Utilizing CT Based Imaging Parameters of Body Composition to Understand Heterogeneity of Response to Biologic Therapies in Severe Asthma Cohorts
Conditions: Asthma
Sponsor: University of Michigan

Utilizing CT Based Imaging Parameters of Body 
Composition to Understand Heterogeneity of 
Response to Biologic Therapies in Severe 
Asthma Cohorts 
 
NCT06922760 
 
Date of Actual IRB Approval: October 28, 2020 

Utilizing CT Based Imaging Parameters of Body Composition to Understand Heterogeneity of 
Response to Biologic Therapies in Severe Asthma Cohorts Protocol 
Version 1.100
 21 November 2019
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Utilizing CT Based Imaging Parameters of Body 
Composition to Understand Heterogeneity of 
Response to Biologic Therapies in Severe 
Asthma Cohorts 
Principal Investigators: Stewart C. Wang, MD PhD and Njira Lugogo, 
MD 
Funded by: GlaxoSmithKline 
Version Number: 1.100 
21 November 2019 
 
 
 

Utilizing CT Based Imaging Parameters of Body Composition to Understand Heterogeneity of 
Response to Biologic Therapies in Severe Asthma Cohorts Protocol 
Version 1.100
 21 November 2019
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1 PROTOCOL SUMMARY 
1.1 SYNOPSIS 
Title: 
Utilizing CT Based Imaging Parameters of Body Composition to 
Understand Heterogeneity of Response to Biologic Therapies in 
Severe Asthma Cohorts  
Study Description: 
We plan to utilize University of Michigan EMR data to form a 
retrospective cohort of severe asthma patients on biologic therapies 
for asthma with CT imaging and known outcomes on therapy. We 
will then analyze these images using morphomics, a combination of 
high-throughput image analysis and deep learning techniques, to 
derive imaging biomarkers that will be able to predict therapeutic 
response to biologics. These biomarkers will then be tested in a 
second cohort from the University of Calgary to assess for validity.   
Objectives: 
Primary 
Objective: 
 
To identify imaging biomarkers that are 
associated with the probability of any 
reduction in pre-treatment baseline 
exacerbation frequency in a severe asthma 
population on biologics  
Secondary 
Objectives: 
1. To identify imaging biomarkers that are 
associated with the probability of any 
post-treatment improvement of FEV1 of 
5% or more 
2. To identify imaging biomarkers that are 
associated with the probability of any 
post-treatment reduction in maintenance 
OCS use 
Exploratory 
Objectives: 
1. To identify imaging biomarkers that are 
associated with the probability of any 
reduction in pre-treatment baseline 
hospitalizations for asthma 
2. To identify imaging biomarkers that are 
associated with the probability of any 
post-treatment improvement in ACT 
score 
3. To identify imaging biomarkers that are 
associated with the probability of any 
subjective post-treatment reduction in 
symptoms described clinically 
Endpoints: 
Primary 
Endpoint:  
Determine the cutoff value in chest wall 
muscle size/quality that optimally classifies 
patients that had and did not have any 
reduction in pre-treatment baseline 
exacerbation frequency (binary).  
Alternative (continuous): determine the 
strength of the linear/non-linear relationship 

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between continuous chest wall muscle 
size/quality and continuous change in 
exacerbation frequency  
Secondary 
Endpoints: 
1. Determine individual and combinations 
of body composition measures 
(phenotypes) associated with a 5% or 
greater improvement in FEV1 when 
compared to baseline FEV1 
2. Determine individual and combinations 
of body composition measures 
associated with any reduction in 
maintenance OCS use when compared 
to baseline OCS use  
Exploratory 
Endpoints: 
These endpoints may be limited in scope of 
analysis by the sample size within each of 
the outcome groups.   
1. Determine individual and 
combinations of body composition 
measures associated with any 
reduction in hospitalizations when 
compared to baseline hospitalizations  
2. Determine individual and 
combinations of body composition 
measures associated with any 
improvement in ACT score when 
compared to baseline ACT score 
3. Determine individual and 
combinations of body composition 
measures associated with any 
subjective improvement of symptoms 
when compared to baseline 
symptoms 
Study Population: 
Approximately 300 male or female subjects ages 18-85 with a 
diagnosis of asthma and on biologic therapy will be studied at the 
University of Michigan in Ann Arbor, MI, USA and approximately 200 
male or female subjects over the age of 18 will be studied in the 
University of Calgary in Calgary, Alberta, CA.  
Description of 
Sites/Facilities 
Enrolling Participants: 
University of Michigan in Ann Arbor, MI, USA is a tertiary academic 
medical center. Rockyview General Hospital, associated with the 
University of Calgary, Calgary, Alberta, Canada, is tertiary academic 
medical referral center for patients with severe asthma. 
Study Duration: 
1.5 years 
  
