%). The availability of patients should provide sufficient sample to estimate standardized 
differences in patient characteristics between three cohorts. 
 
Table 1. Preliminary Patient Counts (will vary once study selection criteria are applied, as 
outlined in Section 9.2) 
 
 
Characteristics 
Number of patients 
1 
Evidence of IPF treatment (pirfenidone or 
nintedanib) between October 1, 2014 to March 30, 
2018), first date is index 
2,750 
2 
In #1 with ≥1 EMR before index date 
2,508 
3 
In #2 with no evidence of pirfenidone or nintedanib 
before index date 
2,499 
4 
In #3 with diagnosis of IPF (ICD-9-CM codes 
516.3x, 515, or ICD-10-CM codes J84.112) before 
index date 
2,061 
5a 
In #4 and with prescription for (mutually exclusive): 
1,627 
 
Pirfenidone only 
868 
 
Nintedanib only 
615 
 
Both Pirfenidone and Nintedanib 
144 
5b 
In #4 and age ≥ 40 years 
1,623 
5c 
In #4 with at least one PFT observations  
(FEV1, FVC, TLC, Oxygen saturation, OR DCCO) 
(with non-missing test results) BEFORE index 
1,231 
6a 
In 5c with at least one oxygen saturation value 
1,150 
6b 
In 5c with at least one DCCO value 
293 

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001-MCS-90-124_RD-01 (4.0) 
 
Characteristics 
Number of patients 
6c 
In 5c with at least one TLC value 
217 
6d 
In 5c with at least one FEV/FVC value 
627 
 
9.6 
DATA MANAGEMENT 
The study will involve use of 
 de-identified EMR data files. Data obtained from the 
EMR files will be imported into and maintained in a SAS® data analysis file.  Tabulation of 
summary statistics, graphical presentations, and statistical analysis will be performed using 
SAS® software. The data files are statistically de-identified and don’t contain any personal 
identifiable information. The data are not subject to oversight by the Institutional Review Board 
(IRB); however, IRB exemption will be obtained. All files will be stored on a secure server 
and access will be limited to the project team and other authorized personnel.  
 
After applying study inclusion and exclusion criteria, a study database will be created with all 
of the necessary variables to perform statistical analysis. The aggregated data (in form of 
counts, %, mean, median, etc.) will be shared with BI at each stage of data programming. The 
data abstraction will be monitored continuously for quality control purposes, and final results 
will only be reported after thorough review of programming codes by internal staff members 
as well as external consultants.    
9.7 
DATA ANALYSIS 
We will ensure internal validity by measuring key study measures and outcomes using a 
standardized process. All codes and definitions will be reviewed by BI, and any modifications 
to them can be made prior to delivering the final protocol document.  
 
With respect to external validity, IPF patients will be identified using ICD-9 or ICD-10 
diagnosis codes that have been used in previously published studies.11-13 Pirfenidone and 
nintedanib, as well as other treatments of interest in this study, will be identified by RxNorm 
(where available) and a search of drug names (including possible variants of the name). 
Although there are some potential limitations with generalizability of the study findings, the 
current study is being conducted using an internal database that contains information on 42 
million active patients in the US. Therefore, the findings from our study will have broad 
generalizability to IPF patients, ensuring appropriate external validity.  
 
9.7.1 
Main analysis 
For the primary objective, baseline patient characteristics will be described using descriptive 
analysis, i.e. described as counts and percentages for categorical variables (e.g. gender, race, 
comorbidities, treatments, diagnostic tests performed) and measures of central tendency (mean, 
median, SD, and min/max) for continuous variables (e.g., age, lab measures values, BMI). 
Differences between the cohorts will be assessed using ASD, where an ASD of at least 10% 
will be considered a meaningful difference.22 Patients with missing data will be reported as 
missing and excluded from the comparison for the missing variable. 

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For continuous variables, the standardized differences will be calculated as 
𝑑= (𝑋1 − 𝑋2)
√𝑠1
2 + 𝑠2
2 
2
 
 
Where 𝑋 denote sample means in each exposure cohort, and s denotes sample variances.  
 
For categorical variables, the standardized differences will be calculated as 
𝑑= 
(𝑝1 −𝑝2)
√[𝑝1(1 −𝑝1) + 𝑝2(1 −𝑝2)]
2
 
 
Where 𝑝 denotes proportion of a binary baseline variable in the two exposure cohorts.  
 
