Enhancing Cognition by using Low-cost In-home Portable Sleep Equipment: A Feasibility Study

by

David Robert Colelli

A thesis submitted in conformity with the requirements for the degree of Master of Science

Institute of Medical Science University of Toronto

© Copyright by David Robert Colelli 2019

ii

Enhancing Cognition by using Low-cost In-home Portable Sleep

Equipment: A Feasibility Study

David Robert Colelli

Master of Science

Institute of Medical Science University of Toronto

2019

Abstract Obstructive sleep apnea (OSA), which causes pauses in breathing during sleep, increases the risk

of developing cognitive impairment. In-laboratory polysomnography (iPSG) is the gold standard

to diagnose OSA, but few patients are screened by iPSG due to refusal to spend a night in a sleep

laboratory. Home sleep apnea testing (HSAT) may be a more accessible alternative, as it is

simple to use, conveniently administered in a patient’s own home, and validated against iPSG.

This study investigated the feasibility and practicality of using HSAT in cognitively impaired

clinic patients and assessed the prevalence and factors associated with OSA. HSAT was found to

be feasible and practical to assess for OSA. OSA was also prevalent in this population. As OSA

is a modifiable risk factor for patients with cognitive impairment, HSAT has the potential to lead

to expedited treatment for OSA, which may potentially improve health-related outcomes such as

cognition.

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Acknowledgments

I am very appreciative for all of the support and guidance I received during my graduate studies. I am forever grateful for the continual support from my primary supervisor, Dr. Mark Boulos. My first experience with clinical research was with Dr. Boulos during a fourth year undergraduate research project that I completed with him. He fostered my interest in research, which in turn led me to pursue a Master’s of Science Degree. I am very thankful for his mentorship, as it has allowed me to grow and become a better researcher and individual.

I would also like to thank my co-supervisor, Dr. Sandra Black, for her mentorship during my graduate studies. Her knowledge and passion for research has encouraged me to continue to ask questions and seek those answers through research. Thank you to Dr. Mario Masellis and Dr. Andrew Lim for providing mentorship and critical review during committee meetings and keeping your office doors open to discussions. Also, thank you to Dr. Benjamin Lam for being available and open to discussing and assisting me in appreciating disease processes.

Thank you to the all the people I worked with at the LC Campbell Cognitive Neurology Research Unit at Sunnybrook Health Sciences Centre. You welcomed me to the team and made my time enjoyable day in and day out.

To the patients and caregivers who participated in the study, I am thankful for their interest and dedicating their time and efforts to complete the study. The findings of this thesis would not have been possible without their participation.

I am also grateful for the funding support of Dr. Sandra Black’s LC Campbell Cognitive Neurology Research Unit and the Ontario Graduate Scholarship, Faculty of Medicine, University of Toronto. Without the funding support provided, I would not have been able to pursue my research interests.

Finally, I am thankful to my parents, Angie and Robert, and to my siblings, Teresa and Paul, for their continued love and support during my undergraduate and graduate studies. They have provided me with a strong support system to rely on and have encouraged me to continue to pursue my academic interests to my fullest capabilities.

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Contributions David Colelli (Author) solely prepared the present thesis. The protocol for this study was proposed by Dr. Mark Boulos (Principle Investigator). The author carried out the protocol and approached, recruited and completed baseline assessments for all participants involved. The author also provided assistance and/or troubleshooting of the home sleep apnea test (HSAT) via phone to patients when necessary. The author learned how to manually review and score raw tracings of the HSAT with the help of Dana Jewell and Dr. Boulos. The author and Dr. Boulos then reviewed the raw tracings of the HSAT for manual scoring. The author completed the statistical analyses in the present thesis and received guidance from his Program Advisory Committee.

Dr. Mark Boulos (Principle Investigator, Primary Supervisor and Program Advisory Committee Member) was the Principle Investigator for the study and proposed the protocol and objectives for this study. Dr. Boulos provided guidance, review and feedback for the current thesis project during Program Advisory Committee meetings and individual meetings. Dr. Boulos also provided input, feedback, and critical review during the preparation of this thesis.

Dr. Sandra Black (Co-Supervisor and Program Advisory Committee Member) provided guidance, review and feedback for the current thesis project. Dr. Black also assisted in the recruitment process and referred patients to the study from her Cognitive Neurology clinic at Sunnybrook Health Sciences Centre.

Dr. Mario Masellis (Program Advisory Committee Member) provided guidance, review and feedback for the current thesis project. Dr. Masellis also assisted in the recruitment process and referred patients to the study from his Cognitive Neurology clinic at Sunnybrook Health Sciences Centre.

Dr. Andrew Lim (Program Advisory Committee Member) provided guidance, review and feedback for the current thesis project.

Dr. Benjamin Lam assisted in the recruitment process and referred patients to the study from his Cognitive Neurology clinic at Sunnybrook Health Sciences Centre.

Dr. Sara Mitchell assisted in the recruitment process and referred patients to the study from her Cognitive Neurology clinic at Sunnybrook Health Sciences Centre.

Areti Apatsidou, Frank Tran and Arman Abedin assisted the author in the administration of the Toronto Cognitive Assessment.

Dana Jewell provided assistance in the manual scoring of raw tracings of the HSAT.

The Ontario Graduate Scholarship, Faculty of Medicine, University of Toronto, provided funding for year 2 of the author’s graduate program valued at $15,000.

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Table of Contents

ACKNOWLEDGMENTS....................................................................................................III

CONTRIBUTIONS..............................................................................................................IV

TABLE OF CONTENTS......................................................................................................V

LIST OF TABLES.............................................................................................................VIII

LIST OF FIGURES..............................................................................................................IX

LIST OF APPENDICES.......................................................................................................X

LIST OF ABBREVIATIONS...............................................................................................XI

CHAPTER 1: GENERAL INTRODUCTION......................................................................1

1.1 Neurodegenerative and Vascular Dementias..............................................................................................................21.1.1 Alzheimer’s disease........................................................................................................................................................................2

1.1.1.1 Etiology.....................................................................................................................................................................................21.1.1.2 Pathogenesis............................................................................................................................................................................31.1.1.3 Treatment and Management...............................................................................................................................................5

1.1.2 Vascular Dementia..........................................................................................................................................................................61.1.2.1 Etiology.....................................................................................................................................................................................61.1.2.2 Treatment and Management...............................................................................................................................................7

1.1.3 Mild Cognitive Impairment..........................................................................................................................................................91.1.3.1 Mild Cognitive Impairment due to Alzheimer disease..............................................................................................91.1.3.2 Vascular Mild Cognitive Impairment..........................................................................................................................11

1.1.4 Other Related Dementias: Dementia with Lewy Bodies and Parkinson’s Disease Related Dementia...........121.1.5 Mixed Diseases.............................................................................................................................................................................131.1.6 Impact of Dementia on Society...............................................................................................................................................14

1.2 Obstructive Sleep Apnea...............................................................................................................................................171.2.1 Etiology............................................................................................................................................................................................171.2.2 Prevalence of Sleep Apnea........................................................................................................................................................191.2.3 Questionnaires to Screen for Sleep Apnea...........................................................................................................................20

1.2.3.1 Berlin Questionnaire..........................................................................................................................................................211.2.3.2 STOP and STOP-Bang Questionnaire.........................................................................................................................211.2.3.3 Epworth Sleepiness Scale.................................................................................................................................................221.2.3.4 Accuracy of Questionnaires in Screening for Sleep Apnea..................................................................................22

1.2.4 Diagnosing Sleep Apnea............................................................................................................................................................231.2.4.1 In-laboratory Polysomnography....................................................................................................................................231.2.4.2 Home Sleep Apnea Tests.................................................................................................................................................23

1.2.5 Treatment and Management of Obstructive Sleep Apnea..............................................................................................241.2.5.1 Continuous Positive Airway Pressure..........................................................................................................................251.2.5.2 Positional Devices and Oral Appliances.....................................................................................................................261.2.5.3 Surgery and Weight Loss.................................................................................................................................................27

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1.3 Sleep and Cognition........................................................................................................................................................291.3.1 Relationship between Sleep Quality and Cognition.........................................................................................................291.3.2 Sleep Apnea and Cognitive Impairment...............................................................................................................................311.3.3 Treatment of Sleep Apnea in Patients with Cognitive Impairment.............................................................................33

1.4 Neuropsychological Tests to Assess Cognitive Impairment...................................................................................361.4.1 Mini-Mental State Examination..............................................................................................................................................361.4.2 Montreal Cognitive Assessment Tool...................................................................................................................................371.4.3 Toronto Cognitive Assessment................................................................................................................................................371.4.4 Psychomotor Vigilance Task....................................................................................................................................................38

1.5 Research Aims and Hypothesis....................................................................................................................................401.5.1 Primary Objectives.......................................................................................................................................................................401.5.2 Secondary Objectives..................................................................................................................................................................41

CHAPTER 2: FEASIBILITY OF HOME SLEEP APNEA TEST IN A COGNITIVELY IMPAIRED POPULATION................................................................................................42

2.1 Abstract.............................................................................................................................................................................43

2.2 Introduction......................................................................................................................................................................45

2.3 Methods.............................................................................................................................................................................472.3.1 Ethics................................................................................................................................................................................................472.3.2 Study Population...........................................................................................................................................................................472.3.3 Study Procedure and Assessments..........................................................................................................................................482.3.4 Home Sleep Apnea Test.............................................................................................................................................................492.3.5 Outcome Measures.......................................................................................................................................................................502.3.6 Statistical Analyses......................................................................................................................................................................51

2.4 Results................................................................................................................................................................................52

2.5 Discussion..........................................................................................................................................................................60

2.6 Conclusion.........................................................................................................................................................................63

CHAPTER 3: PREVALENCE OF OBSTRUCTIVE SLEEP APNEA IN A PATIENT POPULATION WITH COGNITIVE IMPAIRMENT.......................................................64

3.1 Abstract.............................................................................................................................................................................65

3.2 Introduction......................................................................................................................................................................67

3.3 Methods.............................................................................................................................................................................693.3.1 Ethics................................................................................................................................................................................................693.3.2 Study Population...........................................................................................................................................................................693.3.3 Study Procedure and Assessments..........................................................................................................................................703.3.4 Home Sleep Apnea Testing.......................................................................................................................................................703.3.5 Outcome Measures.......................................................................................................................................................................713.3.6 Statistical Analyses......................................................................................................................................................................72

3.4 Results................................................................................................................................................................................73

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3.5 Discussion..........................................................................................................................................................................77

3.6 Conclusion.........................................................................................................................................................................80

CHAPTER 4: GENERAL DISCUSSION...........................................................................81

4.1 General Discussion..........................................................................................................................................................824.1.1 Feasibility and Practicality of HSAT.....................................................................................................................................824.1.2 OSA Prevalence and Risk Factors..........................................................................................................................................854.1.3 Thesis Limitations........................................................................................................................................................................86

CHAPTER 5: CONCLUSIONS..........................................................................................88

CHAPTER 6: FUTURE DIRECTIONS.............................................................................89

6.1 HSAT vs. PSG..................................................................................................................................................................89

6.2 Impact of CPAP Therapy on Cognition and Function...........................................................................................89

6.3 Examining the Relationship Between OSA and Cognitive Impairment.............................................................90

6.4 Phenotypes of OSA.........................................................................................................................................................91

REFERENCES....................................................................................................................93

APPENDIX A: HSAT DEVICE, INSTRUCTIONS AND SURVEY................................124

I) Components of the HSAT.............................................................................................................................................124

II) HSAT Patient Instructions.........................................................................................................................................125

III) HSAT Patient Survey.................................................................................................................................................127

APPENDIX B: INFORMED CONSENT FORM..............................................................130

APPENDIX C: COPYRIGHT ACKNOWLEDGEMENTS.............................................138

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List of Tables Chapter 1

Table 1-1 Prevalence of OSA in large observational studies (N≥500) that used in laboratory polysomnography 20

Table 1-2 Classification of Sleep Study Tests 24

Chapter 2

Table 2-1 Prior studies that have used HSAT in a cognitively impaired population 46

Table 2-2 Patient demographics 55

Table 2-3 Obtaining Analyzable HSAT Data by Patient Diagnosis 56

Table 2-4 Logistic Regression for Analyzable HSAT Data 57

Chapter 3

Table 3-1 Patient demographics 74

Table 3-2 Prevalence of OSA by Patient Diagnosis (N=70) 75

Table 3-3 Logistic Regression for Predictors of OSA 76

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List of Figures Chapter 2

Figure 2-1 Patient flow in the feasibility study 52

Figure 2-2 Amount of analyzable HSAT data obtained during the recording 53

Figure 2-3a HSAT Patient Satisfaction Survey 58

Figure 2-3b HSAT Patient Satisfaction Survey 59

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List of Appendices Appendix A: HSAT DEVICE, INSTRUCTIONS AND SURVEY

I) Components of the HSAT 124

II) HSAT Patient Instructions 125

III) HSAT Patient Survey 127

Appendix B: INFORMED CONSENT FORM 130

Appendix C: COPYRIGHT ACKNOWLEDGEMENTS 138

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List of Abbreviations AASM American Academy of Sleep Medicine

AD Alzheimer disease

ADFACS Alzheimer's Disease Functional Assessment and Change Scale

ADL Basic Activities of Daily Living

AHI Apnea-Hypopnea Index

BMI Body Mass Index

BQ Berlin Questionnaire

CADASIL Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and

Leukoencephalopathy

CPAP Continuous Positive Airway Pressure

CSF Cerebrospinal Fluid

DLB Dementia with Lewy Bodies

ESS Epworth Sleepiness Scale

FOSQ Functional Outcomes of Sleep Questionnaire

FRRT Fast Reciprocal Reaction Time

GDS Geriatric Depression Scale

HSAT Home Sleep Apnea Test

IADL Instrumental Activities of Daily Living

iPSG In-laboratory Polysomnography

IQR Interquartile Range

MCI Mild Cognitive Impairment

MMSE Mini Mental Status Examination

MoCA Montreal Cognitive Assessment

MRI Magnetic Resonance Imaging

NMDA N-methyl D-aspartate

NPI Neuropsychiatric Inventory Questionnaire

OSA Obstructive Sleep Apnea

PAP Positive Airway Pressure

PD Parkinson’s disease

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PDD Parkinson’s disease related Dementia

PET Positron-Emission Tomography

PSQI Pittsburgh Sleep Quality Index

PVT Psychomotor Vigilance Task

RBD Rapid Eye Movement Sleep Behavior Disorder

RRT Reciprocal Reaction Time

SBQ STOP-Bang questionnaire

SD Standard Deviation

SDB Sleep Disordered Breathing

SVD Small Vessel Disease

TIA TransientIschemicAttack

TorCA Toronto Cognitive Assessment

VaD Vascular Dementia

VaMCI Vascular Mild Cognitive Impairment

VCI Vascular Cognitive Impairment

WASO Wake After Sleep Onset

1

Chapter 1: General Introduction

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1.1 Neurodegenerative and Vascular Dementias

Dementia can result from several causes, which can include neurodegenerative and/or

vascular etiologies. In 2011, the National Institute on Aging and the Alzheimer’s Association

provided an update on the clinical criteria for dementia (McKhann et al., 2011). Criteria for a

diagnosis of dementia included cognitiveand/orbehavioral symptoms that impact function at

work and/or daily activities, as well as cognitive impairment that is diagnosed by patient history

and objective assessments (McKhann et al., 2011). In the 2018 World Alzheimer Report by

Alzheimer’s Disease International, 50 million people were estimated to have dementia

worldwide, and this number was projected to increase to 82 million by 2030 (Alzheimer’s

Disease International, 2018). The subsequent subsections discuss different types of dementia and

mild cognitive impairments, a milder form of cognitive dysfunction, that are of interest for this

thesis.

1.1.1 Alzheimer’s disease 1.1.1.1 Etiology

The most common cause of dementia is Alzheimer’s disease (AD), which accounts for

approximately 70% of all cases of dementia (Plassman et al., 2007) and is estimated to impact

approximately 5.4 million people in the U.S.A (Alzheimer's Association, 2016). AD is a

devastating disease that is highly prevalent in the population, particularly in elderly individuals.

The incidence of AD increases with age, ranging from 16.6 (72-79 year olds) to 64 (≥90 year

olds) per 1000 person-years (Plassman et al., 2011).

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A confirmed diagnosis of AD pathology can only be made from an autopsy following

death (Hyman and Trojanowski, 1997), however, there is clinical criteria to assist physicians in

identifying and diagnosing probable AD. Criteria for probable AD includes insidious onset of

symptoms, a history of cognitive decline that is either amnestic (i.e. associated with memory

challenges) or non-amnestic (i.e. associated with language, visuospatial and/or executive

dysfunction) (McKhann et al., 2011). This diagnosis can be further supported by subsequent

cognitive assessments that demonstrate cognitive decline as well as the presence of a genetic

mutation known to cause AD pathology and/or biomarkers associated with AD pathology

(McKhann et al., 2011).

Risk factors for AD include age, where older individuals are at increased risk of

developing the disease (Kukull et al., 2002). Education is also believed to play a role, where

individuals with a lower level of education are at increased risk of AD (Kukull et al., 2002).

Moreover, there is a causal relationship between certain genetic mutations that play a role in

amyloid-beta production and are known to cause early-onset AD: amyloid precursor protein,

presenilin 1 and presenilin 2 (Bertram and Tanzi, 2008). Furthermore, having the apolipoprotein

E gene ε4 allele increases an individual’s risk of late-onset AD (Bertram and Tanzi, 2008).

1.1.1.2 Pathogenesis

Two primary pathological findings characterize the degenerative brain changes seen in

AD: amyloid-beta plaques and tau protein tangles (Alzheimer's Association, 2016). These two

proteins naturally appear in the brain, however, accumulation of them can cause severe

problems. Amyloid-beta can accumulate and impair communication between neurons, which can

result in the death of neurons (Alzheimer's Association, 2016). Similarly, tau protein tangles can

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contribute to impairment and death of neurons by obstructing the movement of important

molecules and nutrients (Alzheimer's Association, 2016). These brain changes are believed to

occur before the presentation of clinical symptoms, as the brain is able to compensate; however,

when damage continues and compensation is no longer possible, symptoms start to appear (Jack

et al., 2009; Alzheimer's Association, 2016).

Several in-vivo biomarkers have been used to assess the pathological features of AD.

Cerebrospinal fluid (CSF) biomarkers include low CSF amyloid-beta-42 (indicator of amyloid-

beta accumulation), and increased CSF phosphorylated- and total-tau (indicator of

degeneration/injury of neurons) (McKhann et al., 2011; Jack et al., 2011). Brain imaging, such

as positron-emission tomography (PET) and magnetic resonance imaging (MRI), has also been

used to assess AD pathology. Imaging biomarkers include positive amyloid PET imaging

(indicator of amyloid-beta accumulation), and decreased uptake of 18fluorodeoxyglucose in

certain brain regions on PET and atrophy in specific brain regions on structural MRI (indicator

of degeneration/injury of neurons) (Jack et al., 2011; McKhann et al., 2011).

Biomarkers have been used outside of assessing AD pathology and several studies have

examined their association with other clinical domains. Sleep is an area of interest that has

analyzed biomarkers and demonstrated an association with cognition and cognitive decline; this

is further discussed in Section 1.3 Sleep and Cognition.

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1.1.1.3 Treatment and Management

Despite the major impact AD has on patients, caregivers and society, research to date has

been unsuccessful in uncovering pharmacological treatments that can stop or reverse the

progression of AD. However, there are several medications that may improve symptoms at

various stages of AD. Acetylcholinesterase inhibitors such as donepezil, galantamine and

rivastigmine inhibit the enzyme that breaks down acetylcholine in the brain and are prescribed

for patients with mild to moderate AD (Schneider, 2013). The use of these medications stems

from the cholinergic hypothesis, which proposes that deficits in cholinergic function are related

to the behavioral and cognitive impairments seen in dementia (Bartus et al., 1982; Schneider,

2013). Several studies have investigated the use of donepezil (Rogers et al., 1998; Winblad et al.,

2001), galantamine (Raskind et al., 2000; Tariot et al., 2000) and rivastigmine (Rösler et al.,

1999) in patients with mild to moderate AD and have demonstrated beneficial outcomes on

measures of cognition and functional ability. Administration of donepezil has also demonstrated

positive benefits on cognition and functional outcomes in patients with moderate to severe AD

(Feldman et al., 2001; Howard et al., 2012; Black et al., 2007), demonstrating donepezil’s

usefulness for all stages of AD.

Memantine is another medication used for patients with AD, specifically for patients with

moderate to severe AD. This medication is an N-methyl D-aspartate (NMDA) antagonist that

acts on NMDA receptors (Reisberg et al., 2003). Glutamate stimulates NMDA receptors,

however, overstimulation of these receptors can result in damage to neurons; glutamate’s

interaction with NMDA receptors has been associated with progression of dementia and AD

(Farber, Newcomer and Olney, 1998; Reisberg et al., 2003). Memantine has been shown to

improve cognition and function in patients with moderate to severe AD (Reisberg et al., 2003).