1.2 SCHEMA 
 
Components 
Year 1 
Year 2 

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AIM 1 
Identify all compatible patients at University of 
Michigan 
 
 
 
 
 
 
Abstract EMR data to complete database 
 
 
 
 
 
 
Collate University of Calgary cohort with database 
 
 
 
 
 
 
Analyze pre-treatment characteristics 
 
 
 
 
 
 
 
 
AIM 2 
Process CT data to derive morphomics values 
 
 
 
 
 
 
Establish morphomics phenotypes 
 
 
 
 
 
 
Assign clinical phenotypes 
 
 
 
 
 
 
Assess for overlap in phenotypes to identify novel 
groups 
 
 
 
 
 
 
 
 
AIM 3 
Perform analysis using University of Michigan data 
 
 
 
 
 
 
Apply derived predictive variables to University of 
Calgary data 
 
 
 
 
 
 
Perform analysis 
 
 
 
 
 
 
Write final report 
 
 
 
 
 
 
 

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2 INTRODUCTION 
2.1 STUDY RATIONALE 
DISEASE BURDEN IN SEVERE ASTHMA AND THERAPY SELECTION 
Asthma is a highly prevalent disease that affects 33 million people in the United States and 
Canada. The vast majority of asthma patients are well controlled with conventional asthma 
therapies; however, 10% of patients have severe disease that is associated with significant 
comorbidity and exposure to high doses of OCS. OCS use increases the odds of developing co-
morbid diseases such as diabetes, hypertension, cardiac disease and cataracts and is 
increasingly being recognized as a major contributor to severe asthma costs, making the 
development of alternative less harmful therapies paramount1. Fortunately, over the past three 
years there has been a rapid increase in the number of biologics approved for the management 
of severe asthma with type II inflammation, which is estimated to be present in up to 60% of this 
population. The term type II inflammation refers to an increase in the levels of type II cytokines 
including IL4, IL5 and IL13, which induce pathologic downstream production of IgE and 
eosinophils. Current clinical practice utilizes surrogate biomarkers, including peripheral and 
sputum eosinophils, FeNO and total IgE levels, to phenotype patients with severe asthma and 
guide biologic drug selection. Unfortunately, these surrogates are imperfect and prone to error 
from multiple sources, especially in the obese2. 
  
Unequal Response to Therapy Despite Similar Biomarkers  
The response to targeted biologic therapies is heterogeneous despite the use of well-studied 
biomarkers to select potential responders. Unfortunately, even when patients with a high 
likelihood of response are selected, 10-15% have complete non-response in all clinical 
parameters, and only 40% have a robust and complete response to therapy. This is likely 
related to the presence of overlapping inflammatory pathways, co-morbid eosinophilic diseases 
that affect response to therapy, possible sub-optimal dosing of drug and the lack of highly 
specific biomarkers. Precision medicine approaches to severe asthma are in their infancy and 
the application of complex techniques to help guide therapy are urgently needed. Currently, 
patients that are partial responders or non-responders to given therapies are switched between 
available biologics as a means of identifying the best therapy—an inefficient approach that is 
both time consuming and costly to patients and payers (Figure 1). 
  

