Validating the Use of Frailty Measurements to Predict Care Expectations and Deteriorations in Quality of Life Among People With COPD: A Prospective Cohort Study
Conditions: Chronic Obstructive Pulmonary Disease
Sponsor: Ottawa Hospital Research Institute

Correspondence Address:       
               
The Ottawa Hospital, General Campus, 501 Smyth Road, Box 211 Ottawa, ON K1H 8L6 
Validating the Use of Frailty Measurements to Predict Care Expectations and Deteriorations in Quality of Life 
Among People with COPD: A Prospective Cohort Study 
Study Protocol, version 3 
 
PI’s: Sunita Mulpuru, Alan Forster, Melissa Andrew 
Co-I’s: Melanie Chin, Shawn Aaron, Gonzalo Alvarez, Smita Pakhale, Nha Voduc, Tetyana Kendzerska, Tim 
Ramsay, Jamie Brehaut, Shirley Huang  
 
September 22, 2022 
 
REB Correspondence: 
Dr. Sunita Mulpuru 
The Ottawa Hospital – General Campus 
Division of Respirology 
501 Smyth Rd, Room L1238, Box 211 
Ottawa, ON K1H 8L6 
smulpuru@toh.ca 
 
 
 
 
 
 
 
 

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1.0 PROJECT SUMMARY 
Frailty is a comprehensive measure of health status that describes the vulnerability of a person to health 
stressors (1–4). Frailty is measured along a continuum from ‘managing well’ to ‘severely frail’, and is 
characterized by progressive impairment in activities of daily living (4).  More than one out of every four 
patients with Chronic Obstructive Pulmonary Disease (COPD) are frail, and frailty is associated with  frequent 
exacerbations, hospital readmission, mortality, and poor quality of life (3,5–10). Further, intervening with 
specialized exercise, nutritional, and personal supportive care programs has the potential to reverse, or 
stabilize frailty when identified at the earlier stages (2,3,7).   
However, little is known about whether frailty measurements can be used to predict the risk of deteriorations 
in validated patient-reported outcomes, if frailty measurements are reproducible among patients with COPD, 
and if patient care preferences differ between degrees of frailty. Further, it is unknown whether frailty 
categories could be used to guide tailored interventions to address and manage frailty, and whether frailty-
focused interventions could improve long term outcomes in addition to conventional treatments (6,10).   
We hypothesize that progressive degrees of frailty will reliably predict important deteriorations in patient-
centered outcomes over time, including health-related quality of life, symptom burden, psychosocial wellbeing, 
and measures of survival and health service use.   
If this association between frailty and health is confirmed, then it follows that frailty could be used as a risk 
stratification tool to guide tailored interventions for population subgroups with varying degrees of frailty.   
To inform the development and testing of effective care interventions within categories of frailty, the following 
questions must first be addressed:(1) What is the predictive value of progressive degrees of frailty on 
deterioration in patient-centered outcomes and increased health care utilization among people with COPD? (2) 
What are the differences in expectations of clinical care between patients and physicians within different 
degrees of frailty? (3) What is the inter-rater agreement between patients and physicians who use the Clinical 
Frailty Scale (CFS) to measure frailty? (4) What is the test-retest reliability of the CFS when used by the same 
patient at 2 different time-points, 6 months apart? 
2.0 BACKGROUND 
What is the Problem to be Addressed?  Patients with Chronic Obstructive Pulmonary Disease (COPD) feel that 
their needs are not met within the current healthcare system (11).  In a scoping review of a decade’s worth of 
qualitative evidence, patients with COPD reported feeling poorly informed about their condition, vulnerable 
when hospitalized, unconfident in community based services, and lacked an understanding of their disease 

