Comparative Effectiveness and Safety of Inhaled Corticosteroids and Antimicrobial Compounds for Non-CF Bronchiectasis
Conditions: Bronchiectasis
Sponsor: Oregon Health and Science University

 
 
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Comparative effectiveness and safety of inhaled 
corticosteroids and antimicrobial compounds for non-CF 
bronchiectasis 
 
 
Funded by: 
Patient Centered Outcomes Research Institute (PCORI) 
 
 
Principal Investigator: 
Kevin L. Winthrop, MD, MPH 
Oregon Health & Science University 
 
 
Protocol Version: 1.4 
Protocol Date: 16 Nov 2017 

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i 
SIGNATURE PAGE 
The signature below constitutes the approval of this protocol and the attachments, and provides 
the necessary assurances that this trial will be conducted according to all stipulations of the 
protocol, including all statements regarding confidentiality, and according to local legal and 
regulatory requirements and applicable US federal regulations and ICH guidelines. 
 
Site Investigator: 
 
Signed: 
 
Date: 
 
 
Name 
Title 
 
 
 
 
 

 
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Table of Contents 
 
 
page 
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ii 
Statement of Compliance ............................................................................................................. i 
Signature Page ........................................................................................................................... ii 
List of Abbreviations ................................................................................................................... iv 
Protocol Summary .................................................................................................................. …v 
 
1 
Key Roles ........................................................................................................................... 1 
2 
Background Information and Scientific Rationale................................................................ 2 
2.1 
Background Information .......................................................................................... 2 
2.2 
Scientific Rationale ................................................................................................. 7 
2.3 
Potential Risks and Benefits ................................................................................... 7 
2.3.1 
Potential Risks ............................................................................................ 7 
2.3.2 
Known Potential Benefits............................................................................. 7 
3 
Objectives and specific aims .............................................................................................. 8 
4 
Study Design ...................................................................................................................... 9 
5 
Study population............................................................................................................... 10 
5.1 
Selection of the Study Population ......................................................................... 10 
5.2 
Inclusion/Exclusion Criteria ................................................................................... 10 
6 
Research Methods ........................................................................................................... 11 
6.1 
Study Outcome Measures .................................................................................... 11 
6.2 
Therapy Exposure Rules and Definitions .............................................................. 11 
6.3 
Attribution of Events to Exposure Groups ............................................................. 13 
6.4 
Control of Confounding ......................................................................................... 13 
6.5 
Statistical Analysis ................................................................................................ 16 
6.6 
Sample Size Considerations ................................................................................. 16 
6.7 
Sensitivity Analyses .............................................................................................. 17 
6.8 
Missing Data ......................................................................................................... 18 
6.9 
Validation Registry Linkage Substudy ................................................................... 18 
7 
Privacy, confidentiality, and data security ......................................................................... 20 
7.1 
Human Subjects Considerations ........................................................................... 20 
7.2 
Protection Against Risk ......................................................................................... 20 
7.3 
Data Safety and Monitoring................................................................................... 22 
8 
Informed Consent Process ............................................................................................... 23 
8.1 
Informed Consent Process ................................................................................... 23 
9 
Literature References ....................................................................................................... 24 
 
 
SUPPLEMENTS/APPENDICES 
 

 
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List of Abbreviations 
 
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BRR 
Bronchiectasis Research Registry 
BRRC 
Bronchiectasis Research Registry Consortium 
BTS 
British Thoracic Society 
COPD 
Chronic Obstructive Pulmonary Disease 
CF 
Cystic Fibrosis 
CMS 
Centers for Medicare and Medicaid 
ICS 
Inhaled Corticosteroids 
HIV 
Human Immunodeficiency Virus 
 
 
 
 
 
 
 
 
 
 
IRB 
Institutional Review Board 
MAC 
Mycobacterium Avium Complex 
NTM 
Nontuberculous Mycobacteria 
 
 
OHSU 
Oregon Health & Science University 
PHI 
Protected Health Information 
PI 
Principal Investigator 
PPV 
Positive Predictive Value 
QT 
QT interval (a measure of the time between the start of the Q 
wave and the end of the T wave in the heart's electrical cycle) 
UAB 
University of Alabama at Birmingham 
VPN 
Virtual Private Network 
 
 
 
 
 

 
 
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Protocol Summary 
 
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iv 
Title: Comparative effectiveness and safety of inhaled corticosteroids and antimicrobial 
compounds for non-CF bronchiectasis 
 
 
Population: Medicare patients with non-cystic fibrosis (non-CF) bronchiectasis.  
 
Study Duration: 3 years 
 
 
Objectives: 
 
Primary: 
• 
Among a national cohort of non-CF bronchiectasis patients, we will compare the relative 
safety of inhaled corticosteroids (ICS) and macrolide therapy with regards to acquisition of 
pulmonary nontuberculous mycobacterial (NTM) disease and prevention of hospitalized 
respiratory infection. 
 
Secondary: 
• 
Secondary safety outcomes include sudden cardiac arrest, hearing loss, bone loss, and 
other opportunistic infections.   
• 
Secondary effectiveness outcomes of importance as identified by preliminary patient input 
will include all-cause death, all-cause hospitalization, and hemoptysis. 
 
 
 

 
 
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1 
KEY ROLES 
Individuals: Principal Investigator:  Kevin L. Winthrop, MD, MPH 
 
Co-investigator: 
Emily Henkle, PhD, MPH 
 
 
 
Co-investigator: 
Jeffrey Curtis, MD, MS, MPH 
 
Co-investigator: 
Charles Daley, MD 
 
Co-investigator: 
David Griffith, MD 
 
Co-investigator: 
Elisha Malanga 
 
 
Study Statistician: 
Lang Chen, MD 
 
 
 
 
 
Institutions: Oregon Health & Science University 
 
University of Alabama at Birmingham 
 
National Jewish Health 
 
University of Texas Healthcare Northeast 
 
COPD Foundation 
 
NTM Info & Research 
 
 

 
 
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2 
BACKGROUND INFORMATION AND SCIENTIFIC 
RATIONALE  
2.1 
Background Information  
 
2.1.1. Non-cystic fibrosis bronchiectasis: Non-cystic fibrosis (CF) bronchiectasis is a rare lung 
disease of increasing incidence, causing chronic morbidity and disability in an estimated 
100,000 Americans over age 65.1,2 Patients with bronchiectasis suffer chronic productive cough, 
debilitating weakness and fatigue, dyspnea, hemoptysis, and are at high risk for recurrent 
pneumonia and death due to lung infections.  Mortality is 3-4% per year, and 1-year mortality 
after hospitalization for pneumonia increases to 13%.3-6  The effect upon quality of life is 
marked, and chronic disability can greatly diminish individual participation in social, 
occupational, and recreational settings. The quotes below taken directly from our pilot survey of 
non-CF bronchiectasis patients highlight patient concerns about their bronchiectasis diagnosis: 
 
(1) “My biggest concern is that it increases my risk of repeated infection.  And there is nothing I can do to 
reverse the damage.” 
(2) “One of the most frustrating symptoms of my bronchiectasis in the past two years is that my hemoptysis 
episodes have gone from about 2 or 3 a year to over 15 a year. It is a ‘symptom’ that is so 
unpredictable.” 
(3) “Dismay of lack of understanding by medical practice, lack of understanding about degree of 
seriousness.”  
(4) “The chance of [nontuberculous mycobacteria] recurrence is on my mind even when [feeling] well now” 
 