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Figure 1: Improving patient outcomes with Precision Medicine  
2.2 BACKGROUND 
Innovation—Expanding on the Role of Body Composition 
Nearly all research in severe asthma patients, including phase 3 trials for the current biologic 
therapies, has used BMI as a surrogate of body composition. Even when CT imaging has been 
used to better characterize body composition through relatively simplistic maneuvers such as 
calculation of total visceral and subcutaneous fat mass there have been no clinically usable 
predictive results from such analyses and no information about the role of the chest wall3,4. 
Morphomics is a combination of high-throughput image analysis and deep learning techniques 
that enable the segmentation of tissues to precise detail, enabling the characterization of body 
composition to an unparalleled degree5,6. Furthermore, the University of Michigan analytic 
morphomics group has an existing relational database of over 120,000 subjects that enables the 
generation of thousands of precise measurements, such as fat distribution, bone mineral 
density, and muscle density and size, and that can serve as a reference population for our 
asthma cohorts. This will allow for the accurate identification of extensive body composition 
patterns that differ from the norm utilizing an imaging population database on a scale that has 
never been done before. 
 
Analytic morphomics provides actionable quantification of patient heterogeneity for 
precision medicine. 
Precision medical intervention requires granular, precise, and actionable data at the time of 
treatment. Supported by rapid advances in new high-throughput technologies, “omics” data 
provide a unique opportunity to better understand pathophysiology and to translate research 
findings to clinical practice. These data enable clinicians to shift from traditional one-size-fits-all 

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treatment toward more targeted and precisely individualized therapies. Over the past decade, 
research at the University of Michigan has demonstrated that morphomics is the “omics” data 
most proximate to a patient’s ultimate health outcome (Figure 2). 
 
Figure 2: Morphomics is the "omics" most proximate to treatment 
  
Recent studies have shown that these digital biomarkers extracted by analytic morphomics 
provide much better quantitative understanding of morphological features of the human body7,8. 
More importantly, morphomics has been shown to significantly and substantially improve the 
prediction of clinical outcome and cost in numerous patient populations9,10. By providing a 
quantitative measure of a patient’s body compartment dimensions and composition, near the 
time of treatment, morphomics can deliver tailoring variables for clinical decision-making. 
  
More recently, morphomic measurements of rib length, density, shape, and angle have allowed 
a level of analysis of the chest wall that has never been studied in asthma patients 11,12,13. Using 
adjacent ribs as a reference, we have then been able to measure intercostal muscle size and 
quality (Figure 3).  Morphomics has previously demonstrated the role the chest wall can play in 
disease by linking rib angle and age to potentially explain the higher incidence of rib fractures in 
the elderly in motor vehicle collisions14. Since severe asthmatics typically have air trapping and 
are at risk for obesity, it follows that they would also have abnormal chest wall anatomy that 
could be mechanistically contributing to their disease in a way that could significantly impair 
their response to medical therapies. The specific characterization of chest wall anatomy in 
severe asthmatics has never been studied and represents an opportunity to identify novel 
biomarkers that could predict response/non-response to therapies and enrich the selection of 
patients in future targeted asthma studies of all levels of severity. 
  

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Figure 3: Proof of concept of intercostal muscle measurements using morphomics 
2.3 RISK/BENEFIT ASSESSMENT 
2.3.1 KNOWN POTENTIAL RISKS 
As the study is retrospective, there is no known immediate or long-range risk to subjects 
besides risk of personal health information theft which is minimized by using only university 
computers for work, storing health information on university servers or hardware, and de-
identification of data once abstraction is complete. 
2.3.2 KNOWN POTENTIAL BENEFITS 
There are no known immediate or long-range benefits to subjects. 
2.3.3 ASSESSMENT OF POTENTIAL RISKS AND BENEFITS 
There is no known risk exposure to subjects and thus the potential value of the knowledge 
gained from this study outweighs the minimal risk of loss of personal health information. 