The confidence intervals (CIs) for standardized differences will also be calculated as defined 
below:  
95% 𝐶𝐼= 𝑑 ± 1.96 ×  𝜎 [𝑑] 
 
𝑤ℎ𝑒𝑟𝑒, 𝜎 [𝑑] = √𝑛1 + 𝑛2
𝑛1 × 𝑛2 +
𝑑2
2(𝑛1 + 𝑛2),  
 
Where ni denotes sample sizes of each exposure cohort.  
 
Bivariate statistical significance tests such as Chi-square test for categorical variables and 
Wilcoxon rank sum test or t-test for continuous variables (depending on distribution of data) 
will also be performed to compare differences in baseline characteristics between the three 
cohorts. In addition to standardized differences, p-value in comparing characteristics between 
the three cohorts will also be reported to help identify “key drivers” associated with treatment 
selection.  
 
For the second objective, the probabilities of receiving one treatment over another (or 
nintedanib or pirfenidone vs. untreated) will be tested by propensity scores (PS) and using 
multivariable logistic regression (possibly with stepwise selection) after controlling for key 
baseline characteristics (selected based on significance in bivariate analysis or face validity, 
e.g. age). The similarities and differences between the exposure cohorts will be assessed using 
balance diagnostics tests (e.g., graphical distribution of overlap between propensity scores of 
two cohorts (histograms, boxplots), percentage of sample matched, Pearson’s correlation 
coefficient, standardized differences, density estimates, c-statistics, receiver operating 
characteristic [ROC] curves). Ninety-five percent confidence intervals will be calculated for 
key parameters. Statistical tests will be two-sided, with α-level of 0.05 for statistical 
significance.  

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9.8 
QUALITY CONTROL 
The study team will be responsible for upholding high levels of quality assurance and control 
throughout the data analysis and reporting processes. Data quality will be enhanced through a 
series of programmed data quality checks that automatically detect out-of-range or anomalous 
data. All the modifications to the data will be recorded in an audit trail. 
 
Data validation checks will include checks for missing data and if systematic patterns are 
observed, adjustments may be made to account for missing data. 
 
Highly qualified personnel from 
 will serve on this project team. 
 will 
serve as the scientific 
 on the project with 
 serving as the 
content and strategy advisor, and 
 providing research and delivery support for 
all project tasks. Each has extensive experience in multiple areas of health outcomes research 
and epidemiology, with experience working with the numerous BI teams. They will be assisted 
by clinical experts and research and programming support staff throughout the project.  
 
The project team has experience leading past and ongoing retrospective database studies for BI 
among IPF patients, and extensive experience conducting research leveraging 
 GE 
Centricity EMR database. 
 
9.9 
LIMITATIONS OF THE RESEARCH METHODS 
The major limitation of the study is the lack of generalizability beyond the ambulatory care 
setting due to the data source. Certain IPF related outcomes such as exacerbation related 
hospitalizations and ER visits will not be examined due to unavailability in the data source. 
Additionally, certain IPF related patient characteristics such as lung transplantation may be 
under reported due to the ambulatory nature of the data. Also specific to the use of an EMR 
database is the limitation that treatment use will be measured by ≥ 1 prescription; information 
is not available on if the patient picked up the prescription and adhered to the treatment. This 
may result in misclassifying patients that did not take their prescriptions as intended or even 
start the medication, as treated with one of the two study drugs of interest. Also, this study may 
suffer from limitations common to retrospective database analyses, including possible missing 
and incorrect data in addition to the fact that data is not collected solely for research purposes. 
In particular, it is possible that some important covariates could not be included in the PS model 
due to missing data, leading to a potential bias in representing the choice of treatment in the 
real world. 