6

Additionally, in patients with moderate to severe AD who were using donepezil, the addition of

memantine (compared to placebo) was significantly associated with improvements in activities

of daily living, behavior and cognition (Tariot et al., 2004).

Treatment using acetylcholinesterase inhibitors can also reduce caregiver burden.

Additionally, treatment with donepezil has been demonstrated to be associated with delays in

time to placement in nursing homes (Geldmacher et al., 2003), which can be very beneficial in

reducing the burden of AD on society and potentially reduce healthcare expenditures.

1.1.2 Vascular Dementia

Vascular Cognitive Impairment (VCI) is a syndrome in which cognitive impairment is

due to cerebrovascular disease; this term encompasses all severity levels from Vascular

Dementia (VaD), discussed in this subsection, to vascular mild cognitive impairment (VaMCI),

discussed in 1.1.3.2 Vascular Mild Cognitive Impairment.

1.1.2.1 Etiology

Second to Alzheimer disease, Vascular Dementia (VaD) is the next most common type of

dementia, and it is estimated that approximately 17% of all dementias are due to VaD (Plassman

et al., 2007). VaD is diagnosed when there is a significant decline in cognition that impairs the

patient’s activities of daily living, which is secondary to cerebrovascular disease determined by

imaging (Gorelick et al., 2011). Some cerebrovascular diseases that can cause VaD include

stroke or cerebral small vessel disease (SVD). Neuroimaging tools such as computed

tomography and MRI have been very useful for assessment of stroke location and/or detection of

7

cerebral SVD, which can provide physicians insights on severity and symptomatology (Gorelick

et al., 2011).

Forms of SVD can include white matter hyperintensities, microbleeds and perivascular

spaces (Wardlaw et al., 2013) and SVD can result from arteriolosclerosis and cerebral amyloid

angiopathy (Pantoni, 2010). In some cases, vascular cognitive impairment may be due to genetic

causes. For example, cerebral autosomal dominant arteriopathy with subcortical infarcts and

leukoencephalopathy (CADASIL) results in white matter lesions in relatively young adults and

can contribute to VaD (Gorelick et al., 2011).

As VaD is of vascular origin, there are several risk factors that can increase an

individual’s risk for stroke and/or vascular disease. Risk factors include hypertension (Sharp et

al., 2011), age (Hébert et al., 2000), diabetes (Hébert et al., 2000) and smoking (Rusanen et al.,

2011). There is also a strong association between obstructive sleep apnea and vascular disease,

including risk of stroke (Yaggi et al., 2005). Furthermore, studies have shown an association

between OSA and the development of dementia and mild cognitive impairment (Yaffe et al.,

2011). The relationship between sleep and cognition will be discussed further in a subsequent

section (1.3 Sleep and Cognition).

1.1.2.2 Treatment and Management

Similar to AD, there is no treatment to stop or reverse the neurodegeneration that results

due to VaD. Deficits in cholinergic function are also seen in VaD (Erkinjuntti, Román and

Gauthier, 2004) and various acetylcholinesterase inhibitors as well as memantine have been

found to be helpful in managing patients with VaD. For example, donepezil was demonstrated to

be effective in improving cognition, daily activities and global function compared to a placebo

8

group in patients with VaD (Black et al., 2003). A study examining galantamine demonstrated

similar results where improvements in cognition were seen, however, activities of daily living

remained unchanged (Auchus et al., 2007). Rivastigmine also improved cognition in patients

with VaD, however, the authors noted that this may have resulted due to coexisting AD (Ballard

et al., 2008). Improvements in cognition were also seen in studies that investigated the use of

memantine in patients with VaD (Wilcock et al., 2002; Orgogozo et al., 2002). Like in AD,

treatment using pharmacological aids has demonstrated benefits in managing the behavioral and

cognitive symptoms seen in VaD.

Treatment of vascular risk factors is another important aspect to managing VaD. Douiri et

al. (2013) demonstrated that in patients who had an ischemic stroke, use of various

antihypertensive, lipid-lowering and antithrombotic medications were associated with a reduced

risk of cognitive impairment; additionally, the combination of these medications was also

associated with a reduced risk of cognitive impairment (Douiri et al., 2013). The American Heart

Association/American Stroke Association also recommended lifestyle interventions such as

smoking cessation and weight control in individuals at risk for vascular cognitive impairment

(Gorelick et al., 2011). Physical activity has been associated with a reduced risk of VaD

(Aarsland et al., 2010) and the American Heart Association/American Stroke Association has

recommended individuals to aim for 30 minutes of moderate intensity exercise daily (Gorelick et

al., 2011). Therefore, management of vascular risk factors is very important, as it may be able to

prevent and/or reduce the risk of vascular disease and subsequent dementia.

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1.1.3 Mild Cognitive Impairment

Mild Cognitive Impairment (MCI) is a syndrome that is characterized by mild

impairment in an individual’s cognition. Clinically, MCI is diagnosed when there is a cognitive

change in one or more cognitive domains, compared to a previous level of cognition, and

functional ability for independent activities remains stable or mildly disrupted (Albert et al.,

2011). MCI differs from dementia in that functional ability is not significantly impaired. Patients

with MCI may present as the amnestic subtype, who have challenges with memory, or as the

non-amnestic subtype, who have challenges with attention, executive function, language or

visuospatial (Petersen, 2004). If a patient has challenges in many domains, they would be

considered to have multi-domain MCI, either of the amnestic type if memory is impaired, or the

non-amnestic type if memory is not impaired (Petersen, 2004).

MCI can result from many causes such as neurodegenerative, which may be suggestive of

AD or Dementia with Lewy Bodies (DLB); vascular, which may be suggestive of VaD;

traumatic brain injury; depression; and/or mixed etiologies (Albert et al., 2011). For the purpose

of this thesis, MCI due to AD and Vascular MCI will be discussed.

1.1.3.1 Mild Cognitive Impairment due to Alzheimer disease

MCI due to AD is a term used for a subset of patients who have MCI that is believed to

be due to underlying AD pathology. The prevalence of MCI due to AD is estimated to increase

with age, from 6.7% (60-64 years) to 25.2% (80-84 years) (Petersen et al., 2018). In patients

with MCI due to AD, it is often seen that episodic memory is impaired during

neuropsychological tests; however, it is important to understand that other cognitive domains

may also be impaired (Albert et al., 2011). These domains include attention, visuospatial,

10

executive function and language, and patients may present with MCI of atypical AD where

visuospatial or language domains are initially impaired (Albert et al., 2011). Genetics and

biomarkers are also examined to determine if MCI is of AD pathology. Genetic mutations in

amyloid precursor protein, presenilin 1 and/or presenilin 2 would be supportive of AD

pathology, as would the presence of the apolipoprotein-e4 gene, which increases the risk of MCI

developing into AD (Albert et al., 2011). Similar to the biomarkers used to assess AD pathology

(Section 1.1.1.1 Etiology), positive biomarkers of amyloid-beta deposition and neuronal injury

would strengthen the likelihood that the presenting MCI is due to AD (Albert et al., 2011).

Additionally, increased CSF total and phosphorylated tau have been associated with an increased

chance of MCI patients converting to AD (Hampel et al., 2008). Biomarkers have their

limitations due to availability and standardization of cut-offs, however, they have the potential to

identify patients at high likelihood of AD pathology and thereby allow for directed management

and treatment of cognitive impairment.

Unlike in AD, research has yet to show cholinesterase inhibitors being effective in

reducing cognitive symptoms in MCI due to AD. Studies of donepezil, galantamine and

rivastigmine were not effective in reducing the development of dementia in patients with MCI

(Petersen et al., 2018). Another avenue to prevent and/or reduce the risk of progression to AD is

the treatment of vascular risk factors. Vascular factors, such as diabetes and hypertension, can

increase an individual’s risk of MCI progressing to AD, and treatment has been demonstrated to

be associated with reducing this risk of progression (Li et al., 2011). Research has also shown

some support for treatment of MCI with non-pharmacological aids. Cognitive interventions and

exercise, such as resistance training, may improve assessments of cognition and physicians are

recommended to advise patients with MCI to exercise weekly because of the positive benefits it

11

may have on cognition and the general benefits it may have on an individual’s health (Petersen et

al., 2018).

1.1.3.2 Vascular Mild Cognitive Impairment

Vascular Mild Cognitive Impairment (VaMCI) is a term used for patients who have MCI

due to cerebrovascular disease, however, the impairment is not severe enough to significantly

impair activities of daily living, which is seen in vascular dementia (Gorelick et al., 2011). The

causes of VaMCI are similar to the causes of VaD. Patients who have a cerebrovascular event,

such as a stroke or transient ischemic attack, or cerebrovascular disease, such as SVD, may

present with cognitive impairment. VaMCI patients do not need to present with impairments in

certain domains to have VaMCI, they can present with any of the MCI subtypes such as single or

multi-domain impairment (Gorelick et al., 2011). However, VaMCI patients often present with

impairments in executive functioning and working memory, with the possibility of impairment in

more domains if the decline continues (Sachdev et al., 2009).

Risk factors for VaMCI are similar to the ones discussed for VaD (Section 1.1.2.1

Etiology) and the treatment and management of VaMCI is accomplished by managing vascular

risk factors to help reduce the chances of a recurrent cerebrovascular event. Some risk factors

include hypertension, diabetes and atrial fibrillation, and treatment may reduce the risk of VCI

(Gorelick et al., 2011). Additionally, patients who present with VaMCI following a stroke may

have improvements in cognition due in part to the recovery process seen in stroke survivors

(Gorelick et al., 2011).

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1.1.4 Other Related Dementias: Dementia with Lewy Bodies and

Parkinson’s Disease Related Dementia

Additional forms of dementia that may impact an individual’s cognitive and functional

status include Dementia with Lewy Bodies (DLB) and Parkinson’s disease dementia (PDD).

Although these neurodegenerative conditions are more commonly associated with non-OSA

sleep disorders such as rapid eye movement sleep behavior disorder (RBD) (McKeith et al.,

2017), patients with these conditions were still of interest for the present thesis and a summary is

provided.

It is estimated that approximately 5% of all cases of dementia are due to DLB (Hogan et

al., 2016). DLB differs from the other discussed dementias in that cognition may fluctuate, and

hallucinations and parkinsonian features may be present (McKeith et al., 2005; McKeith et al.,

2017). Pathology of DLB results from abnormal aggregates of alpha-synuclein, a protein found

in neurons, known as Lewy bodies (Alzheimer's Association, 2016). Lewy bodies may aggregate

in the cortex, which can result in DLB, or they can also aggregate in the substantia nigra, which

can result in Parkinson’s disease (PD) (Alzheimer's Association, 2016). PD may progress to

dementia, known as Parkinson’s disease dementia (PDD), if Lewy bodies accumulate in the

cortex, or may progress to AD if amyloid-beta and tau accumulate (Alzheimer's Association,

2016).

Similar to the other dementias, cholinesterase inhibitors are used to treat DLB. Donepezil

and rivastigmine have been demonstrated to be useful in improving function and cognition in

DLB patients (McKeith et al., 2017). Levodopa may also be used for motor-related symptoms,

however, PD patients often better tolerate this medication as it may increase psychosis in DLB

patients (McKeith et al., 2017). Aside from pharmacological treatments, non-pharmacological

13

treatments, such as cognitive training and exercise, have also shown positive benefits (McKeith

et al., 2017).

1.1.5 Mixed Diseases

In individuals who have dementia or cognitive impairment, it is common that the reduced

cognitive function can be due to mixed pathology, where a patient may have both

neurodegenerative and/or vascular factors that play a role. Mixed dementias may include any

combination of Alzheimer’s pathology, cerebrovascular pathology, and LBD/PD. In the Rush

Memory Aging Project, 141 autopsies were analyzed for dementia pathology and of those with

dementia, over half had mixed pathology, with the most common being AD and VaD (Schneider

et al., 2007). In the interaction between Alzheimer’s and vascular pathology, either pathology

may occur first. AD pathology, which can result in deposition of amyloid in blood vessels,

increases an individual’s risk of stroke and possible VaD; alternatively, cerebrovascular disease

may impact the development of the characteristic plaques and tangles seen in AD (Langa, Foster

and Larson, 2004).

Due to the mixed nature of Alzheimer’s and vascular pathology, this often leads

physicians to treat a range of symptoms with cognitive enhancers as well as treat vascular risk

factors. Patients with mixed dementia in a randomized controlled trial investigating the efficacy

of galantamine demonstrated functional and cognitive benefits (Erkinjuntti et al., 2008). Also in

a randomized controlled trial of rivastigmine in patients with mixed dementia, benefits were seen

on measures of activities of daily living and cognition for patients taking the medication,

compared to placebo (Kumar et al., 2000). Treatment of vascular risk factors, such as

hypertension and hyperlipidemia, is also very important in management, as there is an

14

association with developing cognitive impairment (Langa, Foster and Larson, 2004). Treatment

of hypertension and hyperlipidemia is associated with reduced risk of stroke (Gorelick et al.,

2011) and preventing stroke and further cerebrovascular damage can be vital in reducing the risk

of subsequent dementia. Additional recommendations for vascular risk management include

various lifestyle modifications such as smoking cessation and physical activity (Gorelick et al.,

2011).

1.1.6 Impact of Dementia on Society

The dementias discussed above all have a significant impact on society and burden on the

healthcare system. In the 2018 World Alzheimer Report released by Alzheimer’s Disease

International, it was estimated that the cost of dementia worldwide was approximately one

trillion U.S dollars in 2018 (Alzheimer’s Disease International, 2018). They further estimated

this number to double by 2030 to two trillion U.S dollars, demonstrating the significant financial

cost dementia will continue to have in the future.

Patients with dementia use a variety of healthcare services, which may include hospital

visits, nursing home facilities and healthcare visits at home, and they use these resources more

than non-demented older individuals (Alzheimer's Association, 2016). Patients also rely heavily

on the assistance from caregivers such as formal paid caregivers who assist in those living at

home. Patients with dementia may also rely on informal caregivers such as family or friends who

provide unpaid assistance to those living with dementia and these caregivers are often spouses or

children. It was estimated in 2015 that informal caregivers provided approximately 18 billion

hours of assistance, equal to approximately $220 billion (Alzheimer's Association, 2016).

Although this assistance has helped the financial impact of dementia, there is a substantial

15

burden that can be on the caregivers themselves. The helpful act of caring for a family member

can often be met with a significant amount of stress such as emotional stress of having to care for

a loved one and difficulty completing nursing/medical tasks (Alzheimer's Association, 2016).

Additionally, caregivers may often suffer from depression; one study demonstrated that

caregivers of patients with AD, compared to adults who were not caregivers (i.e. spouse did not

have dementia), had more depressive symptoms and met cutoffs for clinical depression

(Mausbach et al., 2013). Sleep disturbances, which are common in patients with dementia, have

also been shown to be associated with increased burden on caregivers (Gehrman et al., 2018),

demonstrating a potential strategy to reduce burden on caregivers if sleep disturbances are

addressed. Being a caregiver can also impact employment. Due to responsibilities as a caregiver,

54% have stated they had to leave early or go in late to work and 8% retired earlier than planned

(Alzheimer's Association, 2016).

As dementia has a significant burden on society and the healthcare system, research has

examined potential solutions to reduce the risk of dementia. In a review that examined 7 risk

factors associated with AD, they provided estimates of reduction of the disease if risk factors

were managed (Barnes and Yaffe, 2011). The 7 factors included obesity, hypertension, diabetes,

smoking, low education, physical inactivity and depression. They estimated there would be one

million less cases of AD if there was a 10% reduction in all risk factors and three million less

cases if a 25% reduction occurred (Barnes and Yaffe, 2011). This study was limited due to the

assumption that all risk factors were independent of each other; however, the vascular risk

factors assessed often are associated with each other (Barnes and Yaffe, 2011). Norton et al.

(2014) extended this work and accounted for this limitation. They estimated these risk factors

may account for approximately a third of patients with AD and managing these factors could

16

decrease AD prevalence by 8-15% by 2050 (Norton et al., 2014). With this concept, the

FINGER study (Ngandu et al., 2015) assessed the effect of a multidomain intervention on

individuals who did not have dementia. The multidomain approach consisted of monitoring of

vascular risk factors, a nutritional diet, cognitive training and physical exercise. The findings

demonstrated the intervention was beneficial in maintaining or improving cognition (Ngandu et

al., 2015). If strategies such as risk factor management were able to decrease the incidence of

dementia, this would have substantial benefits on extending quality of life and reducing the

impact of dementia on society.

17

1.2 Obstructive Sleep Apnea

1.2.1 Etiology

Obstructive sleep apnea (OSA) is a sleep disorder that is associated with the repetitive

complete or partial obstruction of the upper airway during sleep (Bradley and Floras, 2009).

Some of the many ways OSA may occur is due to a collapsible or crowded airway, or poor

pharyngeal dilator muscle tone (Eckert et al., 2013). Features of OSA include snoring, daytime

sleepiness as well as observed pauses in breathing, choking and/or gasping during sleep (Epstein

et al., 2009).

In OSA, apneas and hypopneas occur during a patient’s sleep that can cause hypoxia and

may result in awakenings during the night. An apnea is the cessation of breathing for 10 seconds

or longer, whereas a hypopnea is the reduction in breathing by 30% or more for 10 seconds or

longer, and some hypopneas are followed by oxygen desaturation (Iber et al., 2007). The apnea-

hypopnea index (AHI) is the number of apneas and hypopneas associated with desaturation that

occur per hour of sleep (Iber et al., 2007). The AHI is an index for sleep apnea severity with

higher values indicating more severe sleep apnea. In addition to apneas and hypopneas, oxygen

desaturations can also occur during a patient’s sleep and they often accompany apneic and

hypopneic events. An oxygen desaturation event occurs when the patient’s oxygen level drops by

a certain percentage (i.e. 3 or 4%) for a pre-specified duration; the number of oxygen

desaturation events that occur per hour yields the oxygen desaturation index (ODI) (Iber et al.,

2007). The ODI is another index that is used to assess the severity of sleep apnea and, similar to

the AHI, higher values indicated more severe sleep apnea.

18

The definition for sleep apnea varies across studies; definitions that have been used

include an AHI≥15, use of the oxygen desaturation index (ODI≥12) (McEvoy et al., 2016),

and/or a combination of using both AHI and oxygen desaturation (Patel et al., 2018). Another

approach for the definition of OSA is an AHI≥5, plus symptoms of OSA such as loud snoring,

daytime sleepiness, or observed pauses in breathing, choking and/or gasping during sleep

(Epstein et al., 2009). However, a general consensus is present for the severity levels of OSA:

normal (AHI<5), mild (AHI 5-14.9), moderate (AHI 15-29.9), and severe (AHI≥30) (Young,

Skatrud and Peppard, 2004).

There are several factors that increase a patient’s risk for OSA. Age is considered to be a

risk factor for OSA (Jordan, McSharry and Malhotra, 2014) and studies have shown an increase

in prevalence of OSA with age (Bixler et al., 1998; Peppard et al., 2013). Excess weight is also a

known risk factor; an analysis of the Wisconsin Sleep Cohort Study found that in patients who

did not have OSA, an increase in weight by 10% was associated with an increased risk of OSA

(Peppard et al., 2000a). In addition, males have a greater risk for OSA compared to females

(Redline et al., 1994). Suggested mechanisms for this relationship included anatomical features

that cause airway collapse, which are more common in males (Jordan, McSharry and Malhotra,

2014). When examining risk factors in females, it was found that hormonal changes due to

menopause play a key role. Two studies found that compared to premenopausal women,

postmenopausal women had an increased risk of having OSA (Bixler et al., 2001; Young et al.,

2003). In addition, craniofacial features in individuals with different racial and ethnic profiles

have been associated with risk of OSA; a maxilla that is shorter and a cranial base that is shorter

were associated with OSA in Asian populations and an enlarged tongue has been associated with

OSA in African American populations (Dudley and Patel, 2016). Smoking and alcohol have also

19

been associated as risk factors for OSA (Jordan, McSharry and Malhotra, 2014; Young, Skatrud

and Peppard, 2004).

Of note, a study that assessed different phenotypic traits (anatomic vs. nonanatomic) of

OSA found patients vary in the pathophysiologic causes of OSA (Eckert et al., 2013). Available

OSA therapies may target different OSA phenotypes, which demonstrates the importance of

patient-specific treatment strategies, as certain treatments may not be beneficial for all OSA

cases.

1.2.2 Prevalence of Sleep Apnea

Severalstudieshaveexaminedtheprevalence of sleep apnea in the adult population.