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3 OBJECTIVES AND ENDPOINTS 
OBJECTIVES 
ENDPOINTS 
JUSTIFICATION FOR ENDPOINTS 
Primary 
To identify imaging 
biomarkers that are 
associated with the 
probability of any 
reduction in pre-treatment 
baseline exacerbation 
frequency in a severe 
asthma population on 
biologics  
Any reduction in 
exacerbations when 
compared to baseline 
exacerbations 
Reduction in exacerbations is the primary 
clinical endpoint used in phase III trials to get 
many of the biologics used in asthma 
approved. Finding imaging biomarkers that 
associate with and possibly predict which 
patients will not have a reduction in 
exacerbations would be critical information for 
clinicians considering biologic initiation.  
Secondary 
To identify imaging 
biomarkers that are 
associated with the 
probability of any post-
treatment improvement of 
FEV1 of 5% or more 
5% or greater 
improvement in FEV1 
when compared to 
baseline FEV1 
FEV1 is an important measure of lung 
function for patients with asthma, and any 
imaging biomarkers that predict a failure to 
improve this measure would be clinically 
useful. 
To identify imaging 
biomarkers that are 
associated with the 
probability of any post-
treatment reduction in 
maintenance OCS use 
Any reduction in 
maintenance OCS 
use when compared 
to baseline OCS use 
OCS are associated with multiple 
comorbidities, especially in the asthma 
population. Multiple phase III studies are 
currently underway to study maintenance 
OCS reduction in asthma due to this clinical 
concern and is a common reason for initiating 
biologics. Any imaging biomarkers that 
predict a failure to wean OCS after biologic 
initiation would be clinically useful. 
Exploratory  
To identify imaging 
biomarkers that are 
associated with the 
probability of any 
reduction in pre-treatment 
baseline hospitalizations 
for asthma 
Any reduction in 
hospitalizations 
when compared to 
baseline 
hospitalizations 
Hospitalizations represent the most severe 
form of exacerbation and being able to find 
imaging biomarkers that associate with no 
improvement in hospitalizations would be 
clinically significant. 
To identify imaging 
biomarkers that are 
associated with the 
probability of any post-
treatment improvement in 
ACT score 
Any improvement in 
ACT score when 
compared to baseline 
ACT score 
Failure to improve symptoms after biologic 
initiation is a clinically frustrating scenario and 
finding imaging biomarkers that can predict 
this would be important for biologic initiation.  

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OBJECTIVES 
ENDPOINTS 
JUSTIFICATION FOR ENDPOINTS 
To identify imaging 
biomarkers that are 
associated with the 
probability of any 
subjective post-treatment 
reduction in symptoms 
described clinically 
Any subjective 
improvement of 
symptoms when 
compared to baseline 
subjective symptoms 
Given the retrospective nature of our study, 
there is a high likelihood some of the other 
endpoint data will be missing from clinical 
encounters for our cohort. Clinical impression 
of symptoms, however, is expected to be 
present in essentially every clinical encounter 
for asthma. In order to make use of this 
important information, we opted to include an 
endpoint designed to capture this subjective 
data since finding imaging biomarkers that 
predict no improvement with this subjective 
marker would be clinically important, since 
this is a common reason clinicians seek to 
start severe asthma patients on biologic 
therapies.  

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4 STUDY DESIGN 
4.1 OVERALL DESIGN 
Over an 18-month period we plan to conduct our study in 3 parts as identified in the study 
timeline table above.   
 
University of Michigan Cohort Discovery, Database Construction, and Baseline Analysis 
We plan to utilize existing data analytic tools at the University of Michigan to search the EMR for 
eligible patients to include in a retrospective cohort as identified by the inclusion criteria in 
section 5. We estimate that approximately 300 subjects will meet our initial criteria based on a 
preliminary search. We will then abstract all available clinical information from the EMR, de-
identify it (with coded key stored in a separate, secured location), and enter the resulting coded 
data into a database. Up to 2 years prior to biologic initiation or CT scan (whichever is earlier) 
and all post-biologic therapy information will be included. Clinical information we will abstract will 
include demographic information, heights, weights, comorbid illnesses (coded as malignancy, 
coronary artery disease, diabetes, stroke/TIA, cirrhosis, and CKD), non-biologic asthma 
medication dosage and frequency of use (OCS, ICS, SABA, LAMA, LABA, montelukast, 
zileuton, and theophylline), asthma biologic agent (benralizumab, reslizumab, mepolizumab, 
omalizumab, and dupilumab) start and (if applicable) end dates as well as reason for end date 
(coded as ineffective, cost, intolerance/allergy, non-compliance, lost to follow-up, and other), 
FeNO, ACT scores, serum and sputum eosinophil counts, IgE, pulmonary function test 
parameters (% predicted), hospital/emergency room/urgent care/unscheduled office visits and 
phone call encounters for asthma or breathing-related complaints, and subjective clinician 
assessment of asthma control (independently graded by 2 different clinicians interpretation of 
EMR data). Each subject will also be assigned a phenotype by the Severe Asthma Research 
Program criteria15. If a majority of subjects are missing post-bronchodilator data and unable to 
be clustered by these criteria, we will instead assign adopted clusters from this paper as follows: 
Cluster 3-Normal FEV1 (≥68% of predicted), eosinophils (<200 cells/μL), FeNO (<25 ppb), and 
IgE (<200 UI/ml) and no evidence of atopy; Cluster 4-Reduced FEV1 (≤68% of predicted) with 
abnormal elevation of eosinophils, FeNO, or IgE and/or evidence of atopy; Cluster 5--Reduced 
FEV1 (≤68% of predicted) with normal eosinophils, FeNO, or IgE and no evidence of atopy. We 
do not expect to have any Cluster 1 or 2 patients in our study but will assign them accordingly if 
present. We will perform similar abstraction procedures for approximately 200 patients in the 
University of Calgary cohort, and label the data with a tag for this cohort for analytical purposes. 
 