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001-MCS-90-124_RD-01 (4.0) 
9.10 
OTHER ASPECTS 
None. 
9.11 
SUBJECTS 
The source population will be patients in the GE EMR database, which is representative of the 
US population receiving healthcare in the ambulatory setting. The eligibility criteria of study 
subjects are described in detail in Section 9.2. 
9.11.1 
Cases 
Not applicable. 
9.11.2 
Controls 
Not applicable. 
9.12 
BIAS 
To minimize the bias resulting from treatment switching, study selection for new initiators will 
be used to exclude patients with nintedanib or pirfenidone use prior to index.  In addition, an 
algorithm for IPF identification adapted from claim-based algorithms (see Section 9.2) will be 
used for the untreated cohort to minimize the bias resulting from misclassifying patients as 
having IPF or not.12,13  

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001-MCS-90-124_RD-01 (4.0) 
10. 
PROTECTION OF HUMAN SUBJECTS 
The database is Health Insurance Portability and Accountability Act (HIPAA) compliant, and 
all patient data was de-identified before delivery to BI (i.e. existing data). An IRB exemption 
will be obtained for the study. 

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BI Study Number 1199-0375 
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001-MCS-90-124_RD-01 (4.0) 
11. 
MANAGEMENT AND REPORTING OF ADVERSE 
EVENTS/ADVERSE REACTIONS 
 
Individual case safety reports will not be reported because the 
 GE Centricity EMR 
database is a secondary database which consists of fully de-identified data. Therefore, it is not 
possible to assess the causality of individual cases. 
 
Data are anonymized, extracted, analyzed, validated and reported in aggregate. 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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001-MCS-90-124_RD-01 (4.0) 
12. 
PLANS FOR DISSEMINATING AND COMMUNICATING 
STUDY RESULTS 
Both preliminary and final study results will be delivered to BI. The final study report will 
include an abstract for presenting the study results at international conferences.  The 
preliminary results will be delivered and presented to BI by February 22, 2019. The final study 
report will be delivered to BI by June 14, 2019. A manuscript is an optional final deliverable, 
if of interest to BI. 
 

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001-MCS-90-124_RD-01 (4.0) 
13. 
REFERENCES 
13.1 
PUBLISHED REFERENCES 
1. Raghu G, Collard HR, Egan JJ, et al. An Official ATS/ERS/JRS/ALAT Statement: Idiopathic 
Pulmonary Fibrosis: Evidence-based Guidelines for Diagnosis and Management. Am J Respir 
Crit Care Med 2011; 183:788-824. 
2. Ley B., Collard H.R., King T.E. Clinical Course and Prediction of Survival in Idiopathic 
Pulmonary Fibrosis. Am. J. Respir. Crit. Care Med. 2011; 183:431–440. 
3. Raghu G, Weycker D, Edelsburg J, et al. Incidence and prevalence of idiopathic pulmonary 
fibrosis. Am J Respir Crit Care Med 2006; 27:136-142. 
4. Collard HR, Chen S-Y, Yeh W-S, et al. Health Care Utilization and Costs of Idiopathic 
Pulmonary Fibrosis in U.S. Medicare Beneficiaries Aged 65 Years and Older. Annals of the 
American Thoracic Society 2015;12(7):981-987. 
5. Bonella F, Stowasser S, & Wollin L. Idiopathic pulmonary fibrosis: current treatment options 
and critical appraisal of nintedanib. Drug Des Devel Ther 2015; 9:6407-6419. 
6. Raghu G, Rochwerg B, Zhang Y, et al. An Official ATS/ERS/JRS/ALAT Clinical Practice 
Guidelines: Treatment of Idiopathic Pulmonary Fibrosis. An Updates of the 2011 Clinical 
Practice Guideline. Am J Respir Crit Care Med 2015; 192(2): e3-e19. 
7. U.S. Food and Drug Administration. FDA News Release: FDA approves Ofev to treat 
idiopathic pulmonary fibrosis. October 15, 2014. Retrieved from 
http://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm418994.htm. . 
8. U.S. Food and Drug Administration. FDA News Release: FDA approves Esbriet to treat 
idiopathic pulmonary fibrosis. October 15, 2014. Retrieved from 
http://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm418991.htm. . 
9. Rogliani P, Calzetta L, Cavalli F, Matera MG, Cazzola M. Pirfenidone, nintedanib and N-
acetylcysteine for the treatment of idiopathic pulmonary fibrosis: A systematic review and 
meta-analysis. Pulm Pharmacol Ther. 2016. 
10. Harari S, Caminati A. Idiopathic pulmonary fibrosis: from clinical trials to real-life 
experiences. Eur Respir Rev. 2015 Sep; 24(137):420-7. 
11. Esposito DB, Lanes S, Donneyong M, et al. Idiopathic pulmonary fibrosis in United States 
automated claims. Incidence, prevalence, and algorithm validation. American journal of 
respiratory and critical care medicine. 2015 Nov 15;192(10):1200-7. 
12. Ley B, Urbania T, Husson G, et al. Code-based diagnostic algorithms for idiopathic 
pulmonary fibrosis. case validation and improvement. Annals of the American Thoracic 
Society. 2017 Jun;14(6):880-7. 
13. Esposito DB, Lanes S, Donneyong M, et al. Idiopathic pulmonary fibrosis in United States 
automated claims. Incidence, prevalence, and algorithm validation. American journal of 
respiratory and critical care medicine. 2015 Nov 15;192(10):1200-7. 
14. Carey RM, Whelton PK. Prevention, detection, evaluation, and management of high blood 
pressure in adults: synopsis of the 2017 American College of Cardiology/American Heart 
Association hypertension guideline. Annals of internal medicine. 2018 Mar 6. 
15. Quan H, Li B, Couris CM, et al. Updating and validating the Charlson comorbidity index and 
score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am J 
Epidemiol. 2011;173(6):676-682. 