Mild to severe sleep apnea (AHI≥5) has been estimated to be prevalent in 17-47% of males

(Young et al., 1993; Tufik et al., 2010; Bixler et al., 1998) and 9-31% of females (Young et al.,

1993; Tufik et al., 2010). Table 1 (Bixler et al., 1998; Bixler et al., 2001; Young et al., 1993;

Tufik et al., 2010) provides a summary of large observational studies (N≥500) that assessed the

prevalence of OSA using in-laboratory polysomnography (iPSG), the gold standard for

diagnosing sleep disorders. In the Wisconsin Sleep Cohort Study, comparing prevalence rates

from 1988-1994 to 2007-2010 demonstrated an increase in OSA prevalence in all age cohorts for

both men and women over the last two decades.(Peppard et al., 2013) These studies provide a

good estimate of OSA prevalence; however, as they reported prevalence in only two countries,

they provide little information on global prevalence.

In addition to population-based studies, the prevalence of sleep apnea has also been

estimated in several diseased populations such as cardiovascular disease, hypertension, stroke

and dementia. Studies that investigated various cardiovascular diseases found that OSA is

20

prevalent in 49-53% of patients with heart failure (Javaheri, 2006; Ferrier et al., 2005) and 66%

of patients with myocardial infarction (Lee et al., 2009). In patients with hypertension,

prevalence estimates of OSA go as high as 85-90% (Pratt-Ubunama et al., 2007; Lloberes et al.,

2010). Prevalence is also high for patients who have suffered a stroke or transient ischemic

attack (TIA), estimating 72% of patients have an AHI>5 (Johnson and Johnson, 2010). Finally,

in patients with dementia, it has been estimated that 49-63% of patients have moderate to severe

OSA, however, small sample sizes limits these results (Rose et al., 2011; Gehrman et al., 2003;

Jorge et al., 2019). These studies demonstrate the higher prevalence of OSA in various diseased

states compared to the general population.

Table 1-1: Prevalence of OSA in large observational studies (N≥500) that used in-laboratory polysomnography

Study Location Age N Prevalence Reported

Male Female Wisconsin Sleep Cohort Study, 1993 (Young et al., 1993)

U.S.A. 30-60y 602 AHI≥5 = 24% AHI≥15 = 9%

AHI≥5 = 9% AHI≥15 = 4%

Southern Pennsylvania Cohort, 1998(Bixler et al., 1998) and 2001(Bixler et al., 2001)

U.S.A. ≥20y 1741 AHI≥5 = 17% AHI≥15 = 7%

AHI≥5 = not reported AHI≥15 = 2%

Sao Paulo Epidemiologic Sleep Study, 2010 (Tufik et al., 2010)

Brazil 20-80y 1042 AHI≥5 = 47% AHI≥15 = 25%

AHI≥5 = 31% AHI≥15 = 10%

AHI: Apnea-Hypopnea Index; U.S.A: United States of America

1.2.3 Questionnaires to Screen for Sleep Apnea

Sleep questionnaires have been investigated for their ability to predict sleep apnea as they

are easy to administer and cost-effective. The following sections discuss common questionnaires

used in research studies and their ability to screen for sleep apnea.

21

1.2.3.1 Berlin Questionnaire

The Berlin Questionnaire (BQ) is a ten-question assessment to assess for risk of sleep

apnea (Netzer et al., 1999). It is comprised of three categories, which determine whether a

patient is at low or high risk for sleep apnea. Category one assesses the presence, frequency and

volume of snoring, and whether apneic events have been witnessed. Category one is positive if a

patient scores ≥ 2 points from their answers. Category two assesses fatigue and tiredness during

the day and while driving. Category two is positive if a patient scores ≥ 2 points from their

responses. Finally, category three assesses high blood pressure and/or if the patient’s BMI is

≥30kg/m2, and is positive if answered “yes” to either of the questions. Patients are considered to

be low risk for sleep apnea if none or only one category is positive and patients are considered to

be at high risk if two or more categories are positive (Netzer et al., 1999).

1.2.3.2 STOP and STOP-Bang Questionnaire

The STOP and STOP-Bang Questionnaires (SBQ) assess for risk of OSA. The STOP

portion has four questions relating to snoring, daytime tiredness/fatigue/sleepiness, witnessed

apneas and high blood pressure (Chung et al., 2008a). The STOP-Bang questionnaire is an

extension of the STOP questionnaire, which adds an additional four questions (Chung, Abdullah

and Liao, 2016). The “Bang” portion relates to high BMI (≥35kg/m2), age (≥50), large neck size

(≥17 inches for males and ≥16 inches for females) and male gender. One point is added for each

question answered “yes” for a maximum of 8 points. A patient is considered to be at low risk for

OSA if their total score is 0-2, intermediate risk if their score is 3-4, and high risk if they have ≥5

points. Additionally, a patient will be considered to be at high risk for OSA if they have

22

answered “yes” to ≥2 of the STOP questions and “yes” to one or more of elevated BMI, large

neck size, or male gender (Chung, Abdullah and Liao, 2016).

1.2.3.3 Epworth Sleepiness Scale

The Epworth Sleepiness Scale is a questionnaire that assesses daytime sleepiness (Johns,

1991). The patient answers how likely they are to doze off or fall asleep in eight different

scenarios. The patient ranks each scenario from 0-3 where 0 is “would never doze or fall asleep”

and 3 is “a high chance of dozing off or falling asleep”. The total sum from the eight scenarios is

calculated with 24 being the maximum score. A patient is considered to have normal daytime

sleepiness if they score between 0-10. A score between 11-24 is considered to be excessive

daytime sleepiness, where higher scores indicate more excessive daytime sleepiness (Johns,

1991).

1.2.3.4 Accuracy of Questionnaires in Screening for Sleep Apnea

Several studies have examined the use of questionnaires to screen for sleep apnea in

different populations. A recent meta-analysis demonstrated that the SBQ and BQ have the

highest sensitivities in predicting OSA as well as the lowest specificities, while the ESS had the

highest specificity and lowest sensitivity (Chiu et al., 2017). These questionnaires have been

examined in different patient populations and have demonstrated to be useful in predicting OSA

in preoperative patients (Chung et al., 2008b; Chung, Abdullah and Liao, 2016) and patients who

have suffered a stroke or transient ischemic attack (TIA) (Katzan et al., 2016; Boulos et al.,

2016). However, in some populations such as in patients with resistant hypertension (Margallo et

23

al., 2014) and type 2 diabetes (Westlake et al., 2016), sleep questionnaires did not perform well

in predicting OSA. Additionally, in patients diagnosed with AD, sleep questionnaires such as the

BQ and SBQ were not useful in predicting OSA (Jorge et al., 2019). Therefore, when choosing

sleep questionnaires to screen for OSA, it is important to know their reliability and validity in the

population of interest prior to using them as tools to rule in/out patient referrals for iPSG.

1.2.4 Diagnosing Sleep Apnea

1.2.4.1 In-laboratory Polysomnography

The gold standard for diagnosing sleep disorders is level 1 in-laboratory

polysomnography (iPSG), which is completed at a sleep laboratory or sleep clinic. Several

channels are evaluated, including electroencephalogram, electrooculogram, electrocardiogram,

respiratory effort, oximetry and airflow (Table 1-2) (Epstein et al., 2009; Ferber et al., 1994).

Level 1 studies are monitored by a sleep technologist, while level 2 studies assess the same

parameters but may be unattended (i.e. without a sleep technologist present) and can be

completed in a home setting (El Shayeb et al., 2014). The sleep study and scoring is completed

in accordance with the standards of the American Academy of Sleep Medicine (AASM) (Iber et

al., 2007).

1.2.4.2 Home Sleep Apnea Tests

An alternative to an in-laboratory iPSG is the use of home sleep apnea testing (HSAT),

which would typically be classified as a level 3 or 4 sleep study (Table 1-2) (Ferber et al., 1994).

Level 3 sleep studies can be completed at home, are unattended, and assess four or more

24

parameters. The channels tested include respiratory effort, oxygen saturation, heart rate and

airflow (Blackman et al., 2010). Level 4 sleep studies assess only one or two parameters, one of

which is oxygen saturation (Blackman et al., 2010). The few parameters collected from level 4

sleep studies limit its diagnostic ability; for this reason, level 3 HSATs are preferred as they

assess a greater number of parameters (Blackman et al., 2010). Additionally, many level 3

HSATs have been validated against iPSG (El Shayeb et al., 2014).

Table 1-2: Classification of Sleep Study Tests

Study Level Parameters Measured Setting Attended? (Yes/No)

I Minimum of 7 channels: - EEG, EOG, EMG, ECG, Airflow, Respiratory Effort, Oxygen Saturation

Lab Yes

II Minimum of 7 channels: - (Same as above) Home or lab Yes or No

III Minimum of 4 channels: - Heart Rate/ECG, Oxygen Saturation, Respiratory Movement, Airflow)

Home or lab No

IV Minimum of 1 channel: - E.g. Oxygen Saturation, Airflow, etc. Home or lab No

EEG: Electroencephalogram; EOG: Electrooculogram; EMG: Electromyogram; ECG: Electrocardiogram. Ferber, R., Millman, R., Coppola, M., Fleetham, J., Murray, C. F., Iber, C., McCall, V., Nino-Murcia, G., Pressman, M. and Sanders, M., Portable recording in the assessment of obstructive sleep apnea. ASDA standards of practice, Sleep, 1994, 17 (4):378-92, by permission of Oxford University Press. See Appendix C for Copyright License

1.2.5 Treatment and Management of Obstructive Sleep Apnea

OSA is an important condition that if left untreated can lead to many serious health

complications. Research has demonstrated that untreated OSA is associated with deficits in

memory, executive function, attention and vigilance (Aloia et al., 2004). In addition to cognitive

aspects, untreated OSA is associated with vascular issues such as hypertension (Peppard et al.,

25

2000b), stroke and death (Yaggi et al., 2005). Fortunately, several treatment options are available

for people with OSA.

1.2.5.1 Continuous Positive Airway Pressure

Continuous positive airway pressure (CPAP) is the gold standard for treating moderate to

severe OSA and research has shown beneficial outcomes to patients who use CPAP. This

treatment strategy aims to prevent airway collapse during sleep by delivering pressurized air into

the patient’s airway (Aloia et al., 2004). In an analysis of the SAVE study, CPAP was effective

in reducing snoring and improved daytime sleepiness and health-related quality of life (McEvoy

et al., 2016). Furthermore, the SAVE study and two additional studies (Barbe et al., 2012; Peker

et al., 2016) demonstrated through post-hoc analyses that patients who were CPAP adherent (i.e.

≥4 hours/night) had a reduced risk of vascular events. Additionally, treatment of OSA with

CPAP has been shown to be associated with improvements in cognitive functioning such as

attention/vigilance, verbal and visual memory and executive dysfunction (Bucks, Olaithe and

Eastwood, 2013). Furthermore in patients with AD and OSA, it has been demonstrated that

treatment with CPAP showed some improvement in cognitive functioning (Ancoli-Israel et al.,

2008). This important finding demonstrates the modifiable role OSA may play in cognition.

Unfortunately, adherence to CPAP is generally low. Results from the SAVE study

demonstrated that only 42% of patients where adherent to CPAP (McEvoy et al., 2016). Several

additional studies that have involved CPAP have also reported low adherence to CPAP, ranging

from approximately 30% to over 80% of patients being non-adherent (Weaver and Grunstein,

2008). Explanations for low adherence have included patient discomfort from the mask and lack

of subjective improvement in symptoms (Mello-Fujita et al., 2015; Weaver and Grunstein,

26

2008). Claustrophobia was also associated with non-adherence to CPAP therapy (Chasens et al.,

2005). Despite the positive effects CPAP has on patients with OSA, low adherence remains an

issue for successful treatment.

1.2.5.2 Positional Devices and Oral Appliances

There are additional strategies for treatment of OSA if treatment with CPAP is

unsuccessful. Positional therapy is one option for patients who decline CPAP. Of patients who

have OSA, over 50% of the cases are position-dependent (Oksenberg et al., 1997; Oksenberg et

al., 2009; Richard et al., 2006; Joosten et al., 2012), meaning that OSA is worse when the patient

sleeps in the supine position. Positional therapy aims to prevent patients from sleeping in the

supine position, and a number of positional devices have been shown to be effective in reducing

OSA severity (Permut et al., 2010; Zuberi, Rekab and Nguyen, 2004; Jackson et al., 2015).

Oral appliances are another form of therapy that can be used for patients with OSA. In

2015, the American Academy of Sleep Medicine and the American Academy of Dental Sleep

Medicine released an updated report on the use of oral appliances in OSA (Ramar et al., 2015).

They recommended the use of oral appliances in patients who have OSA and are non-adherent

with CPAP, and also in patients without OSA who snore. Studies have shown beneficial effects

with the use of oral appliances in reducing OSA severity (Mehta et al., 2001; Gotsopoulos, Kelly

and Cistulli, 2004). Several studies have also compared oral appliances to CPAP and have found

both treatments improved AHI (Aarab et al., 2011; Engleman et al., 2002; Randerath et al.,

2002; Gagnadoux et al., 2009; Phillips et al., 2013; Hoekema et al., 2007), subjective sleepiness

(Phillips et al., 2013; Gagnadoux et al., 2009), as well as improvements on cognitive tests

(Gagnadoux et al., 2009) and driving simulators (Phillips et al., 2013; Hoekema et al., 2007).

27

Both treatments showed similar results, however, CPAP remained the more effective treatment

option in reducing OSA severity, assessed by the AHI (Engleman et al., 2002; Gagnadoux et al.,

2009; Phillips et al., 2013; Randerath et al., 2002).

1.2.5.3 Surgery and Weight Loss

Another alternative to CPAP is surgical intervention, which is often used as a last resort

when treating OSA. Surgery aims to reconstruct the upper airway to help remedy the disorder

(Epstein et al., 2009). There are several approaches for surgery, such as nasal, oral and

hypo/oral/naso-pharyngeal procedures (Epstein et al., 2009). Patients who request surgery are

first assessed for eligibility, which consists of identification of surgical targets, medical

comorbidities and several other factors. Some surgical interventions have potential benefits:

tracheostomy may resolve OSA and maxillomandibular advancement demonstrates

improvements similar to CPAP (Epstein et al., 2009). Surgery may also benefit various

additional health outcomes such as quality of life, cardiovascular risk and mortality (Epstein et

al., 2009). However, the operation itself comes with its risks and different surgical procedures

may not cure a patient’s OSA (Epstein et al., 2009). More recently, hypoglossal nerve

stimulation has been demonstrated to be an effective treatment strategy for OSA (Strollo et al.,

2014).

Weight loss reduction is another useful and more convenient method in treating OSA. An

analysis of the Wisconsin Sleep Cohort Study demonstrated an association between OSA and

weight (Peppard et al., 2000a). Weight gain was associated with an increased AHI and risk of

OSA; contrasting this, the AHI decreased when there was a 10% weight loss (Peppard et al.,

2000a). A meta-analysis demonstrated that use of lifestyle interventions, such as exercise and

28

diet, to lose weight had positive results in reducing OSA severity (Mitchell et al., 2014).

Although this study only included patients who were overweight/obese, making it difficult to

generalize to non-overweight/obese patients, a healthy diet and exercise can have beneficial

outcomes on several aspects of a patient’s health.

29

1.3 Sleep and Cognition

1.3.1 Relationship between Sleep Quality and Cognition

As people age, there is an increase in complaints regarding their sleep and research has

demonstrated an association between difficulties with sleep and poor health (Neikrug and

Ancoli-Israel, 2010). A close relationship between sleep and cognition has also been

demonstrated in research. In the Study of Osteoporotic Fractures that assessed women’s sleep via

actigraphy, reduced sleep efficiency, greater wakefulness after sleep onset (WASO) and greater

sleep onset latency were associated with an increased risk of cognitive impairment (Blackwell et

al., 2006). In the Osteoporotic Fractures in Men Study Group, similar associations were seen:

reduced sleep efficiency, greater WASO as well as self-reported poor sleep were associated with

a decline in cognition (Blackwell et al., 2014). Sleep is important in the consolidation and

strengthening of memories, and disrupted and fragmented sleep can lead to impaired memory

(Landmann et al., 2014). Sleep fragmentation has also been demonstrated to be associated with

incident AD, with more severe sleep fragmentation associated with an increased risk of AD (Lim

et al., 2013).

Amyloid-beta and tau, the two primary pathological findings of AD pathology, are

modulated by sleep. Soluble amyloid-beta levels fluctuate throughout the sleep-wake cycle,

where during the day the levels increase and during sleep the levels decrease (Ju et al., 2017).

Slow wave sleep is associated with amyloid-beta clearance, however, disruption of this stage

during iPSG was associated with increased amyloid-beta in cognitively normal individuals (Ju et

al., 2017). Also, increased levels of CSF and PET-detected amyloid-beta were similarly seen in

several studies that investigated the relationship between sleep and amyloid in individuals with

30

normal cognition. In studies using self-reports of sleep quality (Branger et al., 2016; Spira et al.,

2013) and actigraphy measured sleep quality (assessed by sleep efficiency) (Ju et al., 2013), poor

sleep quality was associated with increased amyloid-beta burden. Similarly in another study,

authors showed the association of poor self-reported sleep quality with amyloid-beta burden, and

also with higher total and phosphorylated tau (Sprecher et al., 2017). The association with tau

was also demonstrated elsewhere where poorer actigraphy derived sleep efficiency was

associated with increased levels of tau (Ju et al., 2017). With respect to sleep quantity, there are

mixed results on the association with amyloid deposition (Spira et al., 2013; Ju et al., 2013).

Patients with poor quality sleep may experience excessive daytime sleepiness (Pack et

al., 2006), and a relationship between excessive daytime sleepiness and amyloid-beta has also

been observed. In individuals without dementia, the presence of excessive daytime sleepiness

was associated with increased amyloid-beta on follow-up PET scans (Carvalho et al., 2018).

Similarly, another study of individuals without dementia demonstrated an association between

baseline excessive daytime sleepiness and presence of amyloid-beta 15 years later; however, not

having baseline PET scans limited the ability to determine a causal relationship (Spira et al.,

2018).

This relationship between disrupted sleep and impaired cognition is also seen in patients

with MCI. In a study that compared MCI patients to health controls, they found slow-save sleep

that was disrupted was associated with increased amyloid-beta in MCI patients (Sanchez-

Espinosa, Atienza and Cantero, 2014). Also, reduced rapid-eye movement sleep was associated

with thinning of the cortex in MCI patients (Sanchez-Espinosa, Atienza and Cantero, 2014). As

sleep disruption is associated with cognitive dysfunction and AD pathology, a bidirectional

relationship between sleep and AD has been suggested (Ju, Lucey and Holtzman, 2014). This

31

relationship has been suggested as reduced sleep quality has been associated with increased AD

pathology, subsequently leading to cognitive impairment and AD, and that increased amyloid-

beta in the brain impairs sleep (Ju, Lucey and Holtzman, 2014).

With disrupted sleep being associated with cognitive dysfunction in cognitively normal

individuals and in patients with MCI and AD, it stresses the importance of identifying and

treating disrupted sleep. Additionally, iPSG and self-reported sleep assessments have

demonstrated disrupted sleep in patients with MCI, however there is disagreement in the

measures with regards to the severity of the disruptions (Hita-Yañez, Atienza and Cantero,

2013). This disagreement between objective and subjective assessments was also seen in patients

with AD (Most et al., 2012). This further highlights the importance of using objective measures

to assess sleep as correct diagnosis and treatment may provide benefits to cognitive function.

1.3.2 Sleep Apnea and Cognitive Impairment

Sleep apnea has been estimated to be prevalent in 49-63% of patients with dementia

(Rose et al., 2011; Gehrman et al., 2003; Jorge et al., 2019), indicating a higher prevalence

compared to the general population. Patients with untreated OSA show deficits in executive

function, vigilance, attention, visuospatial abilities and memory (Bucks, Olaithe and Eastwood,

2013). Additionally, sleep apnea has been shown to be associated with a younger age of MCI or

AD onset (Osorio et al., 2015). A meta-analysis that investigated the relationship between sleep

apnea and cognitive impairment found that patients had a 26% increased risk of developing

cognitive impairment if they had sleep apnea (Leng et al., 2017). A similar meta-analysis found

that sleep problems, such as OSA, poor sleep quality and insomnia, were associated with an

increased risk of AD, cognitive impairment as well as preclinical AD (Bubu et al., 2016).

32

Moreover, they found that OSA was associated with the highest risk of developing incident AD

and/or cognitive impairment (Bubu et al., 2016).

The partial or complete obstruction of the airway that occurs in OSA results in hypoxia

and sleep fragmentation (Gagnon et al., 2014). Longitudinal studies have demonstrated that

hypoxia (Yaffe et al., 2011) and sleep fragmentation (Lim et al., 2013) increase the risk of

cognitive impairment and dementia. It is hypothesized that they contribute to cognitive

dysfunction via various mechanisms such as increased sympathetic activation, endothelial

dysfunction and systemic inflammation (Gagnon et al., 2014). Additionally, impairment in

glymphatic and vascular drainage of amyloid may result from sleep fragmentation (Berezuk et

al., 2015) and this clearance system may be important as it is thought to play a role in the

pathology of AD (Tarasoff-Conway et al., 2015).