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trajectory and significance of their exacerbations (11).  Most importantly, patients reported that their 
subjective distress seemed invisible to clinicians who tended to focus on objective health indicators, and that 
clinicians infrequently addressed or referred patients for assistance with their substantial non-medical needs 
(7). 
From a health system perspective, COPD exacerbations are a leading cause of hospitalizations and re-
admissions in Canada, and cost our health system over 700 million dollars annually (12,13). In a closer review of 
hospital-related costs for COPD care, the highest costs were incurred by patients with acute illness, but also by 
frail patients who developed significant functional limitations and could not return home (14).   
Both the patient and health system perspectives suggest that our current paradigm for COPD care in Canada is 
not optimal. While health providers focus on COPD-specific metrics, which have been correlated with future 
risk of mortality and hospitalization (15–17), clinicians lack appropriate evidence based tools to evaluate the 
non-pulmonary complications of COPD and overall wellbeing of these patients (18).    
In this setting, there is a need for innovative care approaches which (1) reliably predict the outcomes that are 
important to patients living with COPD, (2) are easily implemented in the health system, and (3) have a direct 
impact on front line COPD management across health care settings.  Care models which incorporate 
assessments of ‘Frailty’ may provide this desired innovative approach to enhance patient-centered COPD care. 
There are currently no studies to determine whether health providers could use measurements of frailty to 
accurately predict current and future decline in patient-centered outcomes, and whether patients’ 
expectations of healthcare change between different degrees of frailty.   
The aim of this research program is to validate frailty measurements as a risk stratification tool to predict 
deteriorations in patient-reported outcomes, to gain a better understanding of patient preferences for care 
within varying degrees of frailty, and to assess differences in frailty measurement between patients and their 
care team. 
This work will provide a foundation in the development of customized interventions for people with COPD and 
varying degrees of frailty, with the goal of improving quality of life and optimizing health system service 
delivery for patients and their care givers.  
What is Frailty and Why is it a Relevant Health Measure?   Frailty is a common and important multi-
dimensional syndrome characterized by a loss of reserve in multiple domains including physical ability, energy, 
cognition, and health (4). Loss of reserve in the frail patient leads to an increased vulnerability to adverse 
health outcomes such as hospitalization and death (1,4,19).  As frailty progresses, the ability to recover from 

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both chronic and acute health stressors becomes more difficult, and leads to progressive functional disability 
(19).  
More than half of patients over 85 years of age are identified as frail (1).  However, age alone does not account 
for functional decline (20).  The frailty construct offers a comprehensive approach to identify patients at risk of 
decline who would benefit from additional resources in order to address their needs. For example, frail people 
have been found to benefit from exercise programs to reduce falls, nutritional interventions, cognitive 
assessment and support, and mobility and balance training (21).  
Frailty has also been identified as an important prognostic tool in patients with other chronic diseases such as 
heart and kidney disease (22,23).  Experts hypothesize that using frailty assessments may help clarify which 
interventions and treatments can benefit or harm patients, help to refine clinical decisions, improve 
prognostication, and enhance informed decision making (24–26).   
For healthcare providers, recognition of frailty in patients is crucial to facilitate identification of patients at high 
risk of poor health outcomes such as progressive disability, hospitalization and death (2).  
Why is Frailty Relevant to COPD Care, and Why is it not Currently Used in Patient Evaluation?  The prevalence 
of frailty among people with COPD ranges widely from 16-75%, depending on the population studied, and the 
frailty measurement tool used (3).  In our own prospective assessment of 50 hospitalized patients with COPD 
exacerbation, 54% were found to be at least mildly frail at their baseline, two weeks prior to their 
hospitalization (27). 
Studies demonstrate that people with COPD have a two-fold increase in the prevalence of frailty compared to 
patients without COPD, after adjustment for confounding factors including age, smoking, steroid use, and 
comorbidities (6).  Presence of frailty is also correlated with COPD-specific markers, including severe airflow 
obstruction, shortness of breath, and frequent exacerbations (6). Among people with COPD, frailty is 
associated with increased risk of death,  poor recovery after hospitalization, and re-admission to hospital 
(5,6,8). Emerging evidence suggests that higher degrees of frailty are also associated with poorer acceptance of 
illness, which may have significant implications for adherence to treatment and self-management (28).   
Traditional care discussions of treatment and prognosis between patients and physicians often center around 
the current COPD guidelines, which focus on disease-specific metrics (i.e. lung function and exacerbation 
history) (16,29). While these metrics are important, one of the limitations of this approach is that it overlooks 
clinically relevant complications of COPD, such as co-morbidities, reduced functional capacity, and 
psychological well-being. These factors contribute to poor quality of life and clinical outcomes and are equally 
important to address (30). Frailty measurements could complement disease specific metrics when evaluating 