For the purposes of our analysis, we will define baseline measurements as the average of all 
data obtained in the year prior to biologic initiation or, if only one clinical encounter (defined as 
an office or emergency room visit) is available in this time period, then the average of the 2 
clinical encounters immediately prior to biologic initiation will be use with the time frame noted in 
the database. In order to assess our primary and secondary outcomes we will use the average 
of all data from 4 to 18 months after biologic initiation or, if only one clinical encounter is 
available in this time period, the average of the 2 clinical encounters closest to, but not before 4 
months after, biologic initiation. If a second biologic is introduced or the initial biologic choice is 

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changed, we will use all clinical data prior to the change, even if it is before 4 months of use, 
then follow the above steps to collect new outcome data on the new drug (see Figure 4).    
 
 
Figure 4. Baseline and Response Clinical Data Plan. 
 
Since our interest is primarily in finding the ~15% of patients who do not respond to biologics, 
we have chosen to include partial and complete responders together in our endpoints in order to 
isolate the non-responder population. Table 1 below will represent the dichotomization of 
responders and non-responders for each endpoint using the baseline and post-biologic data as 
detailed above. An exacerbation will be defined as any hospitalization, urgent care, emergency 
room, or unscheduled office visit for asthma or a breathing-related complaint or a temporary 
increase of OCS or ICS dosing for 3 or more days with the intent of the increase to treat acute 
symptoms. For clinician response, clinical encounter notes will be read and interpreted by 2 
independent physicians and graded as detailed in the table. Upon completion of all data 
abstraction the patient will be de-identified as described in data handling below. 
 
Responder 
Non-responder 
Exacerbations 
Any decrease from baseline 
No change from baseline 
Hospitalizations 
Any decrease from baseline 
No change from baseline 
Maintenance oral  
corticosteroid (OCS) 
use 
Any decrease from baseline 
No change from baseline 

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Change in FEV1 
≥5% improvement from 
baseline 
<5% improvement  
from baseline 
ACT score 
≥1-point improvement from 
baseline  
No change from baseline 
Clinician impression of  
response to therapy 
Any improvement in  
symptoms from baseline 
No change from baseline 
 Table 1. Outcome Response Grading. 
 
Morphomics CT Analysis and Clustering  
After identification of all appropriate patients in the University of Michigan cohort, we will then 
identify all relevant CT scans in subjects. We will select pre-biologic CT data of the chest and, if 
available, abdomen/pelvis (or if no pre-biologic scans exist for a subject we will utilize CT 
imaging closest to the biologic start date and make note that the CT scan was obtained after 
biologic initiation) for analysis. Height-normalized measures will be computed to control for 
height-related correlation (e.g., Skeletal Muscle Index = Skeletal Muscle Area / Height^2). 
Morphomics analysis will be run with measures centered and scaled into standard deviation 
units, stratified by sex and age. In addition, the Reference Analytic Morphomics Population16 will 
be used to generate additional variables that offer a population-based control for sex and age.  
 
Analysis of Phenotypes, Training Data Set, and Assessing Model Robustness 
Please see the statistical section for detailed statistical analysis plan. 
4.2 SCIENTIFIC RATIONALE FOR STUDY DESIGN 
We elected to approach this problem in a retrospective fashion due to the availability of existing 
data that is likely sufficient to answer our clinical question and the known risks of radiation from 
CT exposure. Many asthmatics undergo CT scans for various reasons; however, it is rare to 
obtain a CT scan in a stable asthmatic, even if symptoms are severe, since there is no known 
current clinical utility in this diagnostic test. Since our question relies on using CT data to assess 
body composition in severe asthmatics requiring biologics to find novel parameters that may be 
able to predict response more reliably than the current standard of care options (FeNO, 
sputum/serum eosinophils, and IgE), it follows that utilizing existing CT data while also having 
the concurrent retrospective benefit of known response to biologic therapy would allow this 
question to be most efficiently addressed without exposing any of the subjects to the harms of 
radiation solely for research purposes. Furthermore, our use of a learning cohort retrospectively 
constructed at the University of Michigan and a prospectively constructed validation cohort from 
the University of Calgary will allow us to generate more robust results than normally found in 
retrospective studies since we will be using a geographically distinct validation model.  
 
 

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5 STUDY POPULATION 
5.1 INCLUSION CRITERIA 
Our population will include all adults 18 years or older with a diagnosis of asthma (defined as a 
positive methacholine test, supportive PFTs, or clinician diagnosis with appropriate supporting 
clinical context) who have been prescribed and taken an FDA-approved biologic medication for 
severe asthma (mepolizumab, benralizumab, reslizumab, omalizumab, or dupilumab) and have 
chest CT images in the University of Michigan radiology databases. All patients from the 
Calgary cohort will be screened in a similar fashion. 
5.2 EXCLUSION CRITERIA 
We will exclude patients with a > 10 pack year history of smoking and those with evidence of 
emphysema on CT imaging due to concerns that concurrent COPD may be attenuating the 
effects of the biologic. Those with some scattered bronchiectasis thought to be secondary to 
severe asthma will not excluded; however, severe bronchiectasis secondary to other causes will 
be excluded for the same reason. Furthermore, patients who do not have accessible adequate 
follow-up information (4 or more months from drug initiation with clear documentation of 
response) will be excluded from the final analysis in part 3 due to an inability to assess 
response but will be included in the initial analysis to help with cluster identification. 

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6 STATISTICAL ANALYSES 
6.1 BASELINE DATA ANALYSIS  
Tabulate summary statistics of baseline characteristics for each analysis cohort. Continuous 
variables will be summarized using mean, median, standard deviations, and interquartile range 
(IQR) of each baseline characteristic. Categorical variables will be summarized using class 
percentages. Perform exploratory data analysis (EDA) to identify potential outliers, assess the 
statistical distribution of data elements (e.g., Normal vs. Chi-square), and examine bivariate 
correlation matrix for potential non-linearity. Compare pre-treatment characteristics of University 
of Michigan and University of Calgary cohorts to assess similarity using Student’s t (continuous, 
Normal), Fisher’s Exact (binomial), Wilcoxon rank-sum (continuous, non-Normal), and Chi-
Square/Tukey’s Honest Significant Difference (Categorical) statistical tests. 
6.2 MORPHOMICS ANALYSIS AND PHENOTYPING 
Compute height-normalized measures to control for height-related correlation, e.g., Skeletal 
Muscle Index = (Skeletal Muscle Area / Height^2). Center and scale morphomics measures into 
standard deviation units, stratified by sex, to control for sex differences. (Optionally/additionally, 
convert morphomics measures into sex- and age-adjusted percentiles using the Reference 
Analytic Morphomics Population (RAMP), to simultaneously control for sex- and age-related 
correlation). The following analyses will be performed on both the UM and UC cohorts 
combined. Classification and regression tree (CART) will be utilized to develop individualized 
decision rule on SARP class based upon morphomic measures. In addition, Unsupervised 
learning techniques will be carried out to identify novel clusters for morphomic phenotypes in 
these cohorts. 
6.3 IDENFITICATION OF BIOMARKERS FOR OUTCOMES 
To incorporate high-dimensional morphomic parameters to better stratify patients in regard to 
their probability of complete response, we will leverage the regularization technique in machine 
learning to select important variables and develop risk predictive models. To be more specific, 
elastic net regularization will be utilized to address the high dimensional nature of data derived 
with analytic morphomics. Elastic net consists of a linear combination of L1 (lasso) and L2 
(ridge) penalty to address over-fitting and co-linearity, respectively. In addition, to capture 
potential non-linear effects among variables, we will also apply random forest and gradient 
boosting, two highly utilized ensemble tree-based methods. Sample splitting will be used to 
assess model performance. We will evaluate the accuracy of developed predictive models by 
various performance measures, including Harrell’s Concordance index, integrated cumulative 
area under the curve, and integrated brier score. The final developed predictive model will be 
evaluated on an independent dataset from the University of Calgary.  