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BI Study Number 1199-0375 
Proprietary confidential information © 2019 Boehringer Ingelheim International GmbH or one or more of its affiliated companies 
 
 
 
 
001-MCS-90-124_RD-01 (4.0) 
16. Raghu G, Amatto VC, Behr J, Stowasser S. Comorbidities in idiopathic pulmonary fibrosis 
patients: a systematic literature review. European Respiratory Journal. 2015 Oct 
1;46(4):1113-30. 
17. Kopf S, Groener JB, Kender Z, et al. Breathlessness and Restrictive Lung Disease: An 
Important Diabetes-Related Feature in Patients with Type 2 Diabetes. Respiration. 2018 Jun 
6:1-2. 
18. Ley B, Ryerson CJ, Vittinghoff E, et al. A multidimensional index and staging system for 
idiopathic pulmonary fibrosis. Annals of internal medicine. 2012 May 15;156(10):684-91. 
19. Raghu G, Rochwerg B, Zhang Y, et al. An official ATS/ERS/JRS/ALAT clinical practice 
guideline: treatment of idiopathic pulmonary fibrosis. An update of the 2011 clinical practice 
guideline. American journal of respiratory and critical care medicine. 2015 Jul 15;192(2):e3-
19. 
20. Crawford, A.G., et al. Comparison of GE Centricity Electronic Medical Record database and 
National Ambulatory Medical Care Survey findings on the prevalence of major conditions in 
the United States. Popul Health Manag 13, 139-150 (2010). 
21. Crawford, A.G., et al. Prevalence of obesity, type II diabetes mellitus, hyperlipidemia, and 
hypertension in the United States: findings from the GE Centricity Electronic Medical Record 
database. Popul Health Manag 13, 151-161 (2010). 
22. Austin PC. Balance diagnostics for comparing the distribution of baseline covariates between 
treatment cohorts in propensity-score matched samples. Stat Med 2009; 28 (25): 3083 – 3107. 
13.2 
UNPUBLISHED REFERENCES 
None 

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001-MCS-90-124_RD-01 (4.0) 
ANNEX 1. LIST OF STAND-ALONE DOCUMENTS 
1. CHANNELING_IPF Diagnostic and Procedure Codes_20181108.xls 
-Contains: 
Procedure (including laboratory) codes (Section 9.3.3) 
Diagnosis codes for comorbidities (Section 9.3.3) 
 
2. BI_2477175_Channeling IPF_medication codes 20181107.xls 
Contains: 
NDC codes for medications (Section 9.3.3) 
HCPCS codes for medications (Section 9.3.3) 
  
 
 

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001-MCS-90-124_RD-01 (4.0) 
ANNEX 2. ENCEPP CHECKLIST FOR STUDY PROTOCOLS 
ENCePPChecklistfor
StudyProtocols.doc  

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001-MCS-90-124_RD-01 (4.0) 
ANNEX 3. ADDITIONAL INFORMATION 
1. Contact information of main parties 
 
Principal Investigator: 
O: 
 | M: 
 
 
Other investigators: 
O: 
 | M: 
 
 
M: