January 2023 Sleep Case of the Month: An Unexpected EEG Abnormality

David M. Baratz, MD¹

Ken Cooper, RPSGT²

¹Pulmonary Associates

Scottsdale, AZ USA

²Cobre Valley Regional Medical Center

Globe, AZ USA

A 46-year-old woman was referred because of snoring, observed apnea, and daytime hypersomnolence. Her Epworth Sleepiness Scale was 9 out of 24. She was slightly overweight but otherwise her physical examination was normal. An overnight polysomnography was requested but denied by her insurance company.

What should be done at this time? (Click on the correct answer to be directed to the second of six pages)

1. An at home sleep study
2. ENT referral
3. Reassurance
4. 1 and 3
   
5. All of the above

Cite as: Baratz DM, Cooper K. January 2023 Sleep Case of the Month: An Unexpected EEG Abnormality. Southwest J Pulm Crit Care Sleep. 2023;26(1):1-4. doi: https://doi.org/10.13175/swjpccs056-22 PDF

July 2022 Sleep Case of the Month: A Sleepy Scout

Christine S. Fukui MD

Honolulu, HI USA

History of Present Illness:

A 25-year-old African American man complaining of excessive daytime somnolence. He was a US Army Ranger scout who received a traumatic brain injury (TBI) from an improvised explosive device attack in Afghanistan which resulted in a loss of about ¼ of his visual field. He said he slept well at night and there was no history of snoring. There was no history of any parasomnias.

PMH, SH, FH:

Other than the traumatic brain injury there was no significant PMH. His most recent brain scan showed only the remnants of his brain injury which resulted in an intracerebral hemorrhage which was managed conservatively. He was single. He did not smoke and had only moderate alcohol intake. There was no significant FH of sleep apnea.

Physical Examination:

Other than the visual field loss his physical examination was unremarkable.

What should be done next? (Click on the correct answer to be directed to the second of five pages)

1. Brain MRI
2. Electroencephalogram (EEG)
3. PSG (polysomnography) sleep study
4. Repeat CT of head
5. All of the above

Cite as: Fukui CS. July 2022 Sleep Case of the Month: A Sleepy Scout. Southwest J Pulm Crit Care Sleep 2022;25(1):1-3. doi: https://doi.org/10.13175/swjpccs027-22 PDF

Long-term All-Cause Mortality Risk in Obstructive Sleep Apnea Using Hypopneas Defined by a ≥3 Percent Oxygen Desaturation or Arousal

Rohit Budhiraja, MD¹

Stuart F. Quan, MD^1,2

¹Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA

²Arizona Asthma and Airways Research Center, University of Arizona College of Medicine, Tucson, AZ    

Abstract

Study Objectives: Some prior studies have demonstrated an increase in mortality associated with obstructive sleep apnea (OSA) utilizing a definition of OSA that requires a minimum 4% oxygen desaturation to identify a hypopnea. No large community-based studies have determined the risk of long-term mortality with OSA with hypopneas defined by a ≥3% O₂ desaturation or arousal (AHI3%A).

Methods: Data from 5591 Sleep Heart Health Study participants without prevalent cardiovascular disease at baseline who underwent polysomnography were analyzed regarding OSA diagnosed using the AHI3%A criteria and all-cause mortality over a mean follow up period of 10.9±3.2 years.

Results: There were 1050 deaths in this group during the follow-up period. A Kaplan-Meir plot of survival revealed a reduction in survival with increasing AHI severity. Cox proportional hazards regression models revealed significantly increased all-cause mortality risk with increasing AHI, hazard ratio (HR, 95% CI) 1.13 (1.04-1.23), after adjusting for age, sex, race, BMI, cholesterol, HDL, self-reported hypertension and/or diabetes and smoking status. In categorical models, the mortality risk was significantly higher with severe OSA [adjusted HR 1.38 (1.09-1.76)]. When stratified by gender or age, severe OSA was associated with increased risk of death in men [adjusted HR 1.14 (1.01-1.28)] and in those <70 years of age [adjusted HR 1.51 (1.02-2.26)]. In contrast, AHI severity was not associated with increased mortality in women or those ≥70 years of age in fully adjusted models.

Conclusion: Severe AHI3%A OSA is associated with significantly increased mortality risk, especially in men and those <70 years of age.

Introduction

Obstructive sleep apnea (OSA) is a prevalent disorder associated with diverse physiological changes. Intermittent hypoxia-reoxygenation, sympathetic nervous system activation and endothelial dysfunction have been demonstrated in OSA and likely contribute to adverse outcomes including daytime sleepiness, hypertension, coronary artery disease, and stroke (1,2). It is also associated with increased mortality, especially in those with more severe disease (3-7).

The severity of OSA is most frequently categorized using the apnea hypopnea index (AHI). However, the definition of the ‘hypopnea’ component of this index remains a matter of controversy. American Academy of Sleep Medicine (AASM) guidelines recommend that hypopnea be defined as a 30% or greater reduction in the airflow associated with either ≥3% decrease in oxyhemoglobin saturation, or an arousal from sleep (AHI3%A) (8). However, Centers for Medicare and Medicaid Services (CMS), along with several other payors in the United States, utilize an alternate hypopnea definition that requires at least a 4% desaturation and does not recognize arousals for defining hypopnea (AHI4%). The reimbursement for OSA therapy from these payors is reserved for the subset of patients that meets this more stringent definition of OSA. Unfortunately, this policy systematically deprives some patients, even those with clear symptoms attributable to sleep apnea such as increased sleepiness, of appropriate therapy, since they do not meet the higher diagnostic cutoff mandated by this definition.