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7 SUPPORTING DOCUMENTATION AND OPERATIONAL CONSIDERATIONS 
7.1 REGULATORY, ETHICAL, AND STUDY OVERSIGHT CONSIDERATIONS 
7.1.1 INFORMED CONSENT PROCESS 
Both the University of Michigan and University of Calgary sites will apply for exemption of 
informed consent through their respective local IRBs given the retrospective nature of the study 
and intent to store all information in a de-identified fashion. 
7.1.1.1 CONSENT/ASSENT AND OTHER INFORMATIONAL DOCUMENTS PROVIDED 
TO PARTICIPANTS 
 N/A 
7.1.1.2 CONSENT PROCEDURES AND DOCUMENTATION 
 N/A 
7.1.2 STUDY DISCONTINUATION AND CLOSURE 
 This study will be discontinued in approximately 18 months or at completion of the final analysis 
as detailed above. 
7.1.3 CONFIDENTIALITY AND PRIVACY 
Both University of Michigan and University of Calgary sites will obtain institutional authorization 
to share clinical information for the purposes of this study 
7.1.4 FUTURE USE OF STORED SPECIMENS AND DATA 
All finalized CT and chart data will be stored in a de-identified fashion on University of Michigan 
servers for future research use. 
7.1.5 KEY ROLES AND STUDY GOVERNANCE 
Principal Investigators 
Sub-Investigators 
 Stewart Wang, MD PhD 
Jon Grace, MD 
 Njira Lugogo, MD 
Patrick Mitchell, MD 
7.1.6 SAFETY OVERSIGHT 
 N/A 
7.1.7 CLINICAL MONITORING 
 N/A 
7.1.8 QUALITY ASSURANCE AND QUALITY CONTROL 
 Chart data abstraction will be carried out by research staff and overseen by the principal 
investigator and co-investigators. For areas where clinical judgement is required (such as 
identification of an exacerbation or scoring subjective clinical response to therapy), 2 physicians 
with a pulmonary background will adjudicate the clinical data to ensure quality control. 

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7.1.9 DATA HANDLING AND RECORD KEEPING 
7.1.9.1 DATA COLLECTION AND MANAGEMENT RESPONSIBILITIES 
Study oversight will be performed by the principal investigators and the co-investigator and the 
University of Michigan data analysis team. The study database will be developed on servers 
managed by MICHR (the CTSA supported clinical research infrastructure) at the University of 
Michigan. Web-based data entry and management, as well as the creation and export of study 
reports for the safety committee will be provided by the University of Michigan under the 
guidance of the data management core.  The study will use the REDCap clinical trial software 
platform for electronic data capture and clinical data management, as the basis for our custom-
designed data entry and management system. Protocols for the transfer of data, with careful 
attention to data integrity, will be written by experienced programmers and stored in the 
database.  Data collection is the responsibility of the research staff. The investigator is 
responsible for ensuring the accuracy, completeness, legibility, and timeliness of the data 
reported.  
 