While the etiology of this chronic inflammatory disease is multi-factorial, it is characterized by 
airway inflammation and excess mucous production.  This in turn leads to a vicious cycle of 
colonization and infection with microbial organisms that promote inflammation, drive progression 
of airway damage, and cause frequent exacerbations.7  Exacerbations are a regular 
complication characterized by increased respiratory symptoms and decreased lung function and 
often result in hospitalization.  Important pathogens in this setting include nontuberculous 
mycobacteria (NTM) such as Mycobacterium avium complex (MAC) and Mycobacterium 
abscessus, as well as bacterial organisms such as Pseudomonas aeruginosa and Haemophilus 
influenzae. 4  While strategies exist to manage colonizing bacteria such as pseudomonas, 
chronic NTM disease eventually occurs in a substantial proportion of bronchiectasis patients 
resulting in long-term disability and the need for chronic, often life-long, therapy with multiple 
antibiotics.4,6   
 

 
 
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2.1.2 Pulmonary NTM disease (Criterion 1): NTM are ubiquitous environmental organisms found 
in water distribution systems and soil that cause chronic, debilitating pulmonary disease 
primarily in those over age 40.8,9 Patients typically suffer from chronic cough, wheezing, 
dyspnea, fatigue, night sweats, weight loss, depression, social anxiety, hemoptysis, and other 
symptoms.  Pulmonary NTM disease is also a rare disease that is increasing in the U.S, with an 
estimated 50,000 prevalent cases identified in patients over age 65 from 1997 through 2007 
during a review of Medicare databases.10-12  NTM incidence estimates range from 15.5-
26.7/100,000 in those over 50 years of 
age.12-14  Disease disproportionately affects 
females, incidence increases with age (see 
Figure 1), and occurs more frequently with 
chronic underlying lung disease such as 
chronic obstructive pulmonary disease 
(COPD) and bronchiectasis where lung 
architectural distortion puts them at high 
risk for collecting pathogens from the 
environment like NTM.12,14  An increasingly 
predominant clinical presentation is 
generally a slender, older female (“Lady 
Windermere”), with interstitial nodular 
infiltrate, bronchiectasis in the right middle 
lobe or lingula, or bronchiolitis (“tree in 
bud”) on high resolution CT scan (see 
Figure 2).9,15,16 NTM infection is destructive 
and its associated inflammation drives both 
the development and progression of airway damage and dilatation. NTM can both cause 
bronchiectasis, as well as secondarily infect those already with the disorder. In our prior study in 
Oregon, as well as a Danish case-control study, NTM was strongly associated with non-CF 
bronchiectasis.14,17 An estimated 2-10% of non-CF bronchiectasis patients will be infected with 
NTM at any given time and in one retrospective study, 30% were diagnosed with NTM disease 
over a 7-year period.18-21 NTM therapy is prolonged and difficult.  It typically includes the use of 
3 or 4 concurrent antibiotics as part of an azithromycin- or clarithromycin-based regimen for 18-
24 months with a low chance of cure.9 Many patients fail therapy and over half will have either 
recrudescent NTM disease or new NTM infection from the environment after completing such 
therapy. Surgical resection is sometimes necessary, and antibiotic therapy is often difficult to 
tolerate.  Given the morbidity of NTM disease and its treatment, and its close relationship with 
bronchiectasis, prevention of NTM disease is of great importance among these high risk 
individuals.  
 
Figure 1.  Average Pulmonary NTM Prevalence by Age 
and Sex in Two U.S. Healthcare Organizations 

 
 
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2.1.3. Non-CF Bronchiectasis Treatment: At present, pharmacologic strategies are commonly 
employed to treat non-CF bronchiectasis, yet very little effectiveness or safety data exist to 
guide patient-centered decision making. The goals of treatment are to improve symptoms, 
reduce airway inflammation, limit further bronchiectasis progression, and prevent chronic lung 
infection and acute symptomatic infectious exacerbations.  We believe that some of the 
therapies frequently employed in this setting, however, might actually harm patients increasing 
the risk of hospitalization due to respiratory infections, and importantly, increasing the risk of 
pulmonary NTM disease.  There are no published U.S. guidelines for non-CF bronchiectasis 
treatment, but in 2010 the British Thoracic Society (BTS) produced guidelines summarizing 
current therapies.22  These therapies include oral or inhaled corticosteroids, oral or inhaled 
antibiotics, and techniques to promote airway hygiene/mucous clearance (Table 1).  
 
Most of these therapies have received little research attention within non-CF bronchiectasis and 
the proposed benefits/risks of their use have been extrapolated from other settings of chronic 
lung disease like COPD or CF. However, the pathophysiology and natural history of non-CF 
bronchiectasis is distinct from these and other lung diseases, and therapies that benefit one do 
not necessarily benefit the other.  For example, DNAse, which improves mucus clearance and 
outcomes in CF bronchiectasis patients, actually caused worsened lung function in non-CF 
bronchiectasis patients.23,24  This underscores the importance of evaluating therapies within the 
setting of non-CF bronchiectasis, rather than extrapolating from studies performed in other 
chronic lung disease conditions.   
 
 
 
 
Figure 2.  Computed tomography (CT) scan of patient with 
chronic M. avium complex disease in the right middle lobe 
in which bronchiectasis and inflammatory infiltrate are 
evident (arrow)

 
 
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Table 1.  Pharmacotherapies used in bronchiectasis 
Anti-inflammatory 
Bronchodilators 
Antibiotics 
Mucous 
clearance 
Inhaled steroids 
beclomethasone 
Short 
acting 
albuterol 
azithromycin 
clarithromycin 
inhaled 
hypertonic saline 
budesonide 
levabuterol 
erythromycin 
flunisolide 
pirbuterol 
inhaled tobramycin 
 
fluticasone 
Long acting 
formoterol 
inhaled colistin 
 
mometasone 
tiotropium 
inhaled gentamycin 
 
triamcinolone 
salmeterol 
inhaled aztreonam 
 
Combined 
steroid/ 
bronchodilator 
ipratratropium/ 
albuterol 
 
 
 
 
budesonide/ formoterol 
 
 
 
 
fluticasone/ salmeterol 
 
 
 
 
Oral steroids 
prednisone 
 
 
2.1.4. Oral and Inhaled corticosteroids (ICS): There is no evidence that long-term use of 
corticosteroids benefit patients with non-CF bronchiectasis, and their potential risks have been 
poorly studied in this setting.  Yet, use of corticosteroids among such patients is common.  
Corticosteroids are prescribed short-term to treat bronchiectasis exacerbations, but are also 
frequently prescribed chronically in an attempt to limit inflammation and slow bronchiectasis 
progression. There is evidence that ICS therapy reduces exacerbations and slows the decline of 
quality of life in patients with advanced COPD.25 However, the BTS guidelines and a more 
recent U.S. review of therapies suggest there is a lack of evidence to support the chronic use of 
oral or ICS in bronchiectasis.20,22  To date, no large clinical trials have looked at ICS use in non-
CF bronchiectasis.  Several small randomized trials in patients with multiple exacerbations in 
the prior year have suggested an improvement in symptoms in patients receiving ICS for 6-12 
months, but no difference in other outcomes when compared to placebo.26-28   
 
To our knowledge, there are no long term studies specifically evaluating the risks of oral 
steroids or ICS in non-CF bronchiectasis patients.  Such therapy increases the risk of 
opportunistic infections due to mycobacteria, fungus, and other pathogens.29  Further, we and 
others have identified both oral steroids and ICS as likely risk factors for acquiring pulmonary 
NTM disease.  In Oregon we identified high rates (16%) of oral corticosteroid use among 
patients with pulmonary NTM, and a later case-control study suggested a nearly 8 fold higher 
risk for NTM in users of oral corticosteroids.14,17 In addition, there is data in other types of 
chronic lung disease suggesting that oral steroid or ICS use increases the risk of infection, 
particularly pneumonia,30 an outcome for which non-CF bronchiectasis patients are already at 
increased risk. In COPD patients a population-based study identified a RR of 1.69 (95% CI 
1.63-1.75) for serious pneumonia in current ICS users.31  A recent meta-analysis concluded that 
budesonide and fluticasone, with or without long-acting bronchodilators, are associated with 
increased risk of serious adverse pneumonia events in COPD patients but not an increased risk 
of death.32  Although subject to some controversy, inhaled corticosteroids have been also been 
associated with bone loss in asthmatic and COPD patients, similar to oral corticosteroids.33   
 