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patients and could influence care in a way that FEV1 measurements do not; however, we lack the research 
studies to support this hypothesis.  
Given the association between frailty and poor outcomes among patients with COPD, an understanding and 
early recognition of frailty by health providers and patients could determine the choice and timing of 
interventions, and improve the long term clinical response (3,7).   
How is Frailty Measured? There are multiple validated tools that can assess and quantify the degree of frailty 
in a patient (31). The Clinical Frailty Scale (CFS) is a bedside clinical tool that is easy to use, freely available, 
validated in the Canadian population, and correlates with objective clinical outcomes of death and need for 
long term care (Figure 1) (4).  The CFS is also validated for use by patients to self-report frailty (32).  Although 
there are no direct comparisons between frailty tools in the COPD population, among patients with cirrhosis, 
the CFS had greater discriminatory power for outcomes in comparison with the Fried Criteria, and Short 
Physical Performance Battery Scale (33).    
The CFS uses an ordinal scale from ‘1’ to ‘9’ to characterizes degrees of frailty from ‘managing well’ to 
‘terminally ill’. Each degree of frailty is accompanied by a description of physical symptoms and functional 
ability to perform instrumental activities of daily living (IADL’s) and activities of daily living (ADL’s).  Although 
there is a degree of subjectivity in assigning a score, increasing degrees of frailty are objectively associated with 
increased risk of death and need for long term care in adult patients over 65 years of age (4).  
Why is it Important for Patients and their Providers to have a Mutual Understanding of Frailty?  There is 
rightfully a heightened focus on patient-centered care in our health system (34). Patient-centered care refers 
to the active collaboration and shared decision-making between patients, families and health providers to 
create and implement a personal comprehensive care plan (34).  To operationalize this, patients must also have 
a sound understanding of their disease, prognosis and care needs.  
Poor recognition of frailty or discordance between patient and healthcare providers’ frailty assessments, may 
impact patient-centered care.  
If a patient under-perceives their degree of frailty, they may not agree with or adhere to the prescribed 
treatment plans; for example, recognizing the need for pulmonary rehabilitation, home care services, or 
dialogue regarding symptom management and end of life care. If the healthcare team underperceives a 
patient’s frailty, this can lead to delays in requesting additional resources, and possible over treatment with 
more invasive therapies that may not benefit a very frail patient. A discordance between patients and their 
healthcare providers may be a factor which contributes to the increased dissatisfaction and fragmentation of 
care, that is voiced by patients with COPD (11).  

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3.0 OBJECTIVES 
The aim of this research program is to evaluate frailty measurements as a risk stratification tool to predict 
deteriorations in patient-reported outcomes, to gain a better understanding of patient and physician 
preferences for care within varying degrees of frailty, and to assess the change of frailty measurements over 
time. More specifically, our study objectives are:  
1. To determine the predictive value of progressive degrees of frailty on deterioration in patient-centered 
outcomes and increased health care utilization among people with COPD, after adjustment for 
important clinical factors including age, sex, comorbidity, home support, number of medications, 
pulmonary rehabilitation, lung function, dyspnea, and exacerbation history.    
2. To describe the differences in expectations of clinical care between physicians and patients within 
different degrees of frailty.  
3. Determine the inter-rater agreement between patients and physicians who use the Clinical Frailty Scale 
(CFS) to measure frailty. 
4.0 Investigate the change in frailty scores over 12 monthsHYPOTHESIS 
We hypothesize that progressive degrees of frailty will reliably predict important deteriorations in patient-
centered outcomes over time, including health-related quality of life, symptom burden, and psychosocial well 
being, and measures of survival and health service use.  If this association between frailty and health is 
confirmed, then it follows that frailty could be used as a risk stratification tool to guide tailored interventions 
for population subgroups with varying degrees of frailty.   
5.0 METHODS 
5.1 Design & Setting: A prospective cohort study at The Ottawa Hospital (TOH) over two years. TOH is 
a quaternary care academic hospital with an ambulatory care respiratory clinic, staffed by seven 
respirologists. It is a regional referral center with a catchment area of approximately 1 million 
individuals. 
 
5.2 Study population and Inclusion Criteria: We will include literate English or French-speaking 
outpatients with COPD, objectively confirmed by lung function testing1. In patients with multiple 
respiratory conditions and comorbidities, COPD will be the primary diagnosis, as confirmed by the 
treating respirologist.  Participants will be recruited from the respirology ambulatory care clinics at 
TOH. We will exclude patients with a known diagnosis of severe cognitive impairment2, if the 
 
1 post-bronchodilator Forced Expiratory Volume in 1 second / Forced Vital Capacity ratio below the lower limit of normal, 
and >10pack years history of tobacco smoking 
2 as per the medical record 

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recruiting physician feels the impairment would preclude participation in the study.   
 
5.3 Study flow: Prior to the clinic, members of the study team (PI and coordinator) will review the list 
of patients scheduled for a routine visit in the Respirology clinic of TOH during the study period. 
Patients who have a diagnosis of COPD will be flagged as “identified” in EPIC for the study. The 
name (first and last) and MRN will be included in a Master List. This list will link patients’ 
identifiable information (MRN and name) with a screen ID. The screen ID will be included in an 
“appointment note” within the physician’s schedule in EPIC, flagging the care team that the patient 
is possibly eligible for the study.  
 
During their routine visit with respirologist in the Respirology outpatient clinic of TOH, patients will 
be assessed for eligibility. Prior to meeting with the respirologist, the nurse will complete their 
standard patient assessment. They will review the posted note left by the research team for that 
specific patient and assess the patient for eligibility. If eligible, the nurse will ask the patient if they 
agree to have a member of the study team approach them about the study. If the patient agrees to 
be contacted, the physician would assess patients’ frailty using the CFS, include the frailty rating 
and their care expectations for the patient in a form (see Recruitment Survey), with the screen ID, 
provided in the appointment note. This form would be automatically available to the research 
team, which would allow them to contact the patient to obtain verbal consent (see Verbal 
Consent) and schedule a baseline study visit. Please note, once the patient has been assessed for 
eligibility, the screen ID becomes the study ID for that patient.  
 
Consent: The study team will call the patient to obtain verbal consent. If the patient agrees, the 
study team will schedule a baseline visit by phone with a member of the study team. Once 
consented, the study team will request an e-mail address to send the baseline electronic survey 
(see Patient Baseline Survey) and the informed consent document for additional information on 
the study.  
 
Baseline visit:  Prior to each scheduled visit, the baseline survey will be e-mailed to the patient 
with a reminder of their upcoming appointment with a member of the study team. For those who 
do not have or have not provided an e-mail address, a member of the team will call the patient to 
remind them of their upcoming appointment. During the telephone baseline visit, patients will be 
asked questions on their lung health (see Data Collection Sheet – Baseline Visit). If the electronic 

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Baseline Survey was not completed, the study team will go through the questions with the patient. 
At the end of the visit, the study team will schedule the 6-month telephone visits and ask the 
patient to confirm their mailing address to send the first of three gift coffee cards (of 20$), as a 
token of appreciation.  
 
6-month and 12-month follow-up visits: Prior to the scheduled 6-month or 12-month follow-up 
visit, the study team will send an e-mail to patient with a link to the COPD & Frailty – 6-month 
follow-up survey or COPD & Frailty – 12-month follow-up survey. Patients who did not provide an e-
mail address will receive an appointment reminder call from a member of the study team and 
complete the survey with the study team during their 6 and 12-months telephone follow-up visits. 
At the end of each telephone visit, patients will be asked to confirm their mailing address to send 
the second and third gift cards (of 20$). 
 
 
5.4 Sample Size:  Sample size calculations were performed based on the primary analysis strategy, 
which uses multiple variable linear regression with a fixed model. We will determine the adjusted 
association between frailty (categorized in 4 groups using the CFS: ‘vulnerable’, ‘mildly frail’, 
‘moderately frail’ and ‘severely/very severely frail’) and respiratory health-related quality of life as 
measured by the validated SGRQ-C, scored from 1-100.  The minimally important clinical difference 
(MCID) for the SGRQ-C is 4 (41).  We will adjust the model for confounding variables including age, 
sex, dyspnea severity (mMRC), hospitalization, lung function, home support services, and 
comorbidity scores.  
 
In a previous sample of patients with COPD, the standard deviation of SGRQ scores ranged from 13 
to 18 (9,42–44). To detect an effect size of 0.2 to 0.3, with 10 predictors and α error 0.05 and 
power 0.80 (1-β error), we require a range between 64 to 84 individuals (G*Power 3.1.9.2. 
Copyright (C) 1992-2014, see Appendix 1). 
 

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Further, Harrell suggested that 10 subjects per variable is the minimum required sample size for 
linear regression models. To ensure accurate prediction in subsequent study participants, we will 
need 100 individuals to include 10 predictors in our statistical model (50). 
 
Based on the above calculations, we aim to recruit a minimum of 120 patients into this study over 
6 months with a minimum of 30 patients in each of the four frailty categories vulnerable, mild, 
moderate, and severe/very severe.  We will continue to recruit patients from the ambulatory clinic 
until the minimum targets are met.   
 
5.5 Data collection methods 
5.5.1 Patient Self-Assessed Frailty: Patients will use the CFS to self-assess frailty at their 
clinical baseline, which measures nine distinct degrees of frailty from ‘managing well’ 
to ‘terminally ill’ (Figure 1).  The wording in the CFS has been adapted and validated 
for patient-use (32).   
5.5.2 Patient Expectations of Health Care: Patients will review and rank items on a 
standardized list of care interventions (i.e. pulmonary exercise programs, homecare, 
self-management education). The study team developed the list based on services 
recommended in Health Quality Ontario’s updated ‘Quality Standards for COPD’ 
document (38), and currently available community-based programs to help 
individuals with their functional, social, and lung health needs.  The document will be 
written in lay language and a member of the research team will ensure items on the 
list are well understood by patient participants. Patient participants will rank each 
item on a likert scale from 1 (least important), to 5 (most important), to indicate their 
care preferences. (COPD & Frailty - Baseline survey) 
 
5.5.3 Patient Baseline Health Data (see Data Collection Sheet – Baseline visit): We will 
collect variables from direct patient assessment and review of the electronic medical 
record at the time of recruitment:  
• 
Socio-Demographic Data (age, sex, body mass index, physician-assessed clinical 
frailty scale score, current dwelling, caregiver support, marital status, education 
level, type of home assistance services, and duration of time since initial 
diagnosis).  

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• 
COPD-Specific Factors (Spirometry values (FEV1, FVC, FEV1/FVC), smoking history 
in pack-years, home-oxygen use, 6-minute walk test distance (if available), 
influenza and pneumococcal vaccination status, use of pulmonary rehabilitation, 
and number of flare-ups in the last year.  
• 
General Health Data (co-morbidities, presence of family physician).  
• 
Previous Health Service Utilization (hospitalizations, intensive care unit 
admissions, emergency room visits in last 1-year). 
5.5.4 Physician Data Collection:  We will ask the referring respirologist (in the patient’s 
circle of care) to provide a frailty measurement at the time of study enrolment, using 
the CFS (Figure 1). This physician will also rank their desired care interventions for the 
patient using the same standardized list given to study participants (see Recruitment 
Survey). 
5.5.5 Frailty assessment by research team: Frailty will be assessed during the 6-month and 
12-month follow-up telephone visits, using the standardized CFS classification tree. 
(Appendix 2) 
 
5.6 Outcomes 
5.6.1 Primary:   The primary outcome is respiratory Health Related Quality of Life (HRQOL), 
as measured by the validated short version of St. Georges Respiratory Questionnaire 
(SGRQ-C) (39). The SGRQ-C is a 40-item questionnaire which generates an overall 
score between 1 and 100, with lower scores indicating better quality of life (39,40). 
The minimally important clinical difference in SGRQ-C scores is well established at 4 
(41).  The SGRQ is a widely used outcome in respiratory research, and there are 
established standard deviation measurements in the COPD patient population (42–
44). These will be collected with the COPD & Frailty – baseline, 6-month, and 12-
month surveys. 
5.6.2 Secondary: We will measure burden of symptoms with the COPD Assessment Test 
(CAT) and the modified Medical Research Council (mMRC) scales (45,46). Both scales 
are validated and widely used in respiratory research.  These will be collected with 
the COPD & Frailty – baseline, 6-month, and 12-month online surveys. 
 
Psycho-social well being will be measured with the validated Generalized Anxiety 
Disorder (GAD)-7 and Patient Health Questionnaire (PHQ)-9 scales (47,48). The GAD-7 
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presence of depressive symptoms in the prior two weeks (47–49) . Both scales are 
composed of seven items, based on the DSM-IV criteria, and a score above 7 on each 
scale will indicate the presence of anxiety or depression for this study (48,49). We will 
also collect outcomes of mortality, and health service utilization including 
hospitalizations (during study period), emergency room visits, and changes of 
dwelling to an assisted living environment. These will be collected with the COPD & 
Frailty – baseline, 6-month, and 12-month online surveys. 
 
5.7 Statistical Methods:  
5.7.1 Quantitative Analysis (Objective 1): The primary outcome (HRQOL using SGRQ-C 
scores) will be analyzed using multiple variable linear regression, with degree of 
frailty as the main predictor (‘vulnerable’, ‘mildly frail’, ‘moderately frail’, and 
‘severely/very severely frail’).  Secondary outcomes for symptom burden (CAT and 
mMRC scales) and psycho-social wellbeing (GAD-7 and PHQ-9) will be described with 
median (IQR) statistics within each degree of frailty and analyzed with multiple 
variable linear regression to determine their associations with progressive degrees of 
frailty.  Finally, the association between frailty and health service utilization 
(hospitalization, emergency room visit) and mortality will be analyzed with 
multivariable logistic regression. Potential confounding variables will be selected a-
priori and forced into the model, regardless of statistical significance. Backwards 
selection will be used to obtain the most parsimonious model. 
 
5.7.2 Quantitative Analysis (Objective 2): We will use descriptive statistics (proportions, 
means ± SD, medians (IQR)) to characterize the ranked care expectations, socio-
demographic, and disease-specific, variables.  Variables will be stratified by physician-
assessed degrees of frailty, and chi-square and ANOVA testing will be used to analyze 
differences in variables across degrees of frailty, for categorical and continuous 
variables, respectively. 
 
5.7.3 Quantitative Analysis (Objective 3 & 4): We will calculate the percentage of 
agreement to assess inter-rater agreement between patients’ self-assessed and 
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cross-tabulation table. We will graph the trajectory of frailty rating between baseline, 
6-month, and 12-month visits for the study cohort. 
 
5.8 Data sources 
▪ 
Patient surveys: For each study visit, patients will be asked to complete surveys relating to: CFS, 
SGRQ-C, mMRC Dyspnea Scale, CAT, GAD-7 and PHQ-9 .  
▪ 
Physician surveys: CFS and care expectations for patient at time of enrolment 
▪ 
Electronic Medical Record: Baseline health data, socio-demographic data, COPD-specific data, 
previous health care service utilization (1 year prior to study enrolment) 
▪ 
Baseline telephone visit with research team: baseline health data, COPD specific data, previous 
health care service utilization. 
▪ 
6 & 12 month telephone visits with research team: CFS, change in health status (stress event or 
falls) since last visit. 
 
6.0 STUDY TIMELINE  
 
• 
Pre-implementation & Education of study staff and co-investigators (December 2019 – February 
2020): 
o Research Ethics Board application and study set-up 
o Prepare study documents (patient information sheets, physician reminders, poster of CFS) to 
increase awareness and reminder to enroll patients 
o Host session with respirologists from TOH General Campus to review how to use the CFS, 
review study process, and research plan 
• 
Hire (February-April 2020):  
o Hire research assistant to assist with the collection of data. 
o Acquire equipment necessary for the study conduct (iPad, study laptop). 
• 
Implementation & Data Collection (November 2022- March 2024):  
o Recruit patients from the Respirology outpatient clinic of TOH, General campus, obtain consent 
(Novemberl 2022 to March 2024) 
o Collect patient data (at baseline, 6 months, and 12-month follow-ups) with study staff. 
• 
Data Analysis (March 2024):  

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o Quantitative data analysis performed by an OHRI methodologist from the Ottawa Hospital 
Performance Measurement team.  
• 
Knowledge Translation & Next Steps (April 2024 and beyond): 
o Manuscript submissions and plan for next steps 
 
7.0 DATA MANAGEMENT 
The data collected will be password protected and will not leave TOH. Only study personnel will have 
access to the study data. A study master list (containing MRN and patient name) will be linked to a study 
ID. This master linking file will be kept separate from the main study dataset in a protected folder on a 
secure TOH sever. Only the unique study ID will be used on the patient surveys and study case report 
forms. The study files will not be stored on the hard drives of portable devices or on USB keys. 
 
8.0 CHALLENGES AND MITIGATION STRATEGIES 
8.1 Patient Recruitment & Transportation: We may experience difficulty recruiting patients who are 
severely frail.  However, we have chosen The Ottawa Hospital ambulatory care clinic as a referral 
base, where the level of staff engagement for research is high, and patient volume is adequate. To 
ensure adequate recruitment in the higher degrees of frailty, we have grouped the ‘severe’ and 
‘very severe’ categories together. Since patients will not require to be on site for study visits, we 
believe that this will help with participation. 
8.2 Study Adherence: We will provide a $20 coffee gift card incentive for patients to attend the initial 
study visit, 6 month visit, 12-month visit.  This is intended to improve adherence to the study 
protocol and mitigate losses to follow up. Enrolled patient participants will receive personalized 
communication from the study coordinator to increase their engagement and participation in the 
study.  
8.3 Patient Participation: While frailty is a widely accepted medical construct that is frequently used 
by health professionals to describe health status, the term ‘frailty’ may be associated with negative 
perceptions from the patient perspective (37,55).  We anticipate that patients may feel stigma 
attached to using the frailty construct to describe their health and may decline to participate as a 
result. To mitigate this potential challenge, the initial introduction and consent for the study will be 
done by the patient’s respirology circle of care (respirologist or nurse), with whom they have a 
therapeutic alliance.  We will also ensure the use of impartial language during the study visits to 
enhance patient comfort-level. 
 
9.0 EXPECTED IMPACT OF THIS WORK 

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Our study will inform several aspects of care for patients with COPD: a) the validity of frailty assessments as 
a long-term predictor of patient-centered outcomes; b) the reliability and accuracy of the assessments of 
frailty, c) the patient’s preferences of care according to the different levels of frailty; and d) any potential 
differences in the perception of frailty between patients and physicians and whether this may have a 
potential impact on clinical outcomes. We believe that these results will facilitate a practice change to 
introduce frailty measurements as a risk stratification tool for health providers (and patients), and to 
enhance personalized care according to patient preferences. This study will provide important context for 
our next steps to design and test the impact of personalized, patient-centered care interventions in 
subgroups of frail patients with chronic lung disease. 
 
10.0 
FUNDING & COLLABORATION 
10.1 
Funding: This study is funded by a research grant: CIHR-ICRH/AZ Canada/CLA Emerging 
Clinician Scientist Award (2019). We were awarded $174,480.75 for a duration of two years with 
one-year extension due to COVID-19 (April 1, 2020 to March 31, 2024).  
 