CT scans anonymization will be performed through existing processes in the analytic 
morphomic group. Scans will be selected using software that connects directly to the 
institution’s existing PACS system. Once an appropriate scan is identified, it will be downloaded 
and processed through the morphomic lab’s anonymization software. This system removes the 
patient information stored in each image file’s DICOM header, which contains attributes such as 
name, patient ID, date of birth, etc. All patient identifying data is removed, and a unique 
identifier (UI) is created and stored in the Patient ID attribute. The UI takes place of the medical 
record number (MRN) throughout the life of the project.  The key connecting this UI with the 
patient MRN will be maintained and controlled by the project PIs.  
7.1.9.2 STUDY RECORDS RETENTION 
Data obtained from the study will be retained in a de-identified fashion for a minimum of 3 years 
after completion of study or longer if required by local regulations. 
7.1.10 PROTOCOL DEVIATIONS 
Please see additional considerations for anticipated changes to the protocol as described above 
that may occur once data is obtained. 
7.1.11 PUBLICATION AND DATA SHARING POLICY 
An explicit goal of this study is to present the primary and related outcomes of this study at 
Scientific Meetings and to publish, as possible, in peer-reviewed Scholarly Journals to assure 
dissemination of the findings. The authority to do so resides with the subject investigators and 
not with any sponsor. 
7.1.12 CONFLICT OF INTEREST POLICY 
Applicants will certify the absence of certain financial interests and arrangements of clinical 
investigators that could affect the reliability of data, or alternatively disclose those financial 
interests and arrangements to the agency and identify steps taken to minimize the potential for 
bias. 

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7.2 ADDITIONAL CONSIDERATIONS 
None 
 
7.3 ABBREVIATIONS 
ACT 
Asthma Control Test 
AE 
Adverse Event 
ANCOVA Analysis of Covariance 
CFR 
Code of Federal Regulations 
CKD 
Chronic Kidney Disease 
CLIA 
Clinical Laboratory Improvement Amendments 
CMP 
Clinical Monitoring Plan 
COC 
Certificate of Confidentiality 
CONSORT Consolidated Standards of Reporting Trials 
CRF 
Case Report Form 
CT 
Computed Tomography 
DCC 
Data Coordinating Center 
DHHS 
Department of Health and Human Services 
DSMB 
Data Safety Monitoring Board 
DRE 
Disease-Related Event 
EC 
Ethics Committee 
eCRF 
Electronic Case Report Forms 
EMR 
Electronic Medical Record 
FeNO 
Fraction of Exhaled Nitric Oxide 
FEV1 
Forced Expiratory Volume in One Second 
FDA 
Food and Drug Administration 
FDAAA 
Food and Drug Administration Amendments Act of 2007 
FFR 
Federal Financial Report 
GCP 
Good Clinical Practice 
GLP 
Good Laboratory Practices 
GMP 
Good Manufacturing Practices 
GWAS 
Genome-Wide Association Studies 
HIPAA 
Health Insurance Portability and Accountability Act 
IB 
Investigator’s Brochure 
ICH 
International Conference on Harmonisation 
ICMJE 
International Committee of Medical Journal Editors 
ICS 
Inhaled Corticosteroid 
IDE 
Investigational Device Exemption 
IgE 
Immunoglobulin E 
IL 
Interleukin 

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IND 
Investigational New Drug Application 
IRB 
Institutional Review Board 
ISM 
Independent Safety Monitor 
ISO 
International Organization for Standardization 
ITT 
Intention-To-Treat 
LABA 
Long-Acting Beta Agonist 
LAMA 
Long-Acting Muscarinic Antagonist 
LSMEANS Least-squares Means 
MedDRA Medical Dictionary for Regulatory Activities 
MOP 
Manual of Procedures 
MSDS 
Material Safety Data Sheet 
NCT 
National Clinical Trial 
NIH 
National Institutes of Health 
NIH IC 
NIH Institute or Center 
OCS 
Oral Corticosteroid 
OHRP 
Office for Human Research Protections 
PI 
Principal Investigator 
QA 
Quality Assurance 
QC 
Quality Control 
SABA 
Short Acting Beta Agonist 
SAE 
Serious Adverse Event 
SAP 
Statistical Analysis Plan 
SMC 
Safety Monitoring Committee 
SOA 
Schedule of Activities 
SOC 
System Organ Class 
SOP 
Standard Operating Procedure 
TIA 
Transient Ischemic Attack 
UP 
Unanticipated Problem 
US 
United States 
 
 
 
 

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