 
 
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2.1.5. Antibiotic strategies: There is limited evidence that long-term use of antibiotics benefit 
patients with non-CF bronchiectasis, and the risks of such therapy are poorly defined.  
Antibiotics are used with the twin goals of reducing bacterial load in the airways, which in turn 
reduces airway inflammation, and exerting an immunomodulatory role independent of bacterial 
load reduction.34  Macrolides (erythromycin, azithromycin) are oral antibiotics that exhibit 
immunomodulatory effects that may have clinical benefits for a variety of diseases associated 
with airway inflammation including non-CF bronchiectasis patients.35  Two small randomized 
clinical trials evaluated long term (6 months to 1 year) azithromycin and a third evaluated long 
term erythromycin use in non-CF bronchiectasis patients. While macrolide-treated groups 
experienced fewer respiratory exacerbations, defined as increasing symptoms (requiring 
treatment in two studies), there was limited or no improvement in overall lung function.36-38  One 
additional advantage of azithromycin (but not erythromycin) is its antimicrobial activity against 
NTM such that its long-term use theoretically could be protective against NTM.  Only one study, 
to our knowledge, has evaluated this idea, and it was conducted in the CF setting.  Investigators 
using case-control methods found CF patients who developed NTM were less likely to be using 
macrolides in the prior year.39 A potential danger of macrolide monotherapy, however, is the 
development of macrolide resistance in those already infected with NTM.40  Accordingly, 
macrolide monotherapy is not to be used in the treatment of pulmonary NTM disease (similar to 
tuberculosis where multiple drugs are used to diminish the evolution of drug resistance).9 
Further, macrolide resistance in other important organisms can occur (e.g. S. pneumonia) and 
the two macrolide RCTs in non-CF bronchiectasis patients routinely tested for it during the trial 
found higher rates of macrolide-resistant non-NTM pathogens in the macrolide treatment 
groups.36,37 One of these studies excluded patients with NTM and neither reported any data on 
NTM isolates or resistance.    
 
Aerosolized antibiotics, including tobramycin (and less commonly aztreonam, colistin, and 
gentamycin) are frequently prescribed in the CF setting primarily for those colonized with 
Pseudomonas and potentially could be useful in non-CF bronchiectasis.  A small trial evaluated 
inhaled tobramycin versus placebo in 30 non-CF bronchiectatics for 6 months and found it was 
associated with a reduction in hospital stays, but there was no difference in exacerbation 
frequency or quality of life measures.41  While these trials highlight a potential benefit of oral or 
inhaled antibiotics for non-CF bronchiectasis patients, they are small and of inadequate duration 
to provide the essential information regarding benefits and risks of longer courses of treatment.   
 
In general, the relative risks of antibiotic therapy compared to benefits are not clear.  
Azithromycin use has been linked to sudden cardiac death in population-based studies42, and it 
is known to cause QT prolongation with potential for causing other cardiac arrhythmias, 
particularly when used with other drugs that also cause QT prolongation.43  Further, as 
previously mentioned, a significant risk of long-term azithromycin use would be the selection of 
macrolide-resistant NTM in patients colonized with NTM.  NTM patients with M. avium resistant 
to macrolides who remained culture positive after subsequent therapy had a 1–year mortality 
rate of 34%.40  Accordingly, it is recommended that CF and non-CF bronchiectasis patients be 

 
 
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screened for NTM prior to adopting long-term macrolide strategies.22,40  Inhaled tobramycin, an 
aminoglycoside antibiotic, is associated with an increased risk of bronchospasm, dyspnea, 
wheezing, and chest pain compared to placebo, which may precipitate discontinuation of the 
drug and impair quality of life.44  Potentially other more serious side effects of inhaled 
aminoglycoside use include permanent ototoxicity and renal failure, effects seen with parenteral 
use but which have received very limited study to date with inhaled use.45 
2.2 
Scientific Rationale  
 
Non-CF bronchiectasis is a rare chronic disease for which little data exist to inform patients and 
physicians regarding treatment decisions.  We believe findings from our study will assist 
patients and their physicians in choosing safer therapies that diminish their risks of negative 
outcomes identified as of high importance to them, including the development of complicated 
opportunistic infections such as NTM infection, hospitalized respiratory infections, all-cause 
hospitalization, death, and others.  We hypothesize that antibiotic therapy protects against these 
outcomes, and that the relative risks of ICS do not support the widespread use of such therapy 
among non-CF bronchiectasis patients today.   
2.3 
Potential Risks and Benefits 
2.3.1 Potential Risks 
 
This is a minimal risk study.  There is a small risk of breach of confidentiality. 
2.3.2 Known Potential Benefits 
 
Medicare patients will not benefit directly from the study since they will remain anonymous.  
However, subjects may benefit in the future since the project may provide information on 
relative risks and benefits of common therapies which will impact decision-making around 
treatment for non-CF bronchiectasis patients. 
 

 
 
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3 
OBJECTIVES AND SPECIFIC AIMS 
We propose to evaluate the benefits and harms of the most common therapies prescribed for 
non-CF bronchiectasis using a large cohort of bronchiectasis patients identified within Centers 
for Medicare and Medicaid Services (CMS) data, with linkage to a national non-CF 
bronchiectasis registry to validate outcomes lacking prior validation. Our patient co-investigators 
and stakeholders have identified the two most common therapies used in this disease, ICS and 
suppressive macrolide antibiotics, and they have identified the potential benefits and risks of 
these therapies of greatest importance to them. Together, we hypothesize that ICS use leads to 
increases in hospitalized pulmonary infections, death, and opportunistic infection due to NTM, 
while macrolide use does not.  Our proposed study evaluates the relationship of ICS and 
macrolide use and the relative risk of these and other patient-identified outcomes.  We will work 
with patients and clinical stakeholders to interpret and disseminate results from the study.   
 
Specific Aim 1: Among a national cohort of non-CF bronchiectasis patients, we will compare 
the relative safety of ICS and macrolide therapy with regards to acquisition of pulmonary NTM 
disease.  Secondary safety outcomes include sudden cardiac arrest, hearing loss, bone loss, 
and other opportunistic infections.   
 Our primary hypothesis is that ICS, compared to macrolide therapy, is associated with an 
increase in the relative risk of NTM disease 
 
Specific aim 2: Among a national cohort of non-CF bronchiectasis patients, we will compare 
the effectiveness of ICS and macrolide therapy with regards to prevention of hospitalized 
respiratory infection.  Secondary effectiveness outcomes of importance as identified by 
preliminary patient input will include all-cause death, all-cause hospitalization, and hemoptysis. 
 Our primary hypothesis is that ICS, compared to macrolide therapy, is associated with an 
increase in the relative risk of hospitalized respiratory infection 
 

 
 
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4 
STUDY DESIGN 
The study is a retrospective cohort design conducted using Medicare data from 2006-2014.  
Among a national cohort of non-CF bronchiectasis patients, we will evaluate and compare the 
clinical effectiveness and safety of long-term ICS and macrolide antimicrobial therapies.  The 
proposed specific aims were generated based on our initial assessment of patient priorities 
(prevention of NTM infection, hospitalized respiratory infection, all-cause death, all-cause 
hospitalization, and hemoptysis) and operationally defined based on our prior work with CMS 
and other large administrative databases.  Analyses will be conducted within a primary cohort of 
non-CF bronchiectasis patients.  In some cases, outcomes could be considered both 
“effectiveness” and “safety” outcomes (e.g. prevention or acquisition of NTM disease) and 
hence there is overlap in the concepts of effectiveness and safety within the protocol.  Our 
analytic approach is similar and discussed jointly in Section 7.   
 

 
 
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5 
STUDY POPULATION 
5.1 
Selection of the Study Population 
 
Complete national 2006-2014 Medicare data from Part A, B and D will be obtained from CMS.  
We will use ICD-9 code 494.0 (bronchiectasis without acute exacerbation) to identify patients 
with bronchiectasis within Medicare.  This code has been used to evaluate trends in 
bronchiectasis diagnosis and hospitalization in Medicare patients in previously published 
studies, although its validity has not been determined to our knowledge.1,46  Accordingly, we 
conducted a pilot validation study at three geographically varied sites (medical centers in 
Portland, OR, Tyler, TX, and Denver, CO)  in which we randomly selected across the three sites 
a total of 290 electronically identified outpatients over age 18 with 494.0 diagnoses.  Using 
chest CT scan as a gold standard, we found the positive predictive value (PPV) of a single 
494.0 code for bronchiectasis to be 90.3% (95% CI 86.4-93.2%).  In addition, 100 inpatient 
records with a primary or secondary discharge diagnostic code for bronchiectasis (494.0) were 
reviewed at OHSU, with an observed PPV of 95.8% (95% CI 89.8-98.4).  The vast majority of 
reviewed patients in this validation exercise were Medicare patients, and we believe it is likely 
that this code has similar validity at other medical centers caring for Medicare patients. 
However, we acknowledge that it is possible the PPV may differ by medical system.   
5.2 
Inclusion/Exclusion Criteria 
 
In order to potentially further increase the PPV for identifying bronchiectasis patients in our 
cohort study, we will define bronchiectasis patients as those patients with any pulmonologist -
recorded 494.0 ICD-9CM. 
From this identified bronchiectasis cohort, we will exclude patients with cystic fibrosis (ICD-9 
codes 277.00-277.09), HIV infection (042), and a history of organ transplant (V42.0, V42.1, 
V42.6, V42.7, V42.8).   Rationale for exclusion includes the fact that such patients are 
fundamentally different than non-CF bronchiectasis patients who lack these factors with regard 
to their risk for infection, hospitalization, and many of the outcomes under study in this protocol. 
 
 

 
 
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6 
RESEARCH METHODS 
6.1 
Study Outcome Measures 
Table 2 lists the validated operational algorithms used to identify key outcomes already 
identified in the literature or by patient ranking.  Primary outcome events will be the date of NTM 
infection (Aim 1) and hospitalized respiratory infection (Aim 2).  Secondary outcomes will 
include all-cause mortality, all-cause hospitalization, hemoptysis, and hip fracture included as a 
result of patient input.  Secondary safety outcomes of concern include the potential adverse 
events of cardiovascular events (arrhythmia, myocardial infarction), hearing loss, and 
opportunistic infection.   
 
Table 2.  Proposed outcomes and operational definitions 
Diagnosis 
Algorithm/ ICD-9-CM  
Principle Diagnostic Codes 
Validation Studies   
                         Pulmonary Events 
Non-tuberculosis 
mycobacteria (NTM) infection 
031 (inpatient or outpatient) 
Winthrop 201147, Winthrop 201348 
Hospitalized respiratory 
infection 
480-487.0 (inpatient) 
Jackson 200349, Grijalva 200850 
Hemoptysis 
786.3 (inpatient) 
Not validated (see section 6.3.8) 
Other Safety/Effectiveness Events 
All-cause hospitalizations 
- 
- 
Death 
- 
- 
Opportunistic infection (other 
than NTM) 
See Appendix A 
See Appendix A 
Arrhythmia 
427.x (principal diagnosis) 
Tamariz 201251 
Myocardial infarction 
410.X1 
Kiyota 200452 
Hip fracture 
820.0x, 820.20, 820.21, 
820.22, 820.8 821.0x, 
Narongroeknawin 201253 
Sensorineural hearing loss 
389.1 [except 389.13, .15, .16 
and .17], 389.2 [except 
389.21] 
not validated 
6.2 
Therapy Exposure Rules and Definitions 
In this protocol, we borrow from established methodology and our experience conducting prior 
pharmacovigilance studies using Medicare and other administrative databases evaluating the 
comparative effectiveness and safety of immunosuppressive therapies used in other 
inflammatory diseases.54-57 Table 3 lists key terminology and definitions, further described 
below. 
 
 
 

 
 
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Table 3.  Terms and operational definitions 
Term 
Definition 
Cohort inception date 
Date of first treatment with ICS or macrolide  
Baseline period 
12 months prior to cohort inception OR later treatment episode start date 
Clean period 
12 months prior to cohort inception OR later treatment episode start date 
New use 
Absence of prescription for medication group in question during clean 
period 
Treatment episode 
>28 day supply of either an ICS or macrolide regimen, through 30 days 
after switch or no refill of prescription 
Treatment episode start 
date 
First date of prescription for >28 day supply of either an ICS or macrolide 
regimen 
Discontinuation date 
Sum of days supply + 30-day grace period 
 
Similar to these studies, we will allow patients who have a qualifying treatment episode (defined 
below) of chronic ICS or chronic macrolides to enter the study cohort on the date of first receipt 
of that therapy (“cohort inception date”).  Only new users will be included in the analysis, with 
“new use” as defined as an absence of prescriptions for the specific medication group in 
question during a “clean period” of 12 months prior to inception date. National Drug Codes, 
obtained from First Databank by UAB, will be used to identify drugs of interest.   
 
As described in Table 1, ICS include beclomethasone, budenoside, flunisolide, fluticasone, 
mometasone, triamcinolone, ipratratropium/ albuterol, budesonide/formoterol, or 
fluticasone/salmeterol.  We will lump these therapies and analyze them according to effective 
steroid dose. Antibiotics of interest include the macrolides (oral azithromycin, clarithromycin, 
and erythromycin).  A qualifying “treatment episode” will be defined as >28 day supply of 
either an ICS or macrolide regimen, with the first date of prescription receipt defined as the 
“treatment episode start date.”  A treatment episode ends when the patient (1) switches to a 
new regimen that meets a different operational definition or (2) does not refill the prescription or 
another within the same exposure category (i.e. ICS or macrolides) within 30 days after the end 
of the drug supply.  The pharmacy variable “days-supply” will be used to estimate the intended 
duration of each prescription. The “discontinuation date” is defined as the sum of (days supply 
+ 30-day grace period).  Patients may re-enter the cohort as new users.  Patients who leave the 
drug exposure cohort can subsequently contribute new episodes of medication use if selection 
criteria (including “clean period”) were re-fulfilled and can potentially contribute episodes to 
more than one exposure group.   
 
For patients who switch therapies within their exposure group prior to the discontinuation date, 
they will be considered to have continuous exposure.  This is very unlikely for macrolides, 
however, as almost all patients will have used azithromycin (erythromycin and clarithromycin 
use in suppressive fashion is rare) and are unlikely to switch to other macrolides.  Although also 
unlikely, a patient starting erythromycin might switch to azithromycin prior to the discontinuation 
date. In this case, the patient will be considered to have continued macrolide exposure (i.e. the 
same treatment episode).   Rules around ICS exposure and product switches within that 

 
 
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exposure group will be handled similarly, and exposure measured as effective steroid dosage 
(see Section C20). 
6.3 
Attribution of Events to Exposure Groups 
 
Exposure time will begin at the “treatment episode start date”. The follow up will end at the 
earliest date of death, first outcome occurrence, end of study period, lost coverage, or treatment 
switch or discontinuation.  Since patients rarely start or switch therapies at random, and they 
may, in fact, change or stop therapies based upon prodromal symptoms (e.g. chest pain 
consistent with angina), the length of the at-risk period will continue through the 
“discontinuation date” (30 days beyond the exhaustion of the drug supply). Events occurring 
in this at-risk period will be attributed to the exposure, however for NTM infection outcome we 
will perform sensitivity analyses using a longer window of “at risk” time, specifically of 90 days 
after the exhaustion of drug supply.  We believe this is appropriate given the length of time it 
can sometimes take to diagnose NTM disease (e.g. cultures take 4-6 weeks to grow after 
collection).  For all outcomes under study, except hospitalized respiratory infection, patients 
must have no evidence (inpatient or outpatient ICD-9 code) of the condition of interest during 
the 12 month clean-period in order to be included within that specific analysis. For example, 
only patients without evidence of NTM by ICD-9 code within the baseline period will be included 
into the analyses of NTM incidence.     
 
Because this study is an evaluation of “real world” effectiveness and safety, we expect that 
some patients who start one exposure will at some point add the alternate exposure.  Our data 
suggests that concurrent use is uncommon and <10% of ICS users are taking macrolides at the 
same time.  For example, a patient who starts ICS might add macrolides after 3 months of ICS 
therapy.  From that time on, they are exposed to both until one therapy is discontinued. For 
such patients, in our primary analysis we will right censor patients prescribed both ICS and 
antibiotics at the time of the patient meeting a qualifying comparison treatment episode.  In 
sensitivity analyses, however, we will not right censor them at this time, but rather analyze such 
patients in a third category of combined ICS and antibiotic use until one or both therapies are 
discontinued. 
6.4 
Control of Confounding 
 
We will collect baseline clinical and demographic data during the 12 month baseline 
period prior to treatment episode start date.  These covariates are listed in Table 4 and are 
summarized here, and are adapted from our previous experience with RA and other 
inflammatory disease cohorts within Medicare. Descriptive analyses will be performed for all 
variables in Table 4 for the full bronchiectasis cohort and the “new user” treated cohort. 
Demographics include age, sex, median household income (census block group with linkage of 
zip code), nursing home/community residence, rural/urban residence, and state of residence (to 

 
 
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generate geographic region of residence: Midwest, Northeast, South, and West). We will collect 
the number of prior hospitalizations (with or without infection) and physician and pulmonologist 
office visits. Patients using oral corticosteroids for at least 90 days continuously prior to index 
date will be categorized as baseline systemic corticosteroid users (yes or no). For all baseline 
systemic corticosteroid users, we will calculate a mean outpatient prescribed daily dose of 
prednisone equivalents in the 6 months prior to index date: less than 5 mg/d (low dose), 5 to 
less than 10 mg/d (medium dose), and 10 mg/d or more (high dose).57 The total number of 
antibiotic prescriptions during the baseline period will be obtained, including those typically used 
to treat acute respiratory infection (erythromycin, azithromycin, clarithromycin, inhaled 
tobramycin, levofloxacin, moxifloxacin, ciprofloxacin, amoxicilin, amoxicilin/clavulanate, or 
doxycycline).  The Charlson comorbidity index will be calculated at the date of cohort 
inception.58,59  Comorbidities will include the following: COPD, asthma, lung cancer, alpha-1 
antitrypsin deficiency, interstitial lung disease, pulmonary fibrosis, primary immune deficiencies 
(including common variable immune deficiency), primary ciliary diskinesia, allergic 
bronchopulmonary aspergillosis, silicosis, and rheumatoid arthritis.  Pre-existing NTM disease 
within the baseline period will be identified using ICD-9 codes for NTM (031) or any prescription 
for ethambutol (an antibiotic specific to tuberculosis or NTM therapy), and pre-existing 
Pseudomonas disease identified using ICD-9 codes (041.7, 482.1, 008.42).   
 
Table 4. Baseline demographic and clinical covariates for use in propensity score 
calculation within the Medicare bronchiectasis cohort   
Variable 
Proposed Definition 
Notes 
Age 
Log continuous variable  
 
Sex 
Male or female 
 
Median household income 
Census block group with linkage of zip code 
 
Nursing home residence 
Nursing home or community 
 
Rural/urban 
Rural or urban 
 
Region of residence 
Geographic region of residence: Midwest, Northeast, South, 
West 
 
Cohort calendar year 
2006-2014 
 
Number of physician office 
visits 
Continuous variable 
 
Any hospitalization (incl. 
infections) 
Continuous variable 
 
Hospitalization for non-
infections 
Continuous variable:                                          
 
Number of unique medication 
classes 
Continuous variable or categorical based on the distribution 
 
Total number of antibiotic 
prescriptions 
erythromycin, azithromycin, clarithromycin, inhaled tobramycin, 
levofloxacin, moxifloxacin, ciprofloxacin, amoxicilin, 
amoxicilin/clavulanate, or doxycycline 12 months prior to index 
date 
 
Total number of acute 
exacerbations 
Number of antibiotic prescriptions (see above) for >7 but <28 
days 
Added 7/25/16 
Oral corticosteroid use 
Yes/no 
 
Mean oral corticosteroid dose 
mean outpatient prescribed daily dose of prednisone equivalents 
in the 6 months prior to index date: less than 5 mg/d (low dose), 
5 to less than 10 mg/d (medium dose), and 10 mg/d or more 
(high dose) 
Ref: Winthrop KL, et 
al. JAMA 
2013;309:887-95 
Oxygen tank prescription 
Yes/no 
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Frequency of encounters 
Number of dates with an outpatient physician encounter resulting 
in a diagnosis in the 12 months prior to the start of follow-up 
exclusive of emergency department encounters 
Ref: Hoangmai H et 
al. Pham JAMA. 
2005;294(4):473-481 
 
Frequency of pulmonologist 
encounters 
Number of dates with an outpatient pulmonologist encounter 
resulting in a diagnosis in the 12 months prior to the start of 
follow-up exclusive of emergency department encounters 
 
Charlson comorbidity score 
'001',  '002', '003',  '004', '005',  '008',  '009',  '032.81', '034', '035',  
‘036.0', '036.1', '036.42', '038',  '040',  '040.81',   '041',  '049.0', 
'056.71',   '077.9',    '091.81',   '093.2','094.2',    '094.81',  '095.2',    
'098.5',    '098.82',   '098.84',   '130',  '131.03',   '289.5',    '289.59', 
'320',  '323',  '324',  '372.0',    '376.03',   '381.5',    '382',  '383.0',    
'383.1',    '383.9',    '391.1', '397.9',    '421',  '421.9',    '422.92',   
'461',  '462',  '463',  '464.0',    '465',  '466',  '472',  '473','475',  
'478.21',   '478.22',   '478.24',   '481',  '482',  '483',  '485',  '486',  
'510',  '513',  '526.4','528.3',    '540.1',    '566',  '567',  '569.5',    
'569.61',   '572',  '574',  '575',  '576.1',    '590',  '597','599.0',    
'601',  '608.4',    '611.0',    '614.3',    '614.4',    '681',  '682',  
'686.1',    '686.8',    '686.9','711.0',    '711.9',    '728.86',   '728.86',   
'730.0',    '730.1',    '730.2',    '785.4',    '790.7',    '958.3','98.12',    
'98.32',    '996.6',    '998.5' 
Ref: Deyo RA, 
Cherkin DC, Ciol MA. 
Adapting a clinical 
comorbidity index for 
use with ICD-9-CM 
administrative 
databases. J Clin 
Epidemiol 
1992;45:613–19. 
COPD/emphysema 
491.xx - chronic bronchitis, 492.xx - emphysema, 493.2 - chronic 
obstructive asthma, 496.xx - chronic airway obstruction, not 
elsewhere classified 
Ref: Cooke, C et al.  
BMC Health Serv 
Res. 2011 Feb 
16;11:37. doi: 
10.1186/1472-6963-
11-37. 
NTM infection 
031 or ethambutol prescription 
 
Pseudomonas infection 
041.7, 482.1, 008.42 
 
Asthma 
2 inpatient or 1 inpatient 493 
Ref: Gershon AS.  
Can Respir J. 2009 
Nov-Dec;16(6):183-8 
Lung cancer 
>1 162.x (not 162.0), 231.2 
 Ref: Ramsey SD et 
al.  J Manag Care 
Pharm. 2009 
Oct;15(8):659-68. 
Alpha-1 antitrypsin deficiency 
273.4 
  
Interstitial lung disease 
 >2 inpatient or outpatient ICD-9 codes (515, 516.3, 516.8, and 
518.89) given > 7 days apart 
Ref: Herrinton et al. 
PharmEpi Drug Saf. 
2013;22:394-402   
Primary immune deficiency 
279.x, excluding lymphoma/leukemia, HIV 
Ref: Resnick. J Clin 
Immunol. Jan 2013; 
33(1): 40–48.  
Primary ciliary dyskinesia 
759.3 
 
Allergic bronchopulmonary 
aspergillosis 
518.6 
 
Silicosis 
502 
 
Rheumatoid arthritis 
 
1+ inpatient or outpatient visit coded 714.xx AND a biologic or 
non-biologic DMARD  
Ref: Curtis J et al. 
Arthritis Rheum. 
2007;57(2):343-6 
Abbrev: ICD-9-CM, International Classification of Diseases, Ninth Revision, Clinical Modification ; PPV, positive predictive value; 
Ref, reference 
 
 
 

 
 
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6.5 
Statistical Analysis 
 
6.5.1 Crude incidence calculation: In separate outcome-specific models, we will calculate crude 
incidence using incident events divided by total exposed person-years for each exposed group 
(ICS or macrolides).  Exposed person-time will be calculated as the sum of the number of days 
between each treatment episode start date and censoring-date (time of event, death, end of 
exposure risk period [i.e. discontinuation date], or end of study time-period, whichever comes 
first).  Estimated rates per 100 person-years of follow-up and 95% confidence intervals will be 
calculated. 
 
6.5.2 Propensity score generation and primary analysis using multivariable Cox modeling: We 
will use propensity score methods to ensure ICS and macrolide exposure groups are similar in 
patient characteristics in an effort to minimize potential sources of confounding, including 
confounding by indication.  For each patient, we will calculate a propensity score (PS) in a  
logistic regression model using the full set of baseline demographic and comorbidity data 
including proxy measures for underlying bronchiectasis severity such as frequency of 
hospitalizations, pulmonary visits, respiratory antibiotic use, prednisone use, and others (Table 
4). This model will be used to estimate the probability a patient receives therapy with ICS, and a 
propensity score (PS score) for this outcome will be generated and given to each patient. PS 
scores will be reviewed for overlapping distributions between the primary comparison groups 
(ICS and macrolide  exposure) and the goodness of fit will be evaluated using the C statistic.54  
The PS score will be grouped into deciles and non-overlapping tails between exposure 
groupswill be trimmed so that only overlapping exposure cohorts will be compared.60  In our 
primary analysis, we will use Cox proportional hazard regression models to compare incidence 
of outcomes between new users of ICS and macrolides.61  Since patients could contribute ≥1 
episode of new use (with an updated set of covariates), we will use the Huber-White “sandwich” 
variance estimator and calculate robust standard errors for all estimates.62  We will recalculate 
the propensity score whenever the patient begins a new qualifying episode of the same 
treatment within the same analysis. The final disease specific-outcome models for cohort 
analyses will include the exposure group, propensity score decile, and baseline oral prednisone 
use (yes/no), as well as an indicator variable for inhaled antibiotic use (y/n) after cohort 
inception.  Although inhaled antibiotics are seldom used (inhaled tobramycin is commercially 
available for this purpose), it could be an important confounder so that we will control for it in 
this fashion.  All analyses will be done in SAS (SAS Institute Inc., Cary, N.C.). 
6.6 
Sample Size Considerations 
 
We define a priori that a 25% increase (hazard ratio of 1.25) is a clinically meaningful increase 
in the risk of a given outcome (hospitalization, death, etc.).  We expect to identify approximately 
100,000 bronchiectasis patients in the Medicare data.1  Based upon our preliminary patient 
survey data, we anticipate a minimum of 40% will be treated with ICS and 15% with macrolides 
each year resulting in an annual cohort of 55,000 treated patients.  If we identify 10,000 new 

 
 
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users of ICS and 4000 of macrolides, and conservatively assume 5% of patients on antibiotics 
are develop NTM infection, or a similar number are hospitalized each year for pneumonia (see 
preliminary data), we have 82% power to detect a HR of 1.25 comparing ICS to macrolides for 
either of these outcomes.  The test statistic used is the two sided Z test with pooled variance 
and the significance level of the test was targeted at 0.05.  Given that other outcomes under 
study are more frequent or of a similar frequency, we expect to be well powered to conduct the 
other primary and secondary analyses.   
6.7 
Sensitivity Analyses 
 
We do not have any planned subgroup or formal heterogeneity of treatment effects analyses.  A 
number of sensitivity analyses will be conducted. 
   
6.7.1. Alternative models: First, we will explore including additional variables individually within 
the models, beyond PS decile, baseline oral prednisone, and inhaled antibiotics.  Other factors 
that will be considered are age, sex, COPD, asthma, and pseudomonas colonization.  Although 
these factors are currently being controlled within the PS score, they are potentially strong risk 
factors and we will perform these sensitivity analyses to ensure they are adequately controlled 
for in this fashion.   
6.7.2. Macrolide only exposure: Second, although it is unlikely that a large number of patients 
will be using erythromycin, for the outcome of NTM disease we will conduct a secondary 
analysis limiting macrolide users to those starting azithromycin or clarithromycin, as 
erythromycin lacks antimicrobial activity against NTM.  
 
6.7.3. Dose response to steroids: Third, within the ICS category, there could be differential risk 
based upon the contained steroid dosage.  Using methods described previously, we will convert 
all ICS to fluticasone equivalent doses (fluticasone 50 mcg, budesonide 80 mcg, 
beclomethasone 100 mcg, triamincinolone, flunisolide 200 mcg).31 Treatment episodes will then 
be categorized as high (1000 mcg/day or more), medium (500-999 mcg/day), or low (<500 
mcg/day).   
 
6.7.4. Dual use of ICS and macrolides:  Fourth, we will look at the effects of concomitant 
exposure to ICS and antibiotics. Although preliminary data suggests a small percentage of 
patients using concomitantly (<10%), we will look at this subgroup individually. For this analysis, 
patients will be censored at the start of a concomitant qualifying therapy and reclassified into a 
third exposure group labeled “dual ICS/macrolides.”  They will be evaluated separately and 
censored according to the same rules as for the primary analysis.   
 
6.7.5. Inhaled antibiotics:  Finally, our patient partners have expressed an interest in lesser-used 
inhaled antibiotics (tobramycin and aztreonam). We will explore the incidence of events in new 

 
 
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users of these therapies, although given they are used less frequently we will likely lack 
statistical power to make formal comparisons with either ICS or macrolide users. 
6.8 
Missing Data 
 
Missing data constitute a very minor problem in the Medicare data system.  Medicare data 
covers adults age 65 and older and people under age 65 with certain disabilities and captures 
the entirety of beneficiaries’ healthcare encounters.  For example, no beneficiary has missing 
demographic information (gender, race, birth date, zip code).  No claim record has a missing 
claim date, all carrier and outpatient records have at least one non-missing diagnosis code, no 
carrier line record has a missing diagnosis code, only a few inpatient claims have a missing 
primary discharge diagnosis, and dispensing files have no missing values for product, quantity 
dispensed or days of supply.  We use SAS programs to determine the missing counts for each 
variable in each data file, in each year, used in our analyses as part of our standard operating 
procedures. We do not exclude beneficiaries because of missing or invalid data. If such data 
cannot be corrected, we exclude the affected claim (very often, there is a replicate claim 
containing valid data).  Therefore the use of these data minimizes problems of misclassification 
bias due to missing data on the primary outcomes, exposures, and confounders.   The BRR 
(described below) has excellent completeness, with less than 5% missing data for any given 
variable and most at 1% or less.  
6.9 
Validation Registry Linkage Substudy 
 
We plan to use the BRR as a validation cohort for previously unvalidated claims-based 
algorithms for secondary outcomes as described below.  The project will submit a finder file 
including subjects from participating BRRC sites to ResDAC containing the date of birth, sex, 
and social security number (SSN) to create a crosswalk to link BRR and Medicare data.  
Alternatively, if sites are unable to access full SSN data, we will perform probabilistic linkage 
using date of birth, sex, and physician visit dates or other dates that can be matched to claims 
dates and associated physicians.   The BRR, which collects detailed clinical, laboratory, 
microbiology, and radiology data from the medical record, will primarily be used to validate the 
claims-based algorithms for outcomes lacking prior validation: hemoptysis and pseudomonas 
colonization.  The gold standard for hemoptysis is clinical documentation of episodes requiring 
bronchial embolization or surgery.  For pseudomonas, the gold standard will be a positive 
culture result, present in about 25% of BRR patients.  In each case we will calculate the positive 
predictive value (PPV) and 95% CI for the ICD-9 codes as defined in Table 3.  If needed, we 
can explore additional algorithms including requiring two codes or in combination with pharmacy 
data if the PPV is determined to be too low.  In addition, we can confirm that patients were 
treated with the medications and determine how many pseudomonas and NTM infections were 
missed by administrative data.  This cohort will not only be valuable to validate exposures and 
outcomes within our study, but will serve future studies in the validation of various algorithms for 

 
 
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7 
PRIVACY, CONFIDENTIALITY, AND DATA SECURITY 
7.1 
 Human Subjects Considerations 
 
The study will be conducted in accordance with legal and regulatory requirements, as well as 
with scientific purpose, value and rigor and follow generally accepted research practices such 
as Good Pharmacoepidemiology Practices (GPP) issued by the International Society for 
Pharmacoepidemiology (ISPE), the International Society for Pharmacoeconomics and 
Outcomes Research (ISPOR) guidances, Pharmaceutical Research and Manufacturers 
Association (PhRMA) guidelines and similar.   
7.2 
Protection Against Risk 
 
8.3.1 Data storage: The release of personal information is highly unlikely as data safeguards will 
be maintained throughout the study. To protect against the risk of loss of confidentiality we will 
use a secure system of file storage.  The finder file to ResDAC containing identifiers including 
social security numbers will be used for creating a one-time data crosswalk to link BRR and 
Medicare data.  After the crosswalk is completed the finder file will be destroyed.  Any analytic 
data files, which will contain health information, will be coded and tracked by study identification 
number so that study staff will not have access to both identifiers and health information.  Any 
paper or electronic data linking study identification numbers, identifiers and health information 
will be kept secured and accessible only by authorized local personnel working on the study. All 
staff is required to sign pledges promising to maintain study subject confidentiality.   
 
8.3.2 Medicare data storage: Raw Medicare data will be stored and processed at UAB on a 
dedicated, layered-security system, which can be accessed only by designated project staff 
under the direct supervision of the site PI. Since the system is behind multiple firewalls, is 
monitored regularly, and is accessible only to key personnel, the risk of unlawful penetration is 
not a significant data safeguard concern. All applications are run on the server, thereby 
eliminating the need to house data on individual desktop or laptop computers that are generally 
more of a security risk.  Administrative access to databases and corresponding data will be 
limited to the analyst team using Remote Desktop Protocols and/or Virtual Private Network 
(VPN) technologies. Furthermore, all databases will reside behind industry-strength firewalls.  
 
Password protection will be used in additional places on the server for all transactions that allow 
entry and editing of data, provide access to sensitive subject data or administrative privileges. 
Passwords will be managed to require all users to change their password within 90 days and 
strict rules will be implemented to require strong passwords. Additionally, beneficiary contact 
information will be encrypted and write-protected to maintain data integrity. Data access will be 
limited to investigators and key study personnel. Prior to receiving PHI access, researchers 
must demonstrate completion of HIPAA training and abide by security procedures. 

 
 
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Administrative access to databases and corresponding data will be limited to the analyst team 
using Remote Desktop and/or Virtual Private Network (VPN) technologies. Furthermore, all 
databases will reside behind industry-strength firewalls. Individuals, such as analysts and 
statisticians, have access to the data for their specific projects. Other members of the research 
team who do not require access to the raw CMS data do not have access to the data. 
 
All output containing individual identifiable information is treated as confidential data. This 
information is never transferred electronically via email or other protocols. Shredders are used 
on any printed material containing individual identifiers. Printed tabular material will not contain 
cell sizes less than 11. At the conclusion of this study, a CMS “Certification of Destruction” 
certifying the proper destruction of all data obtained will be sent to CMS. 
8.3.3 BRR data: All electronic data for the Bronchiectasis Research Registry is housed at 
DatStat, a secure coordinating center.  Datstat maintain highest levels of research and human 
subject/HIPAA privacy protections as well as 21 CFR Part 11.  On the DatStat platform, all 
communication between the client browser and the web server is protected using SSL 
encryption. SLL Encryption will also be used for transmitting data to and from any external 
software systems and databases.  Additional measures to ensure the security of the data 
include: restricting access to users with valid IDs and passwords and using a hardware firewall 
to restrict access to the web server and database.  In accordance with DatStat standard 
operating procedures, system security logs and event logs are monitored daily to detect 
unauthorized attempts to access the system. DatStat’s follows internal Standard Operating 
Procedures and industry standard guidelines for securing data and services running its 
application.  These guidelines and standard operating procedures include, securing servers with 
complex, hard to guess passwords, processes to lock down and secure servers by changing 
administrator account credentials, managing port security at the firewall level, and restricting 
logical and physical access to servers to only essential personnel.  
All data transferred to DatStat’s collocated datacenter are stored, processed, and analyzed 
within the datacenter. At DatStat’s colocation facility, all access to the datacenter is controlled 
through locked doors which require an escort, and pre-authorized by another internal employee 
listed on the account.  The DatStat office space remains locked after working hours.  Access to 
computer data files is controlled by passwords released only to the personnel who use such 
files.  In addition, data files with personal identifiers (and sensitive information per designation 
by a study’s Steering Committee) are encrypted. Physical access to servers and data backup is 
restricted to a minimal number of IT professionals. Such access is provided only with strong 
passwords that regularly expire, minimizing the chance that passwords distributed inadvertently 
and/or unknowingly could cause inappropriate data access. Access to data stored on the server 
is available only to designated users who log in with specified usernames and passwords. Users 
are logged out after a period of time. A listing of the named users with a description of their 
access privileges is available within the applications. 

 
 
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7.3 
Data Safety and Monitoring 
 
Not applicable given the nature of this administrative data only project. 
 

 
 
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8 
INFORMED CONSENT PROCESS 
8.1 
Informed Consent Process  
 
The research project will be reviewed by the Institutional Review Board (IRB) at both project 
sites, Oregon Health & Science University (OHSU) and University of Alabama at Birmingham 
(UAB).   
 
The research proposed will use national Medicare administrative data to examine Medicare 
beneficiaries with non-CF bronchiectasis. These patients do not actively participate, nor do they 
submit any information not already present in their claims data. Therefore a Waiver of 
Authorization and waiver of informed consent will be submitted to the IRBs at OHSU and UAB 
prior to obtaining protected health information (PHI). The research cannot be conducted without 
access to and use of PHI or without a waiver, and it is not practical to obtain Authorizations for 
Release of PHI from hundreds of thousands of patients.  All data will be transferred from 
Medicare to UAB and OHSU under a data use agreement (similar to prior agreements already 
in place) which describes exactly how the data will be used and protected.    
 