In-vivo biomarkers related to dementia have also been associated with untreated OSA.

Liguori et al. (2017) examined CSF biomarkers in patients who presented with subjective

cognitive impairment. Untreated OSA was associated with lower concentrations of CSF

amyloid-beta-42 and higher concentrations of CSF total and phosphorylated tau, as well as lower

scores on cognitive measures, compared to patients who were using CPAP therapy for OSA or

patients who did not have OSA (Liguori et al., 2017). Importantly, patients with OSA using

CPAP therapy did not have the AD biomarker changes that were seen in untreated OSA.

Similarly, Canessa et al. (2011) found that untreated OSA was linked with cognitive impairment,

which was associated with gray matter reductions in the frontal gyrus and hippocampus.

Additionally, they demonstrated that treatment with CPAP was associated with improvements on

cognitive measures as well as increased volume of gray matter in the frontal gyrus and

33

hippocampus (Canessa et al., 2011). These two studies demonstrated the importance of CPAP

therapy and the modifiable role OSA may play in dementia.

The role OSA may play in cognitive impairment was further demonstrated in the

Alzheimer’s Disease Neuroimaging Initiative, where sleep apnea was associated with an earlier

age of MCI or AD onset (Osorio et al., 2015). An additional finding was that use of CPAP was

associated with a later age of MCI onset (Osorio et al., 2015). This is an important finding as is it

further demonstrates the role sleep apnea may play in cognitive decline and that proper treatment

and management may delay progression of cognitive impairment.

Cerebrovascular events can also play a role in dementia pathology and OSA is associated

with vascular disease and is an independent risk factor for stroke (Yaggi et al., 2005). In an

analysis of patients with a recent stroke, OSA was associated with lower functional status and

lower scores on cognitive tests (Aaronson et al., 2015). Specifically, the cognitive tests patients

with OSA performed poorer on included assessments of executive functioning, psychomotor

ability, attention and visuoperception.

1.3.3 Treatment of Sleep Apnea in Patients with Cognitive

Impairment

There are several treatment options for patients with sleep apnea and many were

discussed in a previous section (1.2.5 Treatment and Management of Obstructive Sleep Apnea).

CPAP is the gold standard for treating OSA and several randomized studies have investigated the

use of CPAP in patients with cognitive impairment and dementia.

34

In patients with cognitive impairment and dementia, treatment of sleep apnea has been

associated with beneficial outcomes. Chong et al. (2006) randomized AD patients with sleep

apnea to standard CPAP or sham-CPAP followed by standard care. Daytime sleepiness, assessed

by the Epworth Sleepiness Scale, significantly improved after three and six weeks of standard

CPAP use. Additionally, there was no significant change in subjective sleepiness after use of

sham-CPAP, however, following the change to standard CPAP, there was a significant

difference (Chong et al., 2006).

Treatment with CPAP has also been associated with beneficial outcomes with respect to

cognitive functioning. In a randomized study of patients with OSA and AD, there was a

significant improvement in cognitive functioning with CPAP therapy (Ancoli-Israel et al., 2008).

Although this study was limited as it was underpowered, post-hoc analyses showed

improvements in executive functioning and memory with CPAP use. An exploratory study on

the subset of the AD patients who continued to use CPAP therapy demonstrated slower decline

of cognitive function compared to those who discontinued CPAP therapy (Cooke et al., 2009b).

This was also seen in another study in AD patients that demonstrated long-term CPAP was

associated with slower decline (Troussière et al., 2014). Although both these studies had small

sample sizes, they provided insight into the possible role management of OSA might play in

cognitive and functional ability in cognitively impaired individuals. Cooke et al. (2009) also

demonstrated the benefits of CPAP therapy with respect to sleep stages, where CPAP use

resulted in patients spending less time in stage 1 sleep, an increased amount of time in stage 3

sleep, and fewer arousals and awakenings after sleep onset (Cooke et al., 2009a). This further

demonstrated that benefit of CPAP in restoring sleep architecture and the benefits it can have on

sleep.

35

The benefit of CPAP treatment on cognition was also seen in a randomized controlled

trial in patients who had a stroke (Aaronson et al., 2016). Following randomization to CPAP

therapy or to standard rehabilitation stroke treatment, patients using CPAP therapy demonstrated

significant improvements in cognition, specifically in areas of executive functioning and

attention.

Treating sleep apnea may also be beneficial to the caregiver. Gehrman et al. (2018)

demonstrated that 60% of AD patients present with symptoms of sleep issues, and caregiver

burden was associated with snoring, daytime sleepiness, and nighttime awakenings and

wandering (Gehrman et al., 2018). Also seen in AD patients, sleep disturbances were associated

with aggressiveness and caregiver burden (Moran et al., 2005). Fortunately, Cooke et al. (2009)

demonstrated that continued CPAP use by patients also benefitted caregivers, as caregivers

reported better sleep (Cooke et al., 2009b). Providing treatment for patients with sleep apnea

may not only benefit the patient’s cognition, sleep and daytime functioning, but may also lessen

one aspect of the burden on caregivers.

36

1.4 Neuropsychological Tests to Assess Cognitive

Impairment

Neuropsychological tests are useful tests for physicians to use to assess the cognitive

functioning of their patients. There are several useful cognitive assessments that are currently

available that are able to determine if a patient has cognitive impairment, as well as the severity

of their impairment. For the present thesis, the various cognitive assessments that were used to

measure cognition are discussed in detail below.

1.4.1 Mini-Mental State Examination

The Mini-Mental State Examination (MMSE) is a well-documented and valid cognitive

test that takes approximately ten minutes to administer and includes eleven questions (Folstein,

Folstein and McHugh, 1975). The MMSE includes two sections, one section involves the patient

providing responses orally and the second section involves the patient providing oral and written

responses. In the first section, questions are related to orientation of the patient, memory

registration and recall, and attention. The second section asks questions related to object

identification, completing oral and written instructions, and copying a figure. The patient can

obtain a maximum of 21 points in the first section and 9 points in the second section, for a total

maximum score of 30 points. Patients who score higher on the MMSE have higher cognitive

functioning. This simple and quick cognitive assessment can objectively measure a patient’s

cognitive ability, however, it is important to note that it cannot be used as a substitute for a full

clinical assessment (Folstein, Folstein and McHugh, 1975).

37

For the present thesis, the MMSE was used to assess baseline cognitive functioning of

potential study participants. Patients that had a MMSE ≥18 were approached for participation in

the study.

1.4.2 Montreal Cognitive Assessment Tool

The Montreal Cognitive Assessment (MoCA) is another widely used cognitive test

(Nasreddine et al., 2005). Patients with mild complaints of impairment may often score high on

the MMSE and in the range of normal cognition; the MoCA was developed to address this

difficulty. The MOCA consists of several sections that assess different cognitive domains.

Patients are asked questions that are related to executive function and visuospatial abilities,

memory registration and recall, attention and calculation, language, abstraction and orientation.

Scores from each question are summed and patients are given an additional point if they have not

obtained more than 12 years of education. The total maximum score that can be obtained is 30

and if a patient obtains a score of 26 or greater, they are considered to be normal. With respect to

the sensitivity of the MoCA, it was superior to the MMSE in identifying patients with MCI (90%

compared to 18%) and patients with mild AD (100% compared to 78%) (Nasreddine et al.,

2005). In patients with OSA, the MoCA was shown to have better discriminative ability in

diagnosing MCI compared to the MMSE (Gagnon et al., 2018).

1.4.3 Toronto Cognitive Assessment

The Toronto Cognitive Assessment (TorCA) is a comprehensive cognitive test to detect

patients with MCI (Freedman et al., 2018). The TorCA was adapted from the Behavioural

38

Neurology Assessment (Darvesh et al., 2005), which was an assessment to detect patients with

dementia. The TorCA includes 27 tests, which assess several cognitive domains. Scores in each

of the domains are totaled and the sum of all domains gives a total score. In each domain, patient

scores can be compared to normative data to determine if they are within normal limits for their

age category or are considered to be in the borderline or impaired region. The total score can

similarly be compared to normative data. The TorCA was validated to detect amnestic MCI and

had high sensitivity and specificity. Additionally, it was demonstrated to be comparable to the

MoCA and other cognitive assessments. The TorCA is a comprehensive test and can be

completed in a short time frame of approximately 35 minutes, which is considerably shorter than

full neuropsychological batteries (Freedman et al., 2018).

1.4.4 Psychomotor Vigilance Task

The Psychomotor Vigilance Task (PVT) is an automated device that is used to assess

vigilant attention (Lim and Dinges, 2008). The PVT device is held while the patient is in a seated

position and the test is 10 minutes in length. Patients are instructed to focus their attention to a

small screen on the device where a red time counter will appear. Patients are instructed to press a

button as soon as they see the red counter appear on the screen, and are told to push the button as

quickly as possible to stop the counter at a lower number, which indicates a quicker reaction

time. After pushing the button, the counter stops to show the participant the time for that trial,

and then disappears, and then the patient waits for the next trial. The time between trials is at

random intervals and patients are instructed of this and are told to not guess and to only push the

button when the counter begins. If they push the button before the counter has started, a false

start will be displayed and recorded; patients are told to try to avoid this.

39

The PVT assesses several parameters, some of which include lapses (i.e. reaction time >

500ms), false starts, mean and median reaction times, and reciprocal reaction times (assessment

of time on task effect). The PVT was used in the present thesis as it has shown to be a useful tool

for assessing attention in sleep-deprived patients (Lim and Dinges, 2008). Additionally, use of

CPAP therapy for OSA was associated with improvements on the PVT (Kribbs et al., 1993). The

PVT metrics that were assessed in the present thesis, as recommended (Basner and Dinges,

2011), included lapses (i.e. reaction time > 500ms), mean reciprocal reaction time (RRT) and

mean fastest reciprocal reaction time (FRRT) (Lim and Dinges, 2008).

40

1.5 Research Aims and Hypothesis

The aim of this thesis was to determine if a home sleep apnea test is a clinically

meaningful screening test for patients with cognitive impairment. The patient population

assessed included patients with Alzheimer disease (AD), Vascular Dementia (VaD), Mild

Cognitive Impairment due to AD (MCI), Vascular Mild Cognitive Impairment (VaMCI),

Parkinson’s Disease Related Dementia (PDD), Dementia with Lewy Bodies (DLB) and/or Mixed

disease.

1.5.1 Primary Objectives

The primary objective of this thesis was to determine the feasibility of obtaining

analyzable data from HSAT in outpatients with cognitive impairment. This method was

considered a feasible technique for the routine screening of OSA in clinic patients if ≥80% of the

patients who completed HSAT obtained ≥4 hours of analyzable data. Additionally, we

determined the practicality of recruiting eligible patients with cognitive impairment to complete

home sleep apnea testing. For this objective, HSAT was considered a practical technique for the

routine screening of OSA in cognitively impaired clinic patients if ≥50% of the patients

approached obtained ≥4 hours of analyzable data.

The primary objectives were assessed in Chapter 2: Feasibility of Home Sleep Apnea

Test in a Cognitively Impaired Population.

41

1.5.2 Secondary Objectives

Secondary objectives of this thesis were to (i) determine predictors of obtaining

analyzable sleep recordings from HSAT; (ii) assess the prevalence of OSA in a cognitively

impaired clinic population; and (iii) determine predictors of an OSA diagnosis by correlating

subjective sleep questionnaire data and objective clinical data with objectively measured OSA.

Secondary objective (i) was assessed in Chapter 2: Feasibility of Home Sleep Apnea Test

in a Cognitively Impaired Population. Secondary objectives (ii) and (iii) were assessed in

Chapter 3: Prevalence of Obstructive Sleep Apnea in a Patient Population with Cognitive

Impairment.

42

Chapter 2: Feasibility of Home Sleep Apnea Test in a Cognitively Impaired Population

43

2.1 Abstract Background: Obstructive sleep apnea (OSA), which causes pauses in breathing during sleep,

increases the risk of developing dementia. In-laboratory polysomnography (PSG) is the gold

standard tool to diagnose OSA, but few patients are screened by PSG due to patient refusal to

spend a night in a sleep laboratory, high costs, and lengthy wait times. Home sleep apnea testing

(HSAT) may be a more accessible alternative, as it is simple to use, conveniently administered in

a patient’s own home and validated against PSG.

Objective: Our objective was to assess if HSAT is a clinically feasible approach to screen for

OSA in a cognitively impaired patient population.

Methods: Patients with cognitive impairment due to neurodegenerative and/or vascular

etiologies were enrolled and completed various cognitive, sleep, and mood assessments and

questionnaires. Patients also completed OSA screening using a HSAT. HSAT was considered a

feasible technique if ≥80% of the study population obtained ≥4 hours of analyzable data. HSAT

was considered a practical technique if 50% of the patients approached obtained ≥4 hours of

analyzable data.

Results: One hundred and seventeen eligible patients were approached for participation, eighty-

one completed baseline assessment and seventy-six patients completed baseline testing and

attempted HSAT. Patients who attempted HSAT had a mean age (±SD) of 72.03 (11.17), 44.7%

identified as male and the median Montreal Cognitive Assessment score was 22. Seventy

participants (92.1%) obtained ≥4 hours of analyzable data using the HSAT and 59.8% of eligible

patients approached obtained ≥4 hours of analyzable data. Completing assessments in the

morning was independently associated with obtaining ≥4 hours of analyzable data (p=0.030).

44

Conclusion: Our study demonstrated HSAT was a feasible and practical technique in a

cognitively impaired patient population. As OSA is a modifiable risk factor for patients with

dementia, HSAT has the potential to lead to expedited treatment for OSA, which may potentially

improve health related outcomes such as cognition.

45

2.2 Introduction

Cognitive impairment and dementia are associated with substantial healthcare

expenditures and present a significant burden on healthcare providers and society (Takizawa et

al., 2015). In a report by Alzheimer Disease International, it was estimated that in 2018, 50

million people were living with some form of dementia worldwide and it is believed that this

number will rise to 82 billion by 2030 (Alzheimer’s Disease International, 2018). Dementia can

result from various neurodegenerative and vascular causes, however, there are many other

conditions that can also play a role. In particular, obstructive sleep apnea (OSA) is now

appreciated to be closely linked to dementia.

OSA is a common sleep disorder where the upper airway collapses during sleep (Bradley

and Floras, 2009). Repetitive closure of the upper airway can result in multiple awakenings

during the night, impacting the quality of sleep. OSA is prevalent in patients with cognitive

impairment, with 60% of patients with Mild Cognitive Impairment (MCI) and 70% with

Vascular Cognitive Impairment (VCI) reporting symptoms suggestive of OSA (Guarnieri et al.,

2012). Additionally, research has shown the presence of OSA leads to an increased risk of

developing dementia (Yaffe et al., 2011; Chang et al., 2013). The presence of OSA is also

associated with a decline in executive function (Ju et al., 2012), attention (Martin et al., 2015)

and global cognition (Blackwell et al., 2015). Fortunately, treatment with continuous positive

airway pressure (CPAP) has been shown to improve cognition in patients without (Bucks,

Olaithe and Eastwood, 2013) and with dementia (Ancoli-Israel et al., 2008). This illustrates the

potential role OSA may have in cognitive impairment and how it may be a modifiable risk

factor.

46

In-laboratory polysomnography (iPSG) remains the gold-standard for diagnosing OSA

(Epstein et al., 2009); however, due to refusal to spend a night in a sleep laboratory, high costs

and lengthy wait times, limited numbers of patients are screened for OSA using iPSG. Use of a

home sleep apnea test (HSAT) may be a suitable alternative as it is less expensive, more

accessible and is validated against iPSG (El Shayeb et al., 2014).

Few studies have examined the use of HSAT in patients with cognitive impairment

(Table 2-1) and only one study to-date has assessed the feasibility of using this approach in a

research setting; the feasibility of broad screening in a clinic population has yet to be examined.

Our primary objective was to determine the feasibility of obtaining analyzable data from

HSAT in clinic outpatients with cognitive impairment who complete HSAT. Our secondary

objectives were to determine predictors of obtaining analyzable HSAT recordings, as well as

determine the practicality of recruiting patients with cognitive impairment from neurology

clinics to complete HSAT.

Table 2-1: Prior studies that have used HSAT in a cognitively impaired population

Author (y) Population N Device Recording Condition Purpose

Gehrman (2003) (Gehrman et al., 2003)

AD 38 Respitrace-Medilog portable system Unknown Relationship of sleep apnea

and agitation in AD

Ancoli-Israel (2008) (Ancoli-Israel et al., 2008)

AD with OSA 52 In-home PSG - Embla Unknown Relationship of CPAP

therapy and cognition

Rose (2011) (Rose et al., 2011)

Dementia 59 Grass Portable PSG Attended Examine sleep patterns in dementia

Maestri (2015) (Maestri et al., 2015)

MCI and AD 33 In-home PSG Unknown Evaluate sleep patterns in dementia

Vaughan (2016) (Vaughan et al., 2016)

MCI and no cognitive

impairment 91 ApneaLink Unattended Feasibility of using HSAT

AD: Alzheimer’s disease; CPAP: Continuous Positive Airway Pressure; HSAT: Home Sleep Apnea Test; MCI: Mild Cognitive Impairment; OSA: Obstructive Sleep Apnea; PSG: Polysomnography.

47

2.3 Methods

2.3.1 Ethics

The study was approved by the local Research Ethics Board and was conducted in

accordance with the Declaration of Helsinki. Prior to being enrolled in this study, all participants,

or their substitute decision maker, provided written informed consent (Appendix B).

2.3.2 Study Population

This was an observational, single-centre prospective study. Patients were consecutively

recruited from academic cognitive neurology clinics at Sunnybrook Health Sciences Centre

during a 17-month recruitment period (November 2017 to April 2019). Inclusion criteria

included 1) cognitive impairment primarily attributable to an underlying neurodegenerative and

or/vascular etiology consistent with the corresponding diagnostic guidelines (i.e. AD, MCI: NIA-

AA (McKhann et al., 2011); VaD, VaMCI: NINDS-AIREN (Román et al., 1993); PDD, DLB:

DLB Consortium (McKeith et al., 2005; McKeith et al., 2017) and/or mixed disease); 2) a Mini

Mental State Examination score ≥18; 3) Outpatients being managed through an ambulatory care

clinic at Sunnybrook Health Sciences Centre; 4) Have the competency to provide informed

consent and complete the HSAT, or the availability of a caregiver to assist in these tasks. Patients

who had any of the following were excluded: 1) the presence of a contraindication for the use of

the HSAT used in the study (e.g. moderate to severe pulmonary disease or congestive heart

failure); 2) Any medical device that would interfere with the placement of the HSAT; 3)

Significant physical impairment or language barrier that would restrict the ability to use the

HSAT or complete study assessments; 4) Current use of CPAP for previously diagnosed OSA.

48

Eligible patients were identified by the patient’s cognitive neurologist or by chart review

performed by the study coordinator and/or cognitive neurologist following a patient’s clinical

visit. Patients were referred to the study whether or not they endorsed subjective sleep

complaints.

2.3.3 Study Procedure and Assessments

Eligible patients were educated on the importance of sleep disorders in the context of

cognitive impairment and provided informed consent. Baseline questionnaires and assessments

related to functional status, cognition, sleep, and mood and behavior were completed.

Assessments related to functional status include the Alzheimer's Disease Functional Assessment

and Change Scale (ADFACS). Cognitive assessments included Psychomotor Vigilance Task

(PVT) (Lim and Dinges, 2008), Montreal Cognitive Assessment (MoCA) (Nasreddine et al.,

2005), Mini Mental State Examination (MMSE) (Folstein, Folstein and McHugh, 1975), and the

Toronto Cognitive Assessment (TorCA) (Freedman et al., 2018). Sleep related questionnaires

included the Epworth Sleepiness Scale (ESS) (Johns, 1991), Berlin Questionnaire (BQ) (Netzer

et al., 1999), and STOP-Bang questionnaire (SBQ) (Chung, Abdullah and Liao, 2016). For mood

and behavior assessment, the Geriatric Depression Scale (GDS) (Yesavage et al., 1982) and the

Neuropsychiatric Inventory Questionnaire (NPI) (Kaufer et al., 2000) were completed.

Following completion of baseline assessments, patients were taught how to use and apply the

HSAT and were provided HSAT instructional sheets and a phone number to call if additional

support was needed (Appendix A-II).

49

2.3.4 Home Sleep Apnea Test

Patients underwent unattended OSA testing using the ApneaLink Air (Appendix A-I).

This level III HSAT is Health Canada and FDA-approved, and records chest/respiratory effort by

a chest effort sensor, nasal flow/pressure by a nasal cannula, and oxygen saturation by a pulse

oximeter (Figure 2-4). The HSAT has been validated against iPSG for the detection of OSA

(Erman et al., 2007; Ng et al., 2009; Nigro et al., 2013). Participants were educated by the

research assistant on how to apply and operate the device and were provided with an

instructional sheet (Appendix A-II). The night recordings were completed at the participant’s

home and were unattended. Following completion of the night recording, a patient satisfaction

survey was completed, which allowed patients to provide feedback on their experiences with the

HSAT (Appendix A-III). Patients also wore actigraphs for up to 7 days and the actigraph used

was the Philips Respironics Actiwatch 2 which has been validated for sleep-wake disturbances

such as sleep fragmentation (Sadeh, 2011). Variables derived from the actigraph that were

assessed included wake after sleep onset (WASO, minutes), sleep efficiency (%), total sleep time

(minutes) and onset latency (minutes).

HSAT recordings were automatically analyzed using the ApneaLink software

application, as well as manually scored by a sleep physician according to the standards outlined

in the American Academy of Sleep Medicine (AASM) (Iber et al., 2007). As previously

described (Colelli et al., 2018), apneas were scored as a ≥90% reduction in airflow for ≥10

seconds and hypopneas were scored as ≥30% reduction in airflow for ≥10 seconds with ≥4%

oxygen desaturation. The sum of apneas and hypopneas per hour were calculated to generate an

apnea-hypopnea index (AHI).

50

2.3.5 Outcome Measures

The primary objective of this study was to determine the feasibility of obtaining

analyzable data from HSAT in clinic outpatients with cognitive impairment who complete

HSAT. This method was considered a feasible technique for the screening of OSA in a

cognitively impaired clinic population if ≥80% of the patients who completed HSAT obtained ≥4

hours of analyzable data in three or more HSAT channels. This cut-off of ≥4 hours was selected

as it has been recommended by clinical practice guidelines (Kapur et al., 2017) and previously

used in an HSAT feasibility study (Boulos et al., 2017). Secondary objectives were to (i)

determine predictors of obtaining analyzable sleep recordings from the HSAT, as well as to (ii)

determine the practicality of recruiting patients with cognitive impairment from neurology

clinics to complete HSAT; for this secondary objective, HSAT was considered a practical

technique for the screening of OSA if ≥50% of the patients approached obtained ≥4 hours of

analyzable data.

The definition of OSA used for our study was mild OSA with significant oxygen

desaturation (AHI≥5 and oxygen desaturation ≤88%) or moderate to severe OSA (AHI≥15), as

previously discussed (Patel et al., 2018; Colelli et al., 2018). Patients who were found to have

OSA on HSAT were seen by a sleep specialist to discuss the health effects of OSA on cognition

and vascular disease and the potential benefits of CPAP therapy in treating OSA. Patients who

were offered and agreed to use CPAP were referred to a sleep company where they were given a

self-adjusting positive airway pressure (auto-PAP) for two weeks then provided CPAP at a fixed

pressure (95th percentile of auto-PAP pressure). The study investigators have no financial

relationship with the CPAP companies involved in this study.

51

2.3.6 Statistical Analyses

Continuous measures were summarized by means and standard deviations (SD),

categorical measures by frequency counts and percentages, and ordinal variables by median and

interquartile range (IQR). Comparison between groups that obtained analyzable data to those

who obtained non-analyzable data were made using t-tests for normally distributed continuous

variables, Mann-Whitney U test for non-normally distributed continuous variables and ordinal

variables, and chi-square tests for categorical variables.

Multivariate logistic regression was then completed to determine factors that predicted an

analyzable HSAT recording. Variables that were assessed included age, gender, a variable that

assessed cognition and a variable that assessed functional ability. Variables from the univariate

analysis that had a p-value<0.05 were also included in the model to account for potential

confounders. The beta coefficient ± standard error (S.E), odds ratio (OR), 95% confidence

interval (C.I) and p-value were reported for each covariate. Multi-collinearity of the model was

assessed by variance inflating factor (VIF), where values greater than 10 suggested variables that

were highly collinear and partial residual plots were assessed to determine if non-linearity of

continuous variables was a concern.

For the primary objective of our study, we determined a sample size of 62 participants or

more would be required, which was calculated assuming 80% of the study population obtained

analyzable data, with a 95% confidence level and precision of 10%. A p-value <0.05 was

considered to be statistically significant. The statistics software “R”, version 3.4.1 (R Foundation

for Statistical Computing, Vienna Austria) and the Statistical Package for the Social Science

(version 24.0) was used to perform all analyses.

52

2.4 Results

One hundred and seventeen patients from the cognitive neurology clinics that were

screened were eligible and approached for participation in the present feasibility study. Of these

patients, thirty-six declined to participate and the remaining eighty-one attended baseline testing.

Five patients returned the HSAT without using it. The remaining analysis includes the seventy-

six patients who attempted to complete the HSAT (Figure 2-1).

Figure 2-1: Patient flow in the feasibility study

117 Eligible & Approached for Participation

81 consented & underwent HSAT administration

70 patients with analyzable data ≥ 4 hours

76completedHSAT

- 5 patients declined to use HSAT

- 36 declined participation

6 patients with non-analyzable data <4 hours - 4 patients with <4 hours - 2 patients with inadequate data (i.e. <2 hours)

53

For our primary objective (i.e. to determine the feasibility of obtaining analyzable data

from HSAT in clinic outpatients with cognitive impairment who complete HSAT), 92% (70 out

of 76) of patients who completed sleep apnea testing using the HSAT obtained analyzable data

(defined as ≥4 hours of HSAT data). Additionally, for our secondary objective (i.e. to determine

the practicality of recruiting patients with cognitive impairment from neurology clinics to

complete HSAT), 60% (70 out of 117) of patients who were approached for participation in this

study obtained analyzable data.

Figure 2-2 demonstrates the distribution of analyzable data (i.e. evaluating time, hours)

patients obtained during the HSAT recording. In these patients, 43% obtained between 7 to 9

hours of evaluating time and 71% obtained over 6 hours of evaluating time. Two patients had

evaluating time less than one hour and they were considered to be inadequate recording; they

declined to repeat HSAT.

Figure 2-2: Amount of analyzable HSAT data obtained during the recording

03691215182124

0-1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 10-11 11-12

ProportionofParticpants(%

)

EvaluationTime(hours)

54

Patients who completed home sleep apnea testing had a mean (± SD) age of 72.03 (11.17)

and 44.7% identified as male (Table 2-2). Actigraphy derived total sleep time identified patients

used the device for a mean (± SD) 471.80 (105.25) minutes (i.e. 7 hours and 52 minutes). The

study population consisted of patients who were diagnosed with AD (N=12; 15.79%), MCI due

to AD (N=36; 47.4%), Vascular MCI (N=15; 19.7%), mixed Dementia (N=8; 10.5%) and mixed

MCI (N=5; 6.6%).

Univariate analysis compared patients who had analyzable data (≥4 hours) to those who

had non-analyzable data (<4 hours). Variables that were significantly associated with having

analyzable HSAT data included completing assessments in the morning (as opposed to the

afternoon), a higher ADFACS score for basic activities of daily living, a lower NPI total score

and NPI caregiver distress score, and lower PVT derived lapses (reaction time >500ms).

55

Table 2-2: Patient demographics

Completed Sleep Testing (N=76)

Analyzable Data (N=70)

Non-Analyzable Data (N=6) p-value

Age (y), mean ± SD 72.03 ± 11.17 71.07 ± 11.37 75.83 ± 8.28 0.440 Male, N (%) 34 (44.7) 30 (42.9) 4 (66.7) 0.399 BMI, mean ± SD 25.66 ± 5.41 25.69 ± 5.55 25.19 ± 3.47 1.00 Years of Education, mean ± SD 15.74 ± 1.37 15.91 ± 3.41 13.83 ± 4.54 0.168 Dementia Diagnosis (vs. MCI), N (%) 20 (26.32) 17 (24.29) 3 (50) 0.183 Morning Assessment, N (%) 60 (78.95) 59 (84.29) 1 (16.67) 0.001* Time between Recruitment and HSAT test (days), mean ± SD 8.54 ± 14.38 8.99 ± 14.78 2.4 ± 3.36 0.119

Actigraphy Measures, mean ± SD Wake after sleep onset (minutes) 55.09 ± 29.93 53.03 ± 28.46 77.38 ± 41.38 0.121 Sleep efficiency (%) 80.43 ± 10.97 80.31 ± 11.23 81.75 ± 8.23 0.804 Total Sleep Time (minutes) 471.80 ± 105.25 467.12 ± 105.46 522.5 ± 96.81 0.196 Onset Latency (minutes) 40.99 ± 44.87 42.22 ± 46.40 27.71 ± 19.84 0.591

ADFACS (N=72), mean ± SD Total Score 89.65 ± 13.12 90.48 ± 12.44 80.50 ± 18.00 0.102 IADL Score 85.17 ± 17.93 86.08 ± 17.49 75.17 ± 21.34 0.186 ADL Score 95.55 ± 9.49 96.21 ± 8.72 87.83 ± 14.96 0.014*

NPI Total Score (N=51), mean ± SD 10.06 ± 10.74 8.40 ± 8.51 22.50 ± 17.49 0.024* NPI Caregiver Score (N=51), mean ± SD 6.08 ± 6.51 5.22 ± 5.86 12.50 ± 8.04 0.022* TorCA Overall Score (N=71), mean ± SD 246.27 ± 45.64 248.58 ± 44.72 207.50 ± 49.64 0.058 MMSE, median (IQR) 26 (5) 27 (5.5) 22.50 (9.25) 0.076 MoCA, median (IQR) 22 (6.25) 22.5 (6) 18.50 (10.75) 0.161 PVT Measures, mean ± SD

Mean RRT 3.09 ± 0.63 3.12 ± 0.63 2.73 ± 0.61 0.138 Mean F RRT 4.17 ± 0.68 4.20 ± 0.66 3.93 ± 0.83 0.476 Lapses (reaction time >500ms) 12.33 ± 16.75 11.33 ± 16.01 24 ± 22.20 0.032*

Geriatric Depression Scale, median (IQR) 6.50 (8.75) 6.50 (9) 8.50 (13.75) 0.622 STOPBANG, N (%)

Low Risk 24 (17.1) 23 (32.9) 1 (16.7) 0.658 Intermediate Risk 37 (31.6) 35 (50) 2 (33.3) 0.675 High Risk 15 (19.7) 12 (17.1) 3 (50) 0.087

Berlin Questionnaire - High Risk (opposed to Low Risk), N (%) 25 (32.9) 22 (31.4) 3 (50) 0.388

Epworth Sleepiness Scale ≥10, N (%) 13 (17.1) 13 (18.6) 0 (0) 0.582 Hypertension, N (%) 44 (57.9) 39 (55.7) 5 (83.3) 0.392 Hyperlipidemia, N (%) 42 (55.3) 38 (54.3) 4 (66.7) 0.686 Diabetes, N (%) 10 (13.2) 10 (14.3) 0 (0) 1.00 History of smoking, N (%) 33 (43.4) 29 (41.4) 4 (66.7) 0.394 ADFACS: Alzheimer's Disease Functional Assessment and Change Scale; ADL: Basic Activities of Daily Living; BMI: Body Mass Index; FRRT: Fast Reciprocal Reaction Time; HSAT: Home Sleep Apnea Test; IADL: Instrumental Activities of Daily Living; MCI: Mild Cognitive Impairment; MMSE; MMSE: Mini Mental Status Examination; MoCA: Montreal Cognitive Assessment; NPI: Neuropsychiatric Inventory Questionnaire; OSA: Obstructive Sleep Apnea; PVT: Psychomotor Vigilance Task; RRT: Reciprocal Reaction Time; SD: Standard Deviation; TorCA: Toronto Cognitive Assessment; *Indicates p<0.05

56

To investigate if the different etiologies for cognitive impairment had an impact on the

ability to successfully obtain analyzable using HSAT, we assessed rates of obtaining analyzable

data across the different diagnoses (Table 2-3). Using Fisher’s exact test, there were no

significant differences in the proportion of patients who obtained analyzable data to those who

did not obtain analyzable data for the various diagnoses assessed.

Table 2-3: Obtaining Analyzable HSAT Data by Patient Diagnosis

Obtained Analyzable

Data

Non-Analyzable Data

Diagnosis Alzheimer’s disease (N=12) 11 (92%) 1 (8%) MCI due to AD (N=36) 34 (94%) 2 (6%) Vascular MCI (N=15) 14 (93%) 1 (7%) Mixed Dementia (N=8) 6 (75%) 2 (25%) Mixed MCI (N=5) 5 (100%) 0 (0%)

AD: Alzheimer’s disease; MCI: Mild Cognitive Impairment

A multivariate logistic regression analysis to assess for predictors of analyzable HSAT

data was performed which included age, gender, a variable that assessed cognition, a variable

that assessed functional ability, and any variables from the univariate analysis that had a p-value

≤0.05 (Table 2-4). For the variable that assessed cognition, we chose to include the TorCA total

score, as we desired to have a global measure of cognition for the analysis. For the variable that

assessed functional ability, we chose to include the ADFACS total score, as it provides an

assessment of both instrumental and basic activities of daily living. The variables that were

ultimately included were age, gender, TorCA total score, ADFACS total score, morning

assessment and PVT derived lapses. Although the NPI total score and NPI caregiver distress

score had a p-value ≤0.05, they were not included due to missing data. The logistic regression

model identified one variable that was significantly associated with obtaining an analyzable

57

HSAT recording: having a morning assessment was a significant independent predictor of having

analyzable HSAT data (OR: 26.72, 95% C.I: 1.37, 519.73, p=0.030).

Model diagnostics were completed for the multivariate logistic regression model. Non-

linearity of continuous variables was not a concern after assessment of partial residual plots and

multicollinearity was not a concern as no covariates had a VIF greater than 10.

Table 2-4: Logistic Regression for Analyzable HSAT Data

Coef ± S.E. Odds Ratio 95% CI of the Odds Ratio p-value

Age 0.04 ± 0.09 1.68 0.16, 17.95 0.670

Male Sex -0.65 ± 1.38 0.52 0.03, 7.87 0.641

TorCA Total Score 0.01 ± 0.02 1.47 0.25, 9.63 0.667

ADFACS Total Score 0.08 ± 0.02 4.33 0.67, 28.08 0.124

Morning Assessment

(opposed to Afternoon) 3.92 ± 1.76 50.17 1.59, 1582.70 0.026*

PVT derived Lapses -0.02 ± 0.04 0.71 0.19, 2.61 0.606

ADFACS: Alzheimer's Disease Functional Assessment and Change Scale; Coef: β-Coefficient; CI: Confidence Interval; OSA: Obstructive Sleep Apnea; PVT: Psychomotor Vigilance Task; S.E.: Standard Error; TorCA: Toronto Cognitive Assessment *Indicates p<0.05

After completion of the HSAT, patients had the option to complete a satisfactory survey

with regards to their experience with the HSAT (N=65, Figure 2-3a-b, Appendix A-III). Of the

respondents, 55.38% stated they did not require assistance to assemble the device, 58.46% stated

it was “easy” to “very easy” to attach the device on their body, 41.45% stated they were

“relatively comfortable” to “comfortable” with using the device compared to a normal night’s

sleep and 52.31% stated they were “aware” to “very aware” of the device during their sleep. For

overall experience using the HSAT, 60.94% of the patients stated their experience was “good” to

“excellent”.

58

Figure 2-3a: HSAT Patient Satisfaction Survey. Each chart demonstrates the proportion of answers patients provided on the HSAT patient satisfaction survey.

45%

55%

Did you need any assistance to assemble the device? (N=65)

Yes

No

3%

18%

20%42%

17%

How easy was it to attach the recorder and effort sensor to your body? (N=65)

Very Difficult

Somewhat Difficult

Neutral

Easy

Very Easy

18%

82%

Did you use tape and/or adhesive pads to keep the nasal cannula securely in place? (N=65)

Yes

No

59

Figure 2-3b: HSAT Patient Satisfaction Survey. Each chart demonstrates the proportion of answers patients provided on the HSAT patient satisfaction survey.

11%

15%

32%

25%

17%

How comfortable were you during the night recording wearing the HSAT compared to a normal night’s sleep?

(N=65)

Extremely uncomfortable

Somewhat uncomfortable

Manageable

Relatively comfortable

Completely comfortable

23%

29%9%

20%

19%

During the night recording how aware were you of the HSAT? (N=65)

Very Aware

Somewhat aware

Neutral

Fairly unaware

Completely unaware

6%

13%

20%

42%

19%

How would you rate your overall experience using the HSAT? (N=64)

Very poor

Poor

Neutral

Good

Excellent

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2.5 Discussion

Our results demonstrated that use of home sleep apnea testing was feasible in a clinic

population of patients with cognitive impairment. We found that 92% of patients who

underwent home sleep apnea testing were able to obtain ≥4 hours of analyzable data. This was

greater than a cutoff of 80%, which has been used in prior literature to assess the feasibility of

home sleep apnea testing in other clinical populations (Boulos et al., 2017). Similarly, we

demonstrated that use of home sleep apnea testing was a practical method, and we were able to

demonstrate that 60% of patients approached for participation in our study obtained analyzable

data with use of home sleep apnea testing.

Only one study to date has assessed use of HSAT in a research setting involving patients

with cognitive impairment, specifically MCI (Vaughan et al., 2016); they demonstrated poor

feasibility of HSAT in individuals with and without cognitive impairment (25% and 54%

completed ≥4 hours of HSAT, respectively). Our study demonstrated superior feasibility of

HSAT (92%), which may be due to the method of HSAT instruction. In Vaughan et al. (2016),

the HSAT and instructions were mailed to participants and support via phone call was provided

if necessary. In the present study, HSAT instructional sheets where provided on the day of the

baseline assessments and patients where shown how to assemble and apply the HSAT in-person;

in addition, they were provided with a phone number to call if they required assistance

(Appendix A-II).

The practicality of recruiting eligible patients with cognitive impairment to complete

HSAT was also demonstrated in the present study and was comparable to previous studies that

used in-home PSG (48-55% of patients who were approached successfully completed in-home

PSG) (Ancoli-Israel et al., 2008; Rose et al., 2011). Also, in the present study, most patients

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obtained over 6 hours of evaluating time with the HSAT and just under half used it for 7 to 9

hours. Although the HSAT used in this study is not able to correctly assess total sleep time,

actigraphy-derived total sleep time demonstrated that, on average, patients slept for over 7 hours.

This suggests patients were still able to sleep for the recommended duration for adults (i.e. 7 to 9

hours) and older adults (i.e. 7 to 8 hours) (Hirshkowitz et al., 2015). The HSAT also

demonstrated high patient satisfaction, as most patients reported it to be easy to assemble and

use, and rated HSAT as being as comfortable as a normal night’s sleep. Although many patients

reported they were aware of the device during their sleep, the overall experience with the HSAT

received positive feedback.

Our study also demonstrated that a morning assessment was independently associated

with obtaining ≥4 hours of analyzable data on the HSAT. This association may be because after

completion of baseline assessments, patients were shown how to use and apply the HSAT. With

demonstration of HSAT assembly being completed earlier in the day, patients may have been

more alert and attentive when instructions were provided. Ad-hoc analyses demonstrated that

assessments of cognitive measures, such as the MoCA, MMSE, TorCA and PVT, did not

significantly differ with the time of day when they were completed.

Another important finding is with respect to cognitive functioning and diagnosis. For

assessments of cognition such as the MoCA, MMSE and TorCA, our analyses demonstrated that

patients who had analyzable data had higher scores compared to those who did not obtain

analyzable data, however, these results were not statistically significantly different. Patients in

the present study had a range of cognitive levels, from high functioning patients with MCI to low

functioning patients with dementia, and results demonstrated most patients were able to use and

successfully obtain analyzable HSAT data.

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Lengthy wait times to complete iPSG often limit timely sleep assessment for patients. In

a study that assessed wait times for sleep apnea care in the same province where the present

study was completed (Ontario, Canada), they determined patients had to wait approximately 4.9

months to complete a sleep study after being referred (Rotenberg et al., 2010). In the present

study, patients completed HSAT on average 9 days after the date of recruitment. While the two

studies cannot be compared directly, this demonstrates the potential HSAT has in providing

timely sleep assessments. Also, in this population of cognitive impairment, research has

demonstrated sleep questionnaires, such as the STOP-Bang and Berlin Questionnaires, are not

reliable tools to screen for OSA (Jorge et al., 2019). This may possibly be due to patients being

cognitively unaware of their sleep issues or misunderstanding of the questions. Alternatively,

OSA may present differently in this population and it may impact the operating characteristics of

these questionnaires. Disagreement between objective and subjective assessments was seen in

one study that included patients with AD (Most et al., 2012). Assistance by caregivers to

complete these questionnaires may be beneficial for future studies. Of note, these sleep

questionnaires have been reliable in other patient populations such as perioperative patients

(Chung, Abdullah and Liao, 2016; Chung et al., 2008b) and patients who have had a stroke

(Boulos et al., 2016; Boulos et al., 2019).

The present study had its limitations. Patients with significant physical impairment or a

language barrier that restricted their ability to complete study assessments or use the HSAT and

patients with severe cognitive impairment (i.e. MMSE score of <18) were not included, which

may limit generalizability of feasibility of HSAT to all patients with cognitive impairment. For

the patients who were approached, our study was limited in determining predictors of accepting

to participate in the study, as data could not be collected on these patients without informed

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consent. Unfortunately, this is a limitation of all REB approved studies that do not allow for

collection of demographics from patients who do not provide informed consent. Obtaining data

on these patients would have been useful, as it would have provided important information on

the practicality of recruiting patients to complete HSAT and reasons for declining to join the

study.

2.6 Conclusion

In conclusion, in a population of cognitively impaired clinic patients, HSAT was

demonstrated to be a feasible and practical method to detect OSA, and patients’ overall

experience with HSAT was positive. As OSA may be a modifiable risk factor for patients with

cognitive impairment, HSAT has the potential to lead to expedited assessment and treatment for

OSA, which may potentially improve health related outcomes such as cognition. Randomized

controlled trials that compare iPSG (current standard of care) to HSAT in a cognitively impaired

population are needed to determine if HSAT is comparable and/or superior to iPSG with respect

to time to sleep assessment and rates of OSA diagnosis.

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Chapter 3: Prevalence of Obstructive Sleep Apnea in a Patient Population with Cognitive Impairment

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3.1 Abstract Background: OSA is associated with an increased risk of developing cognitive impairment.

OSA is prevalent in the general population and the prevalence increases in patients who have

dementia. However, the prevalence of OSA has not been established in AD and other related

conditions such as MCI, VaMCI, PDD, DLB and mixed dementias.

Objective: The objective of this study was to assess the prevalence of OSA in patients with

various forms of cognitive impairment, such as AD, MCI, VaMCI, PDD, DLB and mixed

dementias, using objective measures of OSA.

Methods: We enrolled patients with cognitive impairment primarily attributable to an

underlying neurodegenerative and/or vascular etiology. Patients completed various sleep,

cognitive, and mood questionnaires and assessments. Patients also used HSAT to assess for

OSA. OSA was defined as an AHI≥15 or an AHI≥5 with significant oxygen desaturation of

≤88%.

Results: Seventy patients completed and had an analyzable HSAT recording (i.e. ≥4 hours of

analyzable HSAT data). Patients had a mean age (±SD) of 71.70 (±11.37) years, 42.9%

identified as male and the median Montreal Cognitive Assessment score was 22.50. OSA was

prevalent in 51.4% of the study population and was most prevalent in patients diagnosed with

Alzheimer’s disease (AD) (81.8%, p=0.028). Logistic regression demonstrated OSA was

significantly associated with higher wake after sleep onset (OR: 4.15, p=0.017) and lower MoCA

score (OR: 0.27, p=0.023).

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Conclusion: Our study demonstrated that OSA is highly prevalent in patients with cognitive

impairment and is associated with higher sleep fragmentation and lower cognition. Future

research should examine OSA prevalence in larger cohorts of patients with various forms of

cognitive impairment and assess predictors of OSA in those specific diagnoses.

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3.2 Introduction

Obstructive sleep apnea (OSA) is a common sleep disorder in which repeated pauses in

breathing during sleep result in hypoxia and frequent patient awakenings (Gagnon et al., 2014).

Research has demonstrated a close link between sleep and cognition. A meta-analysis

demonstrated that patients with sleep problems are at a higher risk of developing AD and

cognitive impairment compared to patients with no sleep problems (Bubu et al., 2016). Possible

mechanisms for this association include endothelial dysfunction and systemic inflammation

(Gagnon et al., 2014). OSA can also result in sleep fragmentation, which can impair sleep-

dependent memory consolidation processes (Landmann et al., 2014). Impairment of glymphatic

and vascular drainage of amyloid can also result due to sleep fragmentation (Berezuk et al.,

2015; Xie et al., 2013) and the impaired drainage of amyloid is thought to play a role in the

progression of AD (Tarasoff-Conway et al., 2015).

Treatment of OSA with continuous positive airway pressure (CPAP) has been associated

with positive health outcomes with regards to cognition as research has shown improvements in

global cognitive functioning (Bucks, Olaithe and Eastwood, 2013). In patients with AD, CPAP

therapy has also demonstrated improvements in cognition (Ancoli-Israel et al., 2008) and may

slow decline of cognition (Cooke et al., 2009b; Troussière et al., 2014).

Previous studies (Guarnieri et al., 2012; Pistacchi et al., 2014) have investigated the

prevalence of OSA in various types of dementia and cognitive impairment such as Alzheimer

disease (AD), Mild Cognitive Impairment (MCI), Vascular dementia (VaD), Dementia with

Lewy Bodies (DLB) and Parkinson’s Disease Dementia (PDD). These studies found sleep

disturbances were prevalent in patients with dementia; however, subjective questionnaires were

used to determine sleep disturbances rather than objective measures, limiting their conclusions.

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In studies that used iPSG and a home sleep apnea test (HSAT), study populations consisted of

patients with AD and general unclassified dementia and suggested a prevalence of 49-63% (Rose

et al., 2011; Gehrman et al., 2003; Jorge et al., 2019).

In the present study, a broad range of cognitive impairments were examined objectively

using HSAT to assess prevalence of OSA. Accordingly, the primary objective of our study was

to determine the prevalence of OSA in patients with various forms of cognitive impairment due

to various underlying neurodegenerative and vascular causes. Secondary objectives included

determining predictors of OSA in this patient population.

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3.3 Methods

3.3.1 Ethics

This study was completed in accordance with the Declaration of Helsinki and the local

Research Ethics Board approved the study. All participants, or their assisting decision maker,

provided written informed consent prior to enrollment (Appendix B).

3.3.2 Study Population

Patients were recruited to this observational study from academic cognitive neurology

clinics at a single-centre. The recruitment period was for approximately 17-months (November

2017 to April 2019). Patients were identified by their cognitive neurologist or by review of the

patient’s chart completed by the study research coordinator and/or cognitive neurologist

following a clinical visit. Patients with and without subjective sleep complaints were referred to

the study. Patients were eligible for recruitment if all of the following was present: i) cognitive

impairment primarily attributable to an underlying neurodegenerative and/or vascular etiology

per the diagnostic guidelines (i.e. AD, MCI: NIA-AA (McKhann et al., 2011); VaD, VaMCI:

NINDS-AIREN (Román et al., 1993); PDD, DLB: DLB Consortium (McKeith et al., 2005;

McKeith et al., 2017) and/or mixed disease); ii) a score of ≥18 on the Mini-Mental State

Examination; iii) outpatients managed by an ambulatory care clinic; iv) ability to provide

informed consent and complete HSAT and study assessments, or the availability of a substitute

decision maker/caregiver to assist in these tasks. Patients where not eligible if: i) a

contraindication for the use of the HSAT was present (congestive heart failure or moderate to

severe pulmonary disease); ii) a medical device that would interfere with the placement of the

70

HSAT; iii) a significant physical impairment or language barrier that would restrict the ability to

complete study assessments and/or use a HSAT; iv) current treatment with CPAP for previous

diagnosis of OSA.

3.3.3 Study Procedure and Assessments

Patients who were eligible and consented to the study were first educated on the

importance of sleep disorders in the context of cognitive impairment. Various assessments and

questionnaires were then completed at baseline. Assessments related to cognition included the

Psychomotor Vigilance Task (PVT) (Lim and Dinges, 2008), Montreal Cognitive Assessment

(MoCA) (Nasreddine et al., 2005), Mini Mental State Examination (MMSE) (Folstein, Folstein

and McHugh, 1975), and the Toronto Cognitive Assessment (TorCA) (Freedman et al., 2018).

Sleep related questionnaires included the Epworth Sleepiness Scale (ESS) (Johns, 1991), Berlin

Questionnaire (BQ) (Netzer et al., 1999), and STOP-Bang questionnaire (SBQ) (Chung,

Abdullah and Liao, 2016). Additionally, to assess mood and behavioral symptoms, the Geriatric

Depression Scale (Yesavage et al., 1982) and Neuropsychiatric Inventory Questionnaire (NPI)

(Kaufer et al., 2000) were completed. Patients were then instructed how to use and apply the

HSAT and also provided instructional sheets as well as a phone number if additional assistance

was required (Appendix A-II).

3.3.4 Home Sleep Apnea Testing

Following completion of study assessments, patients were educated on how to use and

apply the HSAT to complete unattended testing in their home. The HSAT used in this study was

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the ApneaLink Air, which is a level III portable sleep monitor (Appendix A-I). This HSAT

records nasal flow/pressure, chest/respiratory effort, and oxygen saturation. The ApneaLink Air

has been validated for the detection of OSA against in-laboratory polysomnography (iPSG)

(Erman et al., 2007; Ng et al., 2009; Nigro et al., 2013). Actigraphy was also measured via

Philips Respironics Actiwatch 2 which has been validated for sleep-wake disturbances such as

sleep fragmentation (Sadeh, 2011) and was used by patients for up to 7 days. Actigraphy derived

variables that were assessed include wake after sleep onset (WASO, minutes), sleep efficiency

(%), total sleep time (minutes) and onset latency (minutes).

In accordance with the American Academy of Sleep Medicine (AASM) (Iber et al.,

2007), recordings where automatically scored by the ApneaLink Air software program and also

manually scored by a sleep physician. As previously discussed (Colelli et al., 2018), apneas were

recorded as a reduction of ≥90% in airflow for 10 seconds or longer and hypopneas recorded as a

reduction of ≥30% in airflow for 10 seconds or longer with an oxygen desaturation of 4% or

greater. The sum of both these values per hour were calculated to yield the patient’s apnea-

hypopnea index (AHI).

3.3.5 Outcome Measures

The primary objective of our study was to determine the prevalence of OSA in patients

with cognitive impairment. OSA is assessed by the apnea-hypopnea index (AHI), which provides

an indication of the severity of a patient’s sleep apnea. The definition of OSA used in our study

to determine prevalence was an AHI≥15 or an AHI≥5 with a significant oxygen desaturation of

≤88%. As previously reported (Patel et al., 2018), this modified definition is comparable to the

72

classical definition of OSA (AHI≥15) when comparing post-CPAP initiation outcomes.

Secondarily, we examined predictors of OSA in our cognitively impaired population.

3.3.6 Statistical Analyses

Normally and non-normally distributed continuous variables were described as mean and

standard deviations (SD). OSA diagnosis groups were compared using independent t-tests for

normally distributed continuous variables and the Mann-Whitney U test for non-normally

distributed continuous variables. Ordinal variables were described as median and interquartile

range (IQR) and the Mann-Whitney U test was used to compare groups. Finally, categorical

variables were described as frequency counts and percentages and groups were compared using

chi-square tests.

The prevalence of OSA was reported as a percentage of the total sample size and

similarly, the prevalence of OSA in each type of cognitive impairment were reported. Logistic

regression analysis was subsequently completed to determine factors that predicted a diagnosis

of OSA. The model included age, gender and variables that had a p-value<0.05 from the

univariate analysis. For each covariate included, the coefficient (±standard error, S.E), odds ratio

(OR), 95% confidence interval (C.I) and p-value were reported. Partial residual plots were

assessed for non-linearity of continuous variables and Variance Inflating Factor (VIF) was used

to assess for multi-collinearity and a cut-off of 10 was set where values greater than this number

indicated highly collinear variables.

For this study, statistical significance was set at a p-value <0.05. The Statistical Package

for the Social Science (version 24.0) and the statistics software “R”, version 3.4.1 (R Foundation

for Statistical Computing, Vienna Austria) was used to perform all analyses.

73

3.4 Results

Seventy patients from the feasibility study who completed HSAT and had ≥4 hours of

analyzable data were included in the analysis. Using the predefined definition of OSA for this

study (i.e. AHI≥15 or an AHI≥5 with a significant oxygen desaturation of ≤88% (Patel et al.,

2018)), thirty-six patients (51%) were diagnosed with OSA. The mean age (± SD) was 71.70

(11.37) and 42.9% of the study population identified as male (Table 3-1). Patients included were

diagnosed with AD (N=11, 15.7%), MCI due to AD (N=34, 48.6%), Vascular MCI (N=14,

20%), mixed dementia (N=6, 8.6%), and mixed MCI (N=5, 7.1%).

Several variables from the univariate analysis were found to be significantly different

between the OSA and non-OSA groups: male sex, larger neck circumference, a diagnosis of

dementia (as opposed to MCI), actigraphy-derived greater wake after sleep onset and lower sleep

efficiency, higher NPI caregiver score, lower MoCA score, and having low risk for OSA

(assessed by the STOP-Bang questionnaire).

74

Table 3-1: Patient demographics

Completed

HSAT (N=70) OSA (N=36) No OSA (N=34) p-value

Age (y), mean ± SD 71.70 ± 11.37 74.11 ± 9.18 69.15 ± 12.96 0.188 Male, N (%) 30 (42.9) 22 (61.1) 8 (23.5) 0.001* BMI (kg/m2), mean ± SD 25.69 ± 5.55 25.85 ± 5.35 25.53 ± 5.85 0.506 Neck Circumference (inches), mean ± SD 15.03 ± 1.38 15.51 ± 1.33 14.52 ± 1.26 0.002* Years of Education, mean ± SD 15.91 ± 3.41 15.85 ± 3.33 15.97 ± 3.54 0.888 Dementia (opposed to MCI), N (%) 17 (24.29) 13 (36.11) 4 (11.76) 0.018* Actigraphy Measures, mean ± SD

Wake After Sleep Onset (minutes) 63.03 ± 28.46 64.02 ± 33.36 42.37 ± 17.43 0.003* Sleep efficiency (%) 80.31 ± 11.23 76.35 ± 13.41 84.16 ± 6.86 0.001* Total Sleep Time (minutes) 467.12 ± 105.46 453.56 ± 113.09 480.27 ± 97.43 0.311 Onset Latency (minutes) 42.22 ± 46.40 52.70 ± 56.65 32.05 ± 31.31 0.061

ADFACS (N=66), mean ± SD Total Score 90.48 ± 12.44 87.67 ± 14.32 93.87 ± 8.83 0.164 IADL Score 86.08 ± 17.49 81.89 ± 19.99 91.10 ± 12.49 0.101 ADL Score 96.21 ± 8.72 94.69 ± 11.19 97.82 ± 4.58 0.220

NPI Total (N=45), mean ± SD 8.40 ± 8.51 9.92 ± 8.22 6.32 ± 8.66 0.068 NPI Caregiver (N=45), mean ± SD 5.22 ± 5.86 6.73 ± 6.45 3.16 ± 4.29 0.035* TorCA Overall Score (N=67), mean ± SD 248.58 ± 44.72 243.83 ± 47.54 253.78 ± 41.55 0.321 MMSE, median (IQR) 27 (5.5) 26 (4) 27 (4) 0.417 MoCA, median (IQR) 22.50 (6) 21.50 (6) 23 (5.75) 0.014* PVT Measures, mean ± SD

Mean RRT 3.12 ± 0.63 3.09 ± 0.68 3.16 ± 0.58 0.647 Mean FRRT 4.20 ± 0.66 4.14 ± 0.76 4.25 ± 0.56 0.704 Lapses (reaction time >500ms) 11.33 ± 16.01 12.89 ± 18.85 9.68 ± 12.39 0.719

Geriatric Depression Scale, median (IQR) 6.50 (9) 6 (1) 8 (9) 0.253 STOP-Bang Questionnaire, N (%)

Low Risk 23 (32.9) 7 (19.4) 16 (47.1) 0.014* Intermediate Risk 35 (50.0) 20 (55.6) 15 (44.1) 0.339 High Risk 12 (17.1) 9 (25) 3 (8.8) 0.073

Berlin Questionnaire - High Risk (opposed to Low Risk), N (%) 22 (31.4) 15 (41.7) 7 (20.6) 0.058

Epworth Sleepiness Scale ≥10, N (%) 13 (18.6) 5 (13.9) 8 (23.5) 0.300 Hypertension, N (%) 39 (55.7) 21 (58.3) 18 (52.9) 0.650 Hyperlipidemia, N (%) 38 (54.3) 22 (61.1) 16 (47.1) 0.238 Diabetes, N (%) 10 (14.3) 8 (22.2) 2 (5.9) 0.085 History of Smoking, N (%) 29 (41.4) 13 (36.1) 16 (47.1) 0.353

ADFACS: Alzheimer's Disease Functional Assessment and Change Scale; ADL: Basic Activities of Daily Living; BMI: Body Mass Index; FRRT: Fast Reciprocal Reaction Time; HSAT: Home Sleep Apnea Test; IADL: Instrumental Activities of Daily Living; MCI: Mild Cognitive Impairment; MMSE: Mini Mental Status Examination; MoCA: Montreal Cognitive Assessment; NPI: Neuropsychiatric Inventory Questionnaire; OSA: Obstructive Sleep Apnea; PVT: Psychomotor Vigilance Task; RRT: Reciprocal Reaction Time; SD: Standard Deviation; TorCA: Toronto Cognitive Assessment *Indicates p<0.05

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Univariate analysis demonstrated OSA was more prevalent in patients with dementia

compared to MCI (p=0.018). To further investigate this relationship, the different diagnoses were

assessed (Table 3-2). Comparing patients with and without OSA, OSA was most prevalent in AD

patients, where 82% of AD patients were diagnosed with OSA (p=0.028). There were non-

statistically different proportions of OSA observed in the other diagnoses.

Table 3-2: Prevalence of OSA by Patient Diagnosis (N=70) OSA No OSA p-value

Diagnosis Alzheimer’s disease (N=11) 9 (82%) 2 (18%) 0.028* MCI due to AD (N=34) 15 (44%) 19 (56%) 0.234 Vascular MCI (N=14) 6 (43%) 8 (57%) 0.473 Mixed Dementia (N=6) 4 (67%) 2 (33%) 0.674 Mixed MCI (N=5) 2 (40%) 3 (60%) 0.669

AD: Alzheimer’s disease; MCI: Mild Cognitive Impairment; OSA: Obstructive Sleep Apnea. *Indicates p<0.05

In our multivariate regression analyses, variables that were included were age, gender and

variables from the univariate analysis that had a p<0.05 (Table 3-1). The variables included were

age, gender, neck circumference, WASO, MoCA and low risk of OSA assessed by the STOP-

Bang questionnaire. Sleep efficiency and WASO are both measures of sleep fragmentation and

WASO was selected for inclusion as it was more strongly correlated with a diagnosis of OSA. A

diagnosis of dementia and MoCA both had a p-value ≤0.05; MoCA was selected for inclusion as

scores provide a good indication of level of cognitive functioning and influence clinical

diagnoses. Although the NPI caregiver distress score had a p-value ≤0.05, it was not included

due to a large proportion of missing data (36%). The logistic regression model identified two

variables to be significantly associated with an OSA diagnosis. Having more sleep

76

fragmentation, assessed by WASO (OR: 4.15, 95% C.I: 1.29, 13.31, p=0.017), and a lower

MoCA score was a significantly associated with OSA (OR: 0.27, 95% C.I: 0.09, 0.84, p=0.023).

Model diagnostics of the regression model demonstrated multicollinearity was not a

concern as all VIF values were less than 10. Also, assessment of partial residuals plots

demonstrated there was not a concern of non-linearity of continuous variables.

Table 3-3: Logistic Regression for Predictors of OSA

Coef ± S.E. Odds Ratio 95% CI of the Odds Ratio p-value

Age -0.01 ± 0.03 0.86 0.38, 11.92 0.707

Male Sex 1.27 ± 0.83 3.54 0.69, 18.16 0.129

Neck Circumference 0.39 ± 0.33 2.19 0.61, 7.88 0.231

WASO 0.04 ± 0.02 4.15 1.29, 13.31 0.017*

MoCA -0.22 ± 0.10 0.27 0.09, 0.84 0.023*

Low Risk of OSA

(assessed by SBQ) -0.46 ± 0.72 0.63 0.15, 2.58 0.522

Coef: β-Coefficient; CI: Confidence Interval; MoCA: Montreal Cognitive Assessment; OSA: Obstructive Sleep Apnea; SBQ: STOP-Bang Questionnaire; S.E.: Standard Error; WASO: Wake After Sleep Onset *Indicates p<0.05

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3.5 Discussion

The present study assessed the prevalence of OSA in 70 patients with cognitive

impairment and dementia. In this study, 51.4% of patients were diagnosed with OSA and OSA

was most prevalent in AD patients (81.8%). Our study demonstrated an OSA diagnosis was

independently associated with lower cognition (assessed via the MoCA) and higher sleep

fragmentation (assessed via actigraphy-derived WASO).