10.2 
Multidisciplinary team: We have assembled a multidisciplinary team of pulmonary physicians: 
S. Mulpuru (early career investigator, OHRI), M. Chin (early career investigator, OHRI), Dr G. 
Alvarez (mid-career investigator, OHRI), S. Pakhale (mid-career investigator, OHRI); Dr. S. Aaron 
(senior investigators, OHRI), Dr. N Voduc Clinical frailty experts: Dr M. Andrew (mid-career 
investigator, Dalhousie University), S.Huang (early career investigator, OHRI); Health Service 
Researchers: A. Forster (senior investigator, OHRI), S. Mulpuru, T. Kendzerska (early career 
investigator), and highly accomplished senior biostatisticians and measurement experts: T. Ramsay 
(senior investigator, OHRI), J. Brehaut (senior investigator, OHRI) to complete this project. Our 
research team has completed and published preliminary work to support this research proposal 
(13) (26), and currently conducting ongoing work to study the use of frailty measurements in 
clinical care re-design (56). 
 
Dr Sunita Mulpuru, the nominated principal applicant, is an early career researcher. She’s a 
respirologist at the Ottawa Hospital, Assistant Professor at the University of Ottawa, and Associate 
Scientist at the Ottawa Hospital Research Institute. She will receive mentorship from highly 
accomplished senior scientists who are Co-PI's and CO-I's on this proposal; Dr's Melissa Andrew, 
Alan Forster, Shawn Aaron, Jamie Brehaut, and Tim Ramsay. All of the listed mentors have 
provided significant input into developing this research proposal and will assist in its' execution. 

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10.3 
Research Environment: The Ottawa Hospital’s division of Respirology will provide the home-
base for patient recruitment. This respiratory clinic is staffed by seven academic respirologists, and 
receives 6000 patient visits per year (53). Our group estimates seeing 50 patients with COPD per 
month (54). This will serve our recruitment target of >=10 patients per month, over 12 months, to 
ensure a final sample size of 120 patients. Our team has access to methodological, statistical, 
scientific support, and knowledge translation experts from the Ottawa Hospital Research Institute 
Methods Center (with whom we are affiliated).  
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Figure 1 – Clinical Frailty Scale (CFS) 
 
 
 
 
 
 
 
 
 

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Appendix 1 – Sample Size Calculations based on Primary Analysis using Linear Regression, with Standard 
Deviation Values from the Literature 
F tests - Linear multiple regression: Fixed model, R² deviation from zero 
Analysis: 
A priori: Compute required sample size  
Input: 
Effect size f² 
= 0.22 (MCID = 4 / SD = 18) 
 
α err prob 
= 0.05 
 
Power (1-β err prob) 
= 0.80 
 
Number of predictors 
= 10 
Output: 
Noncentrality parameter λ 
= 18.4800000 
 
Critical F 
= 1.9630582 
 
Numerator df 
= 10 
 
Denominator df 
= 73 
 
Total sample size 
= 84 
 
Actual power 
= 0.8039786 
 
F tests - Linear multiple regression: Fixed model, R² deviation from zero 
Analysis: 
A priori: Compute required sample size  
Input: 
Effect size f² 
= 0.30 (MCID = 4 / SD = 12.8) 
 
α err prob 
= 0.05 
 
Power (1-β err prob) 
= 0.80 
 
Number of predictors 
= 10 
Output: 
Noncentrality parameter λ 
= 19.2000000 
 
Critical F 
= 2.0147024 
 
Numerator df 
= 10 
 
Denominator df 
= 53 
 
Total sample size 
= 64 
 
Actual power 
= 0.8013694 
 
Reference: Faul F, Erdfelder E, Buchner A, & Lang A.G (2009). Statistical power analyses using G*Power 3.1: 
Tests for correlation and regression analyses. Behavior Research Methods, 41, 1149-1160. 
 

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Appendix 2 – CFS Classification Tree 
 
Theou, O; Perez-Zepeda, M; Van der Valk, A; et al. A classification tree to assist with routine scoring of the Clinical Frailty Scale. 
Age and Ageing. 2021; 50:1406-1411