Similarly, the project proposes to link pre-existing BRR and Medicare data for the validation 
study.  Dr. Winthrop is a BRRC Investigator who is authorized to use the BRR data and his 
team will handle the linkage.  Patients do not actively participate, nor do they submit any 
information not already present in claims or registry data. Therefore, a Waiver of Authorization 
and waiver of informed consent will be submitted to the IRB at OHSU prior to obtaining 
protected health information (PHI). The research cannot be conducted without access to and 
use of PHI or without a waiver, and it is not practical to obtain Authorizations for Release of PHI 
from the registry patients who enrolled across multiple sites.   
 

 
 
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9 
LITERATURE REFERENCES 
1. 
Seitz AE, Olivier KN, Adjemian J, Holland SM, Prevots R. Trends in bronchiectasis 
among medicare beneficiaries in the United States, 2000 to 2007. Chest 2012;142:432-9. 
2. 
O'Donnell AE. Bronchiectasis. Chest 2008;134:815-23. 
3. 
Goeminne PC, Nawrot TS, Ruttens D, Seys S, Dupont LJ. Mortality in non-cystic fibrosis 
bronchiectasis: a prospective cohort analysis. Respiratory medicine 2014;108:287-96. 
4. 
Goeminne PC, Scheers H, Decraene A, Seys S, Dupont LJ. Risk factors for morbidity 
and death in non-cystic fibrosis bronchiectasis: a retrospective cross-sectional analysis of CT 
diagnosed bronchiectatic patients. Respiratory research 2012;13:21. 
5. 
Loebinger MR, Wells AU, Hansell DM, et al. Mortality in bronchiectasis: a long-term 
study assessing the factors influencing survival. The European respiratory journal 2009;34:843-
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6. 
Oliveira MJ, Vaz D, Coutinho D, Dias M, Brito Mdo C. Morbidity and risk factors for 
mortality in patients with acute exacerbation of bronchiectasis. Chest 2014;145:430A. 
7. 
Cole PJ. Inflammation: a two-edged sword--the model of bronchiectasis. European 
journal of respiratory diseases Supplement 1986;147:6-15. 
8. 
Falkinham JO, 3rd. Surrounded by mycobacteria: nontuberculous mycobacteria in the 
human environment. Journal of applied microbiology 2009;107:356-67. 
9. 
Griffith DE, Aksamit T, Brown-Elliott BA, et al. An official ATS/IDSA statement: 
diagnosis, treatment, and prevention of nontuberculous mycobacterial diseases. American 
journal of respiratory and critical care medicine 2007;175:367-416. 
10. 
Adjemian J, Olivier KN, Seitz AE, Holland SM, Prevots DR. Prevalence of 
nontuberculous mycobacterial lung disease in U.S. Medicare beneficiaries. American journal of 
respiratory and critical care medicine 2012;185:881-6. 
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SUPPLEMENTS/APPENDICES 
 
 

 
 
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Appendix A.  Opportunistic Infection Outcomes and Definitions 
Diagnosis 
Algorithm/ ICD-9-CM 
Principle Diagnostic Codes 
Notes 
Tuberculosis 
Any ICD-9 code (010-018)  
AND  
pharmacy records indicating prescription for 
PZA prescribed  on same day within +/- 90 
days of first code date 
Veteran’s Affairs (VA) hospital ICD-9 data compared with 
medical record review:  TB, PPV 0.73 (95%CI, 0.54-0.92); 
Ref: Schneeweiss et al. Veteran's affairs hospital discharge 
databases coded serious bacterial infections accurately. 
Journal of clinical epidemiology 2007;60:397-409 
KNPC validation work:  TB code, sensitivity = 100%, but 
PPV 0.54.   Any code plus INH/RIF, sensitivity 0.79 and PPV 
0.85                                                                                                
Histoplasmosis 
Any ICD-9 code (115) 
WITH cumulative supply of >30 days of any 
of the following three drugs: fluconazole, 
itraconazole, or voriconazole given within +/- 
90 days of first code date 
VA project with spec 0.91, and PPV 0.50;  
 
Blastomycosis  
Any ICD-9  116 WITH cumulative supply of 
>30 days of any of the following three drugs: 
fluconazole, itraconazole, or voriconazole 
given within +/- 90 days of first code date 
VA project with ppv 100%;   
Coccidioidomycosis 
Any ICD-9 (114) WITH cumulative supply 
of >30 days of any of the following three 
drugs: fluconazole, itraconazole, or 
voriconazole given within +/- 90 days of first 
code date                                               
KNPC suggests Code alone, sensitivity 0.59, PPV 0.59-0.72; 
one code + RX increases PPV to 0.91                                           
 VA  project w/ PPV 0.67, specificity 0.92                                   
Cryptococcosis 
Any ICD-9 (117.5, 321.0)           
WITH cumulative supply of >30 days of any 
of the following three drugs: fluconazole, 
itraconazole, or voriconazole given within +/- 
90 days of first code date                             
VA ICD-9 as above PPV 100% [0.45-1.0];  Ref: Schneeweiss 
JCE 2007 (full ref above)                                                              
KNPC suggests Code alone, sensitivity 0.54, PPV .78; code 
plus pharmacy data ppv 0.80                                                         
VA project with spec 0.67, PPV 0.67 
Endemic Mycosis (non-
specific outcome of all 
endemic  fungi above) 
ICD-9 484.7 WITH cumulative supply of 
>30 days of any of the following three drugs: 
fluconazole, itraconazole, or voriconazole 
given within +/- 90 days of first code date    
VA ICD-9 as above:  TB, PPV 0.73 (95%CI, 0.54-0.92); all 
OIs combined (TB, NTM, aspergillus, cryptococcus) PPV 
0.73 (0.61-0.85) ; Ref: Schneeweiss JCE 2007 (full ref above)    
Nocardiosis/actinomycosis 
Any ICD-9 (039) 
from VA project: ppv 0.38 
Listeriosis 
Any ICD-9 (027.0) 
 from VA project ppv 1.00 
Toxoplasmosis 
Any ICD-9 (130) 
from VA project ppv 0.67 
Pneumocystsis 
Any ICD-9 (136.3)                                              from VA project ppv 0.53 
Legionellosis 
Any ICD-9 (482.84) 
from VA project ppv 1.00 
Salmonellosis 
Any ICD-9 (003 
from VA project ppv 0.88 
Aspergillosis 
Any ICD-9 (117.3, 484.6) WITH any 
outpatient RX for voriconazole, itraconazole, 
posaconazole given within +/- 90 days of 
first code date. 
ICD-9 alone in VA system,  PPV .67 [0.4-0.94]) ; Ref: 
Schneeweiss JCE 2007 (full ref above)                                         
Change DC, PPV = 0.71 (.44-.90) in UAB discharge codes of 
transplant patients (unpublished data);                                          
VA project (code plus meds) with specificity 0.95, PPV 0.70 
Ref: JW Baddley  Abstract #2093; ACR 2013 
Zoster 
Any ICD-9 code 053 (outpatient or inpatient 
code) WITH any outpatient RX acyclovir, 
famvir, or valacyclovir given +/- 90 days of 
first code date.  
a. Outpatient code PPV = 0.94 in managed care setting in age 
> 60  Ref: Jumaan et al. J Infect Dis 2005;191:2002-7 
Inpatient codes in either first or second position without 
pharmacy data had high PPV for simply having zoster during 
the time of hospital stay PPV =0.85).     Similar high PPV for 
both inpatient (0.94) and outpatient codes (0.83) of any 
position seen in Kaiser NW data  
Ref: Mullooly J et al. Epidemiol Infect. 2005;133:245-53