Moderate-to-severe OSA is estimated to be present in 13% of males and 6% of females in

the general population of 30-70 year olds (Peppard et al., 2013). These numbers increase in

patients with AD and other forms of dementia, ranging from a prevalence of 49-63% with

moderate-to-severe OSA (Rose et al., 2011; Gehrman et al., 2003; Jorge et al., 2019). In these

studies, OSA was diagnosed via iPSG (N=91) (Jorge et al., 2019), attended in-home PSG

(N=59) (Rose et al., 2011) and a modified HSAT (N=38) that recorded thoracic and abdominal

movements, wrist actigraphy as well as pulse oximetry in some patients (58%) (Gehrman et al.,

2003). The present study had a comparable prevalence using a diagnosis that included mild-to-

severe OSA. It was also seen that in patients diagnosed with AD, OSA was more prevalent, with

82% patients diagnosed with OSA; this is comparable to other studies that investigated AD

populations, which demonstrated 89% of AD patients have mild-to-severe OSA (Gehrman et al.,

2003; Jorge et al., 2019).

This study demonstrated lower scores on the MoCA, indicating lower cognition, was

significantly associated with OSA. This finding was similar to that found in another study that

assessed the association between OSA and cognitive performance (Spira et al., 2008). On

measures of cognition such as the MMSE, OSA was associated with cognitive impairment (Spira

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et al., 2008). Additionally, a meta-analysis has demonstrated an association between sleep apnea

and increased risk of developing cognitive impairment (Leng et al., 2017).

In our study, greater sleep fragmentation was also significantly associated with OSA. The

repetitive closure of the upper airway in OSA can result in sleep fragmentation and an

association has been seen with cognition. Sleep fragmentation was associated with increasing the

risk of cognitive impairment (Blackwell et al., 2006; Blackwell et al., 2014) and incident AD

(Lim et al., 2013). This highlights the importance of treating OSA and sleep fragmentation and

the potential benefit it may have on cognition.

In this study population, both groups (OSA and non-OSA) had an average BMI that was

on the lower end of the overweight range. Additionally, both groups had an average neck

circumference that was not considered to be “large” (i.e. men: ≥17 inches, women: ≥16 inches)

(Chung, Abdullah and Liao, 2016). This was also seen in another recent study of patients with

AD that had a high prevalence of OSA, yet patients had an average BMI that was not obese and

an average neck circumference not considered to be “large” (Jorge et al., 2019). This moves

away from the “typical” risk factors for OSA, which includes obesity and a large neck

circumference (Young, Skatrud and Peppard, 2004), suggesting there may be different clinical

presentations of OSA in this population. Different clinical presentation of OSA could be due to

different OSA phenotypes, which was seen in a cluster analysis that identified patients may

present with disturbed sleep, daytime sleepiness, or with low symptoms of OSA (Ye et al.,

2014). As OSA may play a modifiable role in cognitive dysfunction, future research should

investigate the different clinical presentations of OSA as it may help in understanding the

disease, as well in management and treatment.

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Additionally, univariate analysis demonstrated patients with OSA were more likely to

have caregivers that reported high amounts of distress (assessed via the NPI caregiver distress

score). The present study was limited in assessing this relationship as not all participants had

accompanying caregivers to complete the assessments, however, it raises interest into the patient-

caregiver relationship and how OSA may play a role in caregiver burden and distress. The

caregivers that accompanied participants in the present study were spouses/partners, siblings

and/or children, and caring for a family member that has cognitive impairment can add a lot of

stress and burden onto them. One study that investigated sleep disturbances in AD patients

demonstrated that sleep disturbances, such as snoring, and night-time awakenings and

wandering, were associated with burden on caregivers (Gehrman et al., 2018). Moreover, in

patients who continued therapy with CPAP, the caregivers’ own sleep was also reported to have

positive improvements (Cooke et al., 2009b). Future studies should further investigate this

relationship as treating OSA has the potential to benefit the patient as well as the caregiver by

reducing nighttime burden.

Our study was limited in that HSAT and not iPSG, the gold standard for diagnosing

OSA, was used to determine the prevalence in this population. Although the HSAT (ApneaLink)

used has been validated against iPSG and is highly specific (100%) and sensitive (97%) (Ng et

al., 2009), it has not been validated in this population. Also, as HSAT does not include EEG to

assess hypopneas associated cortical arousal it may underestimate a patient’s sleep disordered

breathing. Second, patients with significant language barrier or physical impairment that would

inhibit them from completing study assessments and/or use the HSAT as well as patients who

had severe cognitive impairment (i.e. a score <18 on the MMSE) were not enrolled; therefore the

prevalence of OSA would be for a subset of the population that has mild-to-moderate cognitive

80

impairment. It is also important to note that patients currently being treated for OSA were not

eligible and the true prevalence may be greater than the prevalence determined in the present

study.

3.6 Conclusion

The present study demonstrated OSA was prevalent in this patient population of

cognitive impairment. OSA was associated with lower cognition and higher sleep fragmentation

and OSA was highly prevalent in patients diagnosed with AD. As research has demonstrated

treatment of OSA can improve cognition in patients with cognitive impairment, future work

should be directed towards determining diagnostic and screening tools that are efficient and easy

to use for this population of interest. In doing so, treatment and management of detected OSA

may have beneficial outcomes on cognitive and functional status, and may prevent further

decline and/or stabilize cognition.

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Chapter 4: General Discussion

82

4.1 General Discussion

The present thesis investigated the feasibility of HSAT in a population of patients with

cognitive impairment. To our knowledge, our study was the first to demonstrate that broad

screening for OSA using HSAT in a cognitively impaired clinic population was feasible and

practical. The only independent predictor of obtaining analyzable HSAT data was completing the

baseline assessment in the morning.

In addition, previous research has examined the prevalence of sleep disturbances in

patients with cognitive impairment, however, these studies used subjective sleep questionnaires

to assess sleep. The few studies that have assessed for prevalence of OSA using objective

measurements have been limited to specific types of dementia such as AD and general

unclassified dementia. Our study was the first to assess the prevalence of OSA using objective

measurements in a broad range of cognitive impairment such as AD, MCI, VCI, and mixed

dementias. Predictors of a diagnosis of OSA were higher sleep fragmentation and lower

cognition.

4.1.1 Feasibility and Practicality of HSAT

In chapter 2, we demonstrated that HSAT was a feasible technique as over 90% of

patients had complete and analyzable data (i.e. defined as ≥4 hours of analyzable data). To date,

only one previous study has assessed the feasibility of HSAT in a subset of patients with

cognitive impairment, specifically MCI (Vaughan et al., 2016). They demonstrated low rates of

successful HSAT completion, where 50% of patients with MCI had analyzable data (i.e. ≥4

hours of analyzable data). In another study of patients with dementia, a similar proportion of

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patients to the present study had complete attended in-home PSG data (88%) (Rose et al., 2011).

Comparing the results of Vaughan et al. (2016) to the present one, the higher proportion of

analyzable HSAT recordings in the present study could be due to the approach used to educate

the patients on how to use the HSAT. In the study by Vaughan et al. (2016), patients were sent

the HSAT via mail with instructions and were provided a phone number to call if they needed

assistance. In the present study, patients were educated and taught how to apply the HSAT in-

person at the study visit, and were also provided written instructions and a phone number to call

if they required assistance and troubleshooting. This demonstrated the importance of properly

educating patients with cognitive impairment as in-person instructions may have allowed for a

better understanding of the HSAT and provided an opportunity to ask questions, which

subsequently led to a higher rate of HSAT completion.

The time of day when the assessment occurred was independently associated with

obtaining analyzable HSAT data. After baseline assessments were completed, a demonstration of

how to use and apply the HSAT was completed. Ad-hoc analyses demonstrated that the time of

day did not significantly impact scores on assessments of cognition such as the TorCA, MMSE,

MoCA and PVT. Therefore, the finding that time of day was associated with an analyzable

HSAT may be because patients were more alert to the instructions earlier in the day. This was

further supported by the fact that on univariate analyses, obtaining analyzable data was

associated with a lower lapse count.

The present study also found that HSAT was considered a practical technique for use in

cognitive neurology clinics as 60% of patients approached for participation agreed and had

complete and analyzable data. In the Wisconsin Sleep Cohort Study, they reported 53% of

participants invited to complete iPSG had an adequate study (Peppard et al., 2013). In studies

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examining cognitively impaired populations, the proportions were similar, as 48-55% of patients

who were approached successfully completed in-home PSG (Ancoli-Israel et al., 2008; Rose et

al., 2011). The present study had comparable proportions to studies that assessed patients with

and without cognitive impairment. This demonstrated that HSAT was a practical technique as a

high proportion of patients accepted participation in the study and successfully obtained

analyzable data in an unattended setting.

In addition to HSAT being demonstrated to be a feasible and practical technique, it was

also positively received from patients. In a survey that was completed after use of HSAT, most

patients stated that they did not have trouble attaching the device and its components to their

body and that compared to a normal night’s sleep, wearing the HSAT was “manageable” to

“completely comfortable”. Regarding their awareness of the device at night, just over half of the

respondents stated they were “aware” of the device compared to those that stated they were

“unaware” of the device during their sleep. Finally, the HSAT received positive feedback with

regards to overall experience as most patients stated their experience was “good” to “excellent”.

The positive feedback received demonstrated that the HSAT used in the present study is a device

that is easy to use and did not impact the patient’s comfort during their sleep.

Patients who are referred to iPSG are often unwilling to complete the sleep study, as they

do not want to spend a night in a sleep lab. This could potentially be even more difficult for

patients with cognitive impairment due to cognitive and functional difficulties. As the HSAT was

demonstrated to be feasible, practical, and received positive feedback, it has the potential to be a

suitable alternative to iPSG in this patient population.

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4.1.2 OSA Prevalence and Risk Factors

In chapter 3, we demonstrated that OSA is prevalent in patients with cognitive

impairment as 51% of the patients who had an analyzable HSAT were diagnosed with OSA.

OSA was most prevalent in patients with AD, as 82% of AD patients were found to have OSA.

This is comparable to previous studies that have reported a prevalence of 89% in patients with

AD (Gehrman et al., 2003; Jorge et al., 2019). In our study, OSA was also prevalent amongst the

other forms of cognitive impairment (i.e. MCI, VaMCI, and mixed dementias).

Factors that were found to be associated with OSA included lower cognition and higher

sleep fragmentation. Previous research has supported the association between OSA and lower

cognition (Spira et al., 2008) and between OSA and sleep fragmentation (Gagnon et al., 2014).

Although these two factors were the only significant independent predictors of OSA, the trends

in our data were in line with current knowledge of risk factors for OSA. Male sex and larger

neck circumference are known risk factors for OSA (Young, Skatrud and Peppard, 2004) and the

present study demonstrated an association between these factors and a diagnosis of OSA on

univariate analyses. Research has also demonstrated higher sleep fragmentation and lower sleep

efficiency to be associated with increased risk of cognitive impairment (Blackwell et al., 2006;

Blackwell et al., 2014) and both these factors were associated with OSA in the present study.

Therefore, OSA may play a modifiable role in cognitive impairment where treating OSA, which

would decrease sleep fragmentation and improve sleep efficiency, may have beneficial outcomes

on cognition. The modifiable role OSA may play was demonstrated in the Alzheimer’s Disease

Neuroimaging Initiative study, where treating OSA with CPAP was associated with a later age of

cognitive impairment (Osorio et al., 2015).

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4.1.3 Thesis Limitations

The present thesis did have its limitations. For our inclusion criteria, we excluded patients

with significant physical impairment and/or a language barrier that would restrict them from

completing study assessments and questionnaires and/or use the HSAT, as well excluded patients

with a MMSE score <18 (i.e. more severe cognitive impairment). Excluding these patients may

limit the generalizability of our results to a subset of patients who had mild-to-moderate

cognitive impairment. This limitation of generalizability would impact the feasibility and

practicality of the HSAT and we cannot make conclusions on the use of HSAT in patients with

more severe cognitive impairment. This limitation would also extend to the prevalence of OSA,

in that the prevalence of OSA may vary in patients with more severe cognitive impairment and

our results would only reflect patients with mild to moderate cognitive impairment. Also, as this

study was completed at tertiary care cognitive neurology clinics, it may limit the generalizability

of the findings to primary care clinics. Finally, the HSAT used in this study was the ApneaLink

Air and our results regarding feasibility and practicality may only be generalizable to this HSAT,

and may not be generalizable to use of other HSATs in patients with cognitive impairment.

An additional limitation is the possible bias that may have been present due to the

enrollment procedure of the study. For the enrollment procedure, patients were referred to the

study whether or not they endorsed subjective sleep complaints. However, there may have been

an unintentional pre-filtering of patients by the cognitive neurologist, where patients that had

subjective sleep complaints and/or were more compliant with prior therapies were more likely to

be referred. Of the 81 patients included in the study, 67% of patients endorsed sleep complaints

at their previous clinic visits; this rate of sleep complaints is comparable to that previously

reported in patients with cognitive impairment (Guarnieri et al., 2012). Although patients were

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included if they did not have sleep complaints, the possible lack of referral of these patients may

have biased the findings in favor of the practicality of HSAT (i.e. patients with sleep complaints

may have been more likely to accept and complete HSAT). An ideal study would approach all

patients, regardless of patient characteristics; such a study would be able to account for these

biases and provide a better representation of what the true feasibility and practicality of HSAT is

in tertiary cognitive neurology clinics.

With respect to our analysis on the feasibility and practicality of HSAT in this

population, there were 36 patients who declined participation in this study. Without informed

consent, we were limited in collecting data to determine factors that were associated with

accepting or declining participation. Assessing these factors would be of great interest with

respect to the practicality of HSAT, as information on these patients would allow for a better

understanding of why patients chose not to participate in a study that investigated use of HSATs.

This limitation is unfortunately a limitation of all REB-approved studies that prevent the

collection of data on patients who have not provided informed consent.

With respect to our analysis on the prevalence of OSA in this population, another

limitation was that HSAT was used to objectively measure OSA and not iPSG, which is the gold

standard. The HSAT that was used has previously been validated against iPSG and is highly

specific (100%) and sensitive (97%) (Ng et al., 2009), however, it has not been validated in this

patient population. Also, as HSAT does not include EEG to assess hypopneas associated cortical

arousal it may underestimate a patient’s sleep disordered breathing.

A final comment is that patients who were currently using therapy, such as CPAP, for

previously diagnosed OSA were excluded. Therefore, the prevalence of OSA in a cognitively

impaired population would likely be greater than the prevalence reported in the present thesis.

88

Chapter 5: Conclusions

The present study was novel as a broad range of patients with cognitive impairment,

ranging from mild cognitive impairment to dementia, were included to assess the feasibility and

practicality of the HSAT as well as to estimate the prevalence of OSA in this population. We

demonstrated that HSAT was a feasible and practical technique for the screening of OSA. HSAT

also received positive feedback with regards to ease of use and overall experience with the

device. In this patient population of cognitive impairment, there was a high prevalence of OSA.

As OSA is closely associated with cognitive impairment and may be a modifiable risk factor,

timely diagnosis and treatment may improve clinical outcomes such as cognition. HSAT has the

potential to be a suitable alternative to iPSG in expediting sleep specialist consultation and

treatment. Future randomized controlled trials should further investigate this relationship as it

could have important implications on healthcare.

89

Chapter 6: Future Directions

6.1 HSAT vs. PSG

The HSAT used in this study has been validated against iPSG (Erman et al., 2007; Ng et

al., 2009) and the present study has demonstrated HSAT to be a feasible and practical technique

in a cognitively impaired clinic population. Future research is needed to determine if HSAT is

comparable to iPSG for the diagnosis of OSA in this patient population; future work should also

assess the cost-effectiveness of HSAT vs. iPSG in diagnosing OSA in this patient population. A

randomized controlled trial in this population would be valuable to determine whether HSAT is

comparable to iPSG, the current standard of care, with regards to time to diagnosis and rates of

OSA diagnosis and whether it is a cost-effective method compared to iPSG.

6.2 Impact of CPAP Therapy on Cognition and Function

In this feasibility study, patients diagnosed with OSA were referred for sleep consultation

with a sleep physician. If deemed clinically suitable, patients were offered auto-PAP to use for

two weeks, followed by CPAP set at a fixed pressure. Research has demonstrated treatment of

OSA with CPAP in patients with cognitive impairment has benefits on cognition (Ancoli-Israel

et al., 2008). This was of interest in the present study and patients completed follow-up

assessments post-CPAP prescription. A small portion of the patients diagnosed with OSA and

prescribed CPAP have completed follow-up and current follow-up assessments are ongoing for

the remaining portion. As the current data set would be too small to complete meaningful

analysis, it is of interest to determine if CPAP use was associated with improved cognitive and

90

functional status from baseline assessments. In small sample sized studies, it was demonstrated

that long-term use of CPAP therapy in patients with AD was associated with a slower decline in

cognition (Cooke et al., 2009b; Troussière et al., 2014) and also stabilized sleep and mood

(Cooke et al., 2009b). Future work should be directed at completing longitudinal studies with

larger sample sizes to assess if long-term use of CPAP is associated with benefits in cognition

and function in patients with cognitive impairment.

6.3 Examining the Relationship Between OSA and Cognitive

Impairment

Several studies have examined the relationship between OSA and cognitive impairment

and the impact CPAP may have on cognition. Additionally, studies have shown the relationship

with OSA and dementia biomarkers. In particular, one study found that untreated OSA was

associated with biomarkers of AD pathology whereas patients using CPAP therapy for OSA did

not show this association (Liguori et al., 2017). This study was cross-sectional, in that they only

assessed people at one time point. It would be interesting to see if a longitudinal analysis

demonstrated differences between patients who used CPAP compared to those who discontinued

treatment. An ideal study would recruit patients with cognitive impairment and have them

complete a baseline assessment that would include neuropsychological testing, iPSG for sleep

assessment, and CSF analysis and neuroimaging such as PET and/or MRI. In patients who are

diagnosed with OSA, they would be offered CPAP therapy. All patients would then complete a

follow-up study visit where they would again complete neuropsychological testing and the

biomarker analyses. For the analysis, patients would be divided into groups: OSA on no therapy,

91

OSA using CPAP therapy, and no OSA. Analyses would be able to provide insight into the

longitudinal changes on cognition and biomarkers, assess the role OSA plays in cognition, and

examine whether CPAP therapy can improve cognition and function. It is important to note that a

study like this would be very demanding for patients with cognitive impairment as it would

include invasive techniques and multiple research visits.

6.4 Phenotypes of OSA

Cluster analysis has been used in various diseases to better understand the subtypes of

that particular disease. It is appreciated that the clinical presentation of OSA varies between

patients and that OSA is a heterogeneous disease. One of the first attempts to characterize the

different clinical presentations of OSA was completed in the Icelandic Sleep Apnoea Cohort (Ye

et al., 2014), which assessed moderate-to-severe OSA in 822 patients. They identified three

clusters of OSA classification, which included patients with disturbed sleep (cluster 1), patients

who had minimal symptoms of OSA (cluster 2), and patients who had excessive daytime

sleepiness (cluster 3). They highlighted the different clinical presentations of OSA and how

many patients may not present with many of the traditional symptoms of OSA, such as daytime

sleepiness and snoring, which may lead to undiagnosed OSA. One study that extended upon the

Icelandic Sleep Apnea Cohort was the Sleep Apnea Global Interdisciplinary Consortium

(Keenan et al., 2018) that investigated OSA in 972 patients across six countries to assess clusters

of OSA in a global population that was ethnically diverse. They identified three clusters that

were similar to the Icelandic study as well as an additional two, which included sleepiness

dominant and dominant upper airway symptoms.

92

Understanding the various OSA phenotypes can have important implications for

personalized treatment strategies. Another analysis was completed on the Icelandic Sleep Apnea

Cohort, where the relationship between OSA phenotypes (i.e. the three clusters) and therapy with

positive airway pressure (PAP) was investigated (Pien et al., 2018). They found patients who

used PAP therapy had improved symptoms of OSA in all clusters, and participants with

excessive daytime sleepiness had the greatest improvements. Also, patient use and response to

PAP therapy varied in patients with disturbed sleep. This knowledge of OSA phenotypes and

which patients are likely to use and respond to therapy can allow for personalized treatment

strategies to be developed.

To better understand the clinical presentations of OSA in patients with cognitive

impairment, future research should investigate the different OSA phenotypes in this population

of interest. In chapter 3, we determined OSA was prevalent in this population. Examining the

patient demographics, it was seen that patients who were diagnosed with OSA did not have the

typical risk factors for OSA such as an elevated BMI suggestive of obesity or “large” neck

circumference (Young, Skatrud and Peppard, 2004; Chung, Abdullah and Liao, 2016). Also few

patients (14%) with OSA reported excessive daytime sleepiness and ad-hoc analysis

demonstrated 44% of OSA patients reported snoring loudly at night. In patients with cognitive

impairment, there is variability in the presentation of OSA and future studies of larger sample

sizes can characterize these clusters to determine if similar clusters to the ones discussed above

exist, or if distinct clusters exist in a cognitively impaired population. Furthermore, as OSA may

play a modifiable role in cognitive impairment, investigating response and benefit to CPAP

therapy in the different OSA phenotypes may allow for personalized treatment strategies.

93

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Appendix A: HSAT Device, Instructions and Survey

I) Components of the HSAT

A: HSAT portable pouch, B: Chest strap, C: Effort sensor, D: Pulse oximeter, E: Nasal cannula, F: ApneaLink Air monitor

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II) HSAT Patient Instructions

Instructions for using the Home Sleep Apnea Test

1. Thread the Velcro strap through the ApneaLinkTM Air device with the ResMed logo on the tabs facing outwards.

2. Take the left end of the Velcro belt and thread it through the effort sensor.

3. Attach effort sensor connector to ApneaLinkTM device (lower left side).

4. Place the Velcro belt with ApneaLinkTM device and effort sensor on your upper chest, just under your armpits.

Make sure the device is lying on the centre of your chest (midway between the nipples) 5. Secure the belt so that it does not slide down

during the night.

4

6. Insert nasal prongs in your nostrils, with the curved side pointing downwards toward your nose. Loop plastic tubing around your ears.

7. To secure the tube, pull slider up towards your chin.

6

Slider

8. Plug in connector end of nasal cannula into the connector on the device (lower right side).

9. Place finger sensor over the index finger (finger beside thumb) on your non-dominant hand.

Make sure the wire is lying on top of your finger.

9

2

1

Page 1 of 2 Version 1.0 Last updated August 21, 2018 - DC

126

10 10. Attach black connector from the finger sensor to the blue oximeter device. Fix onto belt clip and slide on Velcro strap.

Belt Clip Velcro Strap

11. Connect black oximeter connector to device (upper left side).

If properly set up, the full system should look like this.

12 13 To turn device on: 12. Press and hold the power button for

3 seconds or until light turns on. 13. Check that all 4 lights are green.

Proceed to sleep. If the lights are red and blinking then the

device is not attached correctly. Please re-try the steps above.

14 15 To turn device off (when you wake up): 14. Press and hold power button for 3

seconds. 15. Check that the test complete

indicator light (top left) is green. This means the test is successful and complete.

Please Note: • You can use medical tape to secure the nasal tube or finger sensor in place if needed. • You can remove the finger sensor if you need to go to the bathroom; please

remember to replace the finger sensor after washing your hands and going back to sleep.

Questions or concerns regarding the use of these devices? Please contact David Colelli, Research Coordinator, Sunnybrook Research Institute.

Tel: 647-282-0892 E-mail: david.colelli@sunnybrook.ca Visit https://tinyurl.com/SNBstudy for a video demonstration.

Please do not forget to return all your equipment at your next visit.

Page 2 of 2

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III) HSAT Patient Survey This survey was designed to get your perspective on the home sleep apnea test. In this short survey you will be asked to rate the product based on your own experience. The answers you give will help us in assessing how patients responded to using this device. This survey can be completed by either yourself or your caregiver.

1. Did you need any assistance to assemble the device?

O Yes

O No

O I don’t know

2. How easy was it to attach the recorder & effort sensor to your body?

Very difficult Somewhat difficult Neutral Easy Very Easy

O O O O O

3. Did you use tape and/or adhesive pads to keep the nasal cannula securely in place?

O Yes

O No

O Other: ______________________

4. During the night recording how many hours of sleep were you able to obtain?

O < 4 hours

O 4 – 6 hours

O 6 – 8 hours

O 8 – 10 hours

O > 10 hours

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5. How comfortable were you during the night recording wearing the home sleep apnea test compared to a normal night’s sleep?

Extremely uncomfortable

Somewhat uncomfortable Manageable Relatively

comfortable Completely comfortable

O O O O O

6. How many times did you wake up during the night recording?

O None

O 1 – 2 times

O Other: _______

7. During the night recording how aware were you of the home sleep apnea test?

Very aware Somewhat aware Neutral Fairly

unaware Completely

unaware

O O O O O

8. The night of the recording, at what time did you fall asleep?

9. The night of the recording, how many minutes did it take you to fall asleep?

10. The night of the recording, what time did you wake up in the morning?

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11. Did you encounter any disruptions during your night recording sleep?

O Yes

O No

O I don’t know

12. If so, please elaborate below:

13. How would you rate your overall experience using the home sleep apnea test?

Very poor Poor Neutral Good Excellent

O O O O O

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Appendix B: Informed Consent Form INFORMEDCONSENTTOPARTICIPATEINARESEARCHSTUDY

FullStudyTitle:EnhancingCognitionbyusingLow-costIn-homePortableSleepEquipment(ECLIPSE)PrincipalInvestigator:Dr.MarkI.Boulos,SleepNeurologist,416-480-4473Sponsor:Thisstudyisbeingsupportedbydonations____________________________________________________________________INFORMEDCONSENT

Youarebeingaskedtoconsiderparticipatinginaresearchstudy.Aresearchstudyisawayofgatheringinformationonatreatment,procedureormedicaldeviceortoansweraquestionaboutsomethingthatisnotwellunderstood.Thisformexplainsthepurposeofthisresearchstudy,providesinformationaboutportable(ambulatory)sleepmonitors,thetestsandproceduresinvolved,possiblerisksandbenefits,andtherightsofparticipants.Pleasereadthisformcarefullyandaskanyquestionsyoumayhave.Youmayhavethisformandallinformationconcerningthestudyexplainedtoyou.Ifyouwish,someonemaybeavailabletoverballytranslatethisformintoyourpreferredlanguage.Pleaseaskthestudystafforoneoftheinvestigatorstoclarifyanythingyoudonotunderstandorwouldliketoknowmoreabout.Makesureallyourquestionsareansweredtoyoursatisfactionbeforedecidingwhethertoparticipateinthisresearchstudy.Participatinginthisstudyisyourchoice(voluntary).Youhavetherighttochoosenottoparticipate,ortostopparticipatinginthisstudyatanytime.INTRODUCTION

Youarebeingaskedtoconsiderparticipatinginthisstudybecauseyoumayhavetroubleswithcognition(e.g.challengeswithyourmemory,languageorplanningabilities).Manypeoplewithcognitivetroublesalsohavesleepproblemssuchasobstructivesleepapnea(abnormalpausesinbreathingduringsleep),restlesslegssyndrome(whichcancauseinvoluntarylegmovementsinsleep),and/orfragmentedsleep.Thepresenceofsleepdisordersinpatientswithcognitiveissuesmaybeassociatedwithworseoutcomesinthefuture.

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WHATISTHEUSUALTREATMENT?Sleepdisordersareusuallydiagnosedinasleeplaboratoryduringanovernightsleepstudy(polysomnography).However,somepatientsdonotlikesleepinginasleeplaboratory.Inaddition,highcostsandlongwaitingtimesareothersbarrierstopolysomnography.WHYISTHISSTUDYBEINGDONE?

Thepurposeofthisstudyistodeterminewhetherusingportablesleepmonitorsinpatientswithcognitiveimpairmentispossibleandpractical.Portablesleepmonitorscanbeusedinapatient’sownbed,aremuchlessexpensive,andserveasgoodscreeningtoolscomparedtopolysomnographyinthedetectionofsleepproblems.Theuseofportablesleepequipmentmayallowforearlierdetectionofsleepdisordersinlargenumbersofpatientswithcognitiveissues,whichmay(butisnotguaranteed)toleadtoimprovedoutcomesforpatients.

WHATWILLHAPPENDURINGTHISSTUDY?Youwillbeeducatedabouttheimportanceofsleepdisordersinthecontextofcognitiveimpairment.Next,youwillcompletesomestandardizedquestionnairesandassessmentsrelatedtosleepproblems,functionalstatus,cognition,moodandbehaviour.Afterwards,youwillbetaughthowtoapplytheportablesleepmonitors.Therearetwosleepapneadetectionmonitors;eitherwillbeusedforasinglenight.Youcandiscusswiththeresearchteamwhichofthetwodevicesyouwouldliketouse.Oneofthesedeviceswillinvolvewearingasmallprobeonyourfinger,aneffortsensoraroundyourchestandanasalcannula.Theotherdeviceissimplywornaroundyourwrist.Actigraphy(similartowearingawrist-watchoranklet)willbeusedforupto7days.Youwillalsobeofferedthechancetomeasureyourbrainwaveactivity(electroencephalography)foronenightathome.Thiswillinvolveplacingfourelectrodesonyourheadandasmallsensoronyourforehead.Ifyouareunabletoapplythesleepmonitor(s),wewouldbehappytoshowafamilymemberorcaregiverhowtousethesleepequipment.Theresearchassistantwillcollectthesleepmonitorfromyouwhenyouarenextinthehospital.Ifyouarefoundtohaveasleepdisorder,youwillbeofferedtheusualcare,whichmayincludeanappointmentwithasleepspecialistand/orfullpolysomnography.Aswithanymedicaltreatment,itisyourchoicetoacceptordeclineit.Threetosixmonthsafterjoiningthestudy,youmaybere-assessedin-person(whenyoureturntothehospitalforanappointmentortest).Atthistime,youwillagaincompletesomestandardizedquestionnairesandassessmentsrelatedtosleepproblems,functionalstatus,cognition,andmood.

132

HOWMANYPEOPLEWILLTAKEPARTINTHISSTUDY?Itisanticipatedthatabout200peoplewillparticipateinthisstudyatSunnybrook.Thelengthofthisstudyforparticipantsisonenight,andthetimeittakesyoutocompletetheotherstudyassessments.Theentirestudyisexpectedtotakeabout3yearstocompleteandtheresultsshouldbeknowninabout3.5years.WHATARETHERESPONSIBILITIESOFSTUDYPARTICIPANTS?Ifyoudecidetoparticipateinthisstudyyouwillbeaskedtodothefollowing:

• Completesomestandardizedquestionnairesandassessmentsrelatedtosleepproblems,functionalstatus,cognition,moodandbehaviour.Theseassessmentswilltakeapproximately60-90minutestocomplete,andwilloccuratbaselineand3-6monthsafteryoujointhestudy.

• Useasleepapneadetectionmonitorforasinglenightatthebeginningofthestudy,and/oruseactigraphy(similartowearingawrist-watchoranklet)forupto7daysatthebeginningofthestudy.Youwillalsobeofferedthechancetomeasureyourbrainwaveactivity(electroencephalography)foronenightathome.

WHATARETHERISKSORHARMSOFPARTICIPATINGINTHISSTUDY?Youmayexperiencesideeffectsfromparticipatinginthisstudy.Somesideeffectsareknownandarelistedbelow,buttheremaybeothersideeffectsthatarenotexpected.Ifyoudecidetotakepartinthisstudy,youshouldcontactDr.MarkBoulos(neurologistspecializinginsleepdisorders,telephone416-480-4473)aboutanysideeffectsthatyouexperience.Risksandsideeffectsincludethefollowing:

• Non-physicalRisks:Lossoftimespentathomeorwork• PhysicalRisks:Youmayfindwearingthesleepmonitorstobemildly

uncomfortable.Thenasalcannula,pulseoximeter,chestbands,surfaceelectrodesand/oractigraphyusedduringtheportablesleepmonitoringcancausetemporaryminorirritationoftheskin(Possible)

Youwillbetoldaboutanynewinformationthatmightreasonablyaffectyourwillingnesstocontinuetoparticipateinthisstudyassoonastheinformationbecomesavailabletothestudystaff.

133

WHATARETHEBENEFITSOFPARTICIPATINGINTHISSTUDY?Youmayormaynotbenefitdirectlyfromparticipatinginthisstudy.However,researchsuggeststhatsleepdisordersarecloselylinkedwithcognitivetroubles.Asaresult,patientsmaybenefitfromhavingtheirunderlyingsleepdisordersidentifiedandultimatelytreated.Identificationandtreatmentofsleepdisordersmayimprovecognitionandimproveneurologicalfunction,butthisisnotguaranteed.Inaddition,yourparticipationmayormaynothelpotherpeopleinthefuture.CANPARTICIPATIONINTHISSTUDYENDEARLY?Theinvestigatorsmaydecidetoremoveyoufromthisstudywithoutyourconsentforanyofthefollowingreasons:

• Theinvestigatorsdecidethatcontinuinginthisstudywouldbeharmfultoyou.• Youareunableorunwillingtofollowthestudyprocedures.

Ifyouareremovedfromthisstudy,theinvestigatorswilldiscussthereasonswithyouandplanswillbemadeforyourcontinuedcareoutsideofthestudy.Youcanalsochoosetoendyourparticipationatanytimewithouthavingtoprovideareason.Ifyouchoosetowithdraw,yourchoicewillnothaveanyeffectonyourcurrentorfuturemedicaltreatment.Ifyouwithdrawvoluntarilyfromthestudy,youareencouragedtocontactDr.MarkBoulos(416-480-4473).WHATARETHECOSTSOFPARTICIPATINGINTHISSTUDY?Takingpartinthisstudyshouldnotleadtoanyaddedcoststoyou.WHATHAPPENSIFIHAVEARESEARCHRELATEDINJURY?Ifyoubecomesickorinjuredasadirectresultofyourparticipationinthisstudy,yourmedicalcarewillbeprovided.Financialcompensationforsuchthingsaslostwages,disabilityordiscomfortduetothistypeofinjuryisnotroutinelyavailable.Bysigningthisconsentform,youdonotgiveupanyofyourlegalrights.ARESTUDYPARTICIPANTSPAIDTOPARTICIPATEINTHISSTUDY?Youwillnotbepaidtoparticipateinthisstudy.

134

HOWWILLMYINFORMATIONBEKEPTCONFIDENTIAL?Youhavetherighttohaveanyinformationaboutyouthatiscollected,usedordisclosedforthisstudytobehandledinaconfidentialmanner.Ifyoudecidetoparticipateinthisstudy,theinvestigator(s)andstudystaffwilllookatyourpersonalhealthinformationandcollectonlytheinformationtheyneedforthisstudy.“Personalhealthinformation”ishealthinformationaboutyouthatcouldidentifyyoubecauseitincludesinformationsuchasyour;

• name,• address,• telephonenumber,• dateofbirth,• newandexistingmedicalrecords,or• thetypes,datesandresultsofvarioustestsandprocedures.

Youhavetherighttoaccess,reviewandrequestchangestoyourpersonalhealthinformation. The following people may come to the hospital to look at your personal health information to check that the information collected for the study is correct and to make sure the study followed the required laws and guidelines:

• Representatives of the Sunnybrook Research Institute, Sunnybrook Health Sciences Centre or the Sunnybrook Research Ethics Board, because they oversee the ethical conduct of research studies at Sunnybrook.

Access to your personal health information will take place under the supervision of the Principal Investigator. “Study data" is health information about you that is collected for the study, but that does not directly identify you. The investigators, study staff and the other people listed above will keep the information they see or receive about you confidential, to the extent permitted by applicable laws. Even though the risk of identifying you from the study data is very small, it can never be completely eliminated. The Principal Investigator will keep any personal health information about you in a secure and confidential location for 10 years and then destroy it according to Sunnybrook policy. When the results of this study are published, your identity will not be disclosed. Youhavetherighttobeinformedoftheresultsofthisstudyoncetheentirestudyiscomplete.DOTHEINVESTIGATORSHAVEANYCONFLICTSOFINTEREST?Therearenoconflictsofinteresttodeclarerelatedtothisstudy.

135

WHATARETHERIGHTSOFPARTICIPANTSINARESEARCHSTUDY?Youhavetherighttoreceiveallinformationthatcouldhelpyoumakeadecisionaboutparticipatinginthisstudy.Youalsohavetherighttoaskquestionsaboutthisstudyandyourrightsasaresearchparticipant,andtohavethemansweredtoyoursatisfactionbeforeyoumakeanydecision.Youalsohavetherighttoaskquestionsandtoreceiveanswersthroughoutthisstudy.Ifyouhaveanyquestionsaboutthisstudyyoumaycontactthepersoninchargeofthisstudy(PrincipalInvestigator):Dr.MarkBoulos,DivisionofNeurology,416-480-4473.TheSunnybrookResearchEthicsBoardhasreviewedthisstudy.Ifyouhavequestionsaboutyourrightsasaresearchparticipantoranyethicalissuesrelatedtothisstudythatyouwishtodiscusswithsomeonenotdirectlyinvolvedwiththestudy,youmaycalltheChairoftheSunnybrookResearchEthicsBoardat(416)480-6100ext.88144.

136

DOCUMENTATIONOFINFORMEDCONSENTYouwillbegivenacopyofthisinformedconsentformafterithasbeensignedanddatedbyyouandthestudystaff.FullStudyTitle:EnhancingCognitionbyusingLow-costIn-homePortableSleepEquipment(ECLIPSE)NameofParticipant:________________________________________

Participant/Substitutedecision-makerBysigningthisform,Iconfirmthat:• Thisresearchstudyhasbeenfullyexplainedtomeandallofmyquestionswereansweredtomysatisfaction

• Iunderstandtherequirementsofparticipatinginthisresearchstudy• Ihavebeeninformedoftherisksandbenefits,ifany,ofparticipatinginthisresearchstudy

• Ihavebeeninformedofanyalternativestoparticipatinginthisresearchstudy• Ihavebeeninformedoftherightsofresearchparticipants• Ihavereadeachpageofthisform• Iauthorizeaccesstomypersonalhealthinformation,medicalrecordandresearchstudydataasexplainedinthisform

• Ihaveagreed,oragreetoallowthepersonIamresponsiblefor,toparticipateinthisresearchstudy_____________________________________________________________________________________________________Nameofparticipant/Substitute Signature Datedecision-maker(print) ASSISTANCE DECLARATION The Assistance Declaration provides a mechanism for potential participants who are unable to read the informed consent form (i.e. illiterate, blind or for who English is their second language) to participate in research studies. Was the participant assisted during the consent process? Yes No

The consent form was read to the participant/substitute decision-maker, and the person signing below attests that the study was accurately explained to, and apparently understood by, the participant/substitute decision-maker.

The person signing below acted as a translator for the participant/substitute decision-maker during the consent process. He/she attests that they have accurately translated the information for the participant/substitute decision-maker, and believe that that participant/substitute decision-maker has understood the information translated.

_____________________________________________________________________________________________________NameofPersonAssisting(Print) Signature Date

137

PersonobtainingconsentBysigningthisform,Iconfirmthat:• Thisstudyanditspurposehasbeenexplainedtotheparticipantnamedabove• Allquestionsaskedbytheparticipanthavebeenanswered• Iwillgiveacopyofthissignedanddateddocumenttotheparticipant_____________________________________________________________________________________________________NameofPersonobtaining Signature Dateconsent(print)StatementofInvestigatorIacknowledgemyresponsibilityforthecareandwell-beingoftheaboveparticipant,torespecttherightsandwishesoftheparticipantasdescribedinthisinformedconsentdocument,andtoconductthisstudyaccordingtoallapplicablelaws,regulationsandguidelinesrelatingtotheethicalandlegalconductofresearch._____________________________________________________________________________________________________NameofInvestigator(print) Signature Date

138

Appendix C: Copyright Acknowledgements Chapter1Section1.2.4DiagnosingSleepApnea:Table1-2:ClassificationofSleepStudyTests

5/22/2019 RightsLink Printable License

https://s100.copyright.com/AppDispatchServlet 1/2

OXFORD UNIVERSITY PRESS LICENSE

TERMS AND CONDITIONS

May 22, 2019

This Agreement between Sunnybrook Health Sciences Centre -- David Colelli ("You") andOxford University Press ("Oxford University Press") consists of your license details and theterms and conditions provided by Oxford University Press and Copyright Clearance Center.

License Number 4594451485836

License date May 22, 2019

Licensed content publisher Oxford University Press

Licensed content publication SLEEP

Licensed content title Portable Recording in the Assessment of Obstructive Sleep Apnea

Licensed content author Ferber, Richard; Millman, Richard

Licensed content date Jun 1, 1994

Type of Use Thesis/Dissertation

Institution name

Title of your work Enhancing Cognition by using Low­cost In­home Portable SleepEquipment (ECLIPSE): A Feasibility Study

Publisher of your work n/a

Expected publication date Nov 2019

Permissions cost 0.00 USD

Value added tax 0.00 USD

Total 0.00 USD

Title Enhancing Cognition by using Low­cost In­home Portable SleepEquipment (ECLIPSE): A Feasibility Study

Institution name n/a

Expected presentation date Nov 2019

Portions Table 1

Requestor Location Sunnybrook Health Sciences Centre 2075 Bayview Ave,

Toronto, ON M4N 3M5

Canada Attn: David Colelli

Publisher Tax ID GB125506730

Total 0.00 USD

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