Much of the current status quo may be related to a lack of substantial data evaluating the impact of hypopnea events associated with less severe desaturation or arousals on diverse OSA outcomes. In contrast, several large cohort studies have established a robust relationship between OSA defined using the AHI4% definition and cardiovascular outcomes (9-11). Two large community-based longitudinal studies demonstrating an association between OSA severity and all-cause mortality, that from Sleep Heart Health Study (SHHS) cohort (3) and that from Wisconsin Sleep Cohort (5), also utilized the AHI4% definition. However, no large community-based longitudinal studies have assessed the association between OSA diagnosed using the AHI3%A definition and mortality. The current study utilized data from SHHS to assess the relationship between OSA defined by the AHI3%A at baseline and all-cause mortality over an 11-year follow up period.

Methods

Participants

The Sleep Heart Health Study (SHHS) was a multicenter cohort study that investigated prospectively the relationship between OSA and cardiovascular diseases in the United States. Details of the rationale and study design have been described elsewhere (12). Recruitment began in 1995 with eventual enrollment of 6,441 participants, 40 years of age and older, from several ongoing “parent” cardiovascular and respiratory disease cohorts who were initially assembled between 1976 and 1995 (13). These “parent” cohorts consisted of the Offspring and the Omni Cohorts of the Framingham Heart Study in Massachusetts; the Hagerstown, MD, and Minneapolis, MN, sites of the Atherosclerosis Risk in Communities Study; the Hagerstown, MD, Pittsburgh, PA, and Sacramento, CA, sites of the Cardiovascular Health Study; 3 hypertension cohorts (Clinic, Worksite, and Menopause) in New York City; the Tucson Epidemiologic Study of Airways Obstructive Diseases and the Health and Environment Study; and the Strong Heart Study of American Indians in Oklahoma, Arizona, North Dakota, and South Dakota. Between 1995 and 1997, these participants underwent a home sleep evaluation that included full unattended polysomnography to determine whether they had OSA. Subsequently, they were followed for mortal events by their parent cohorts. Follow-up duration was 10.9±3.2 years (Mean±SD). As shown in Figure 1, consent was withdrawn by 134 participants from the Arizona cohort of the Strong Heart Study because of sovereignty issues after the end of the follow-up period.

Figure 1. Diagram of Sleep Heart Health Study (SHHS) analytic cohort.

Participants with self-reported prevalent cardiovascular disease (CVD: coronary heart disease, stroke or congestive heart failure) at enrollment also were excluded. Consequently, there were 5,591 participants in the analytic cohort. Parent cohort data were used for documentation of age, height, sex and ethnicity. Co-morbid self-reported diabetes, cardiovascular disease (CVD), concurrent treatment for OSA and smoking status were ascertained from parent cohort data or from responses on health interview and sleep habit questionnaires administered on the evening of the polysomnography home visit (vide infra). Hypertension status was derived as previously described from blood pressure measurements on the night of the home visit and hypertensive medication use (14). Body mass index (BMI) was calculated as weight (kg)/height (m²).

Institutional review boards for human subjects’ research of the respective parent cohorts approved the study. Informed written consent was obtained from all participants at the time of their recruitment.

Polysomnography and Home Visit

Participants underwent overnight in-home polysomnograms using the Compumedics Portable PS-2 System (Abbottsville, Victoria, Australia) administered by trained technicians (15). The home visits were performed by two-person, mixed-sex teams in visits that lasted 1.5 to 2 hours. At the time of the home visit, blood pressure was measured manually in triplicate in a seated position after 5 minutes of rest (16). The average of the second and third measurements was used. Body weight was measured using a digital scale.

The SHHS recording montage for both the initial and follow-up sleep evaluations consisted of electroencephalogram (C4/A1 and C3/A2), right and left electrooculogram, a bipolar submental electromyogram, thoracic and abdominal excursions (inductive plethysmography bands), airflow (detected by a nasal-oral thermocouple [Protec, Woodinville, WA]), oximetry (finger pulse oximetry [Nonin, Minneapolis, MN]), electrocardiogram and heart rate (using a bipolar electrocardiogram lead), body position (using a mercury gauge sensor), and ambient light (on/off, by a light sensor secured to the recording garment). Equipment and sensors were applied and calibrated during the evening home visit by a study certified technician. In the morning, the equipment and the data stored in real time on PCMCIA cards, were retrieved and downloaded to the computers of each respective clinical site. The data were locally reviewed, and then forwarded to a central reading center (Case Western Reserve University, Cleveland, OH). Comprehensive descriptions of polysomnography scoring and quality-assurance procedures have been previously published (15,17). In brief, sleep was scored according to guidelines developed by Rechtschaffen and Kales (18). Strict protocols were maintained to ensure comparability among centers and technicians. Intra-scorer and inter-scorer reliabilities were high (17).

The apnea hypopnea index (AHI) was calculated for each participant using the AASM recommended definition of hypopnea. Thus, hypopneas were identified if the amplitude of a measure of flow or volume (detected by the thermocouple or thorax or abdominal inductance band signals) was reduced discernibly (at least 25% lower than baseline breathing) for at least 10 seconds, did not meet the criteria for apnea and the event was associated with either a ≥3% oxygen desaturation from baseline or terminated with electroencephalographic evidence of an arousal. An apnea was defined as a complete or almost complete cessation of airflow, as measured by the amplitude of the thermocouple signal, lasting at least 10 seconds.

Statistical Analyses

Mean and standard deviation were used to provide an overall description of the data used in the analyses. For analyses using the AHI, each participant’s AHI was assigned to one of 4 OSA severity categories: No OSA (AHI <5 /hour), Mild (AHI ≥5 and <15 /hour), Moderate (AHI ≥15 and < 30/hour) and Severe (AHI ≥30). For some analyses, because values for AHI were extremely left skewed, a natural log transformation was performed to express AHI as a continuous factor in the form of lnAHI+0.1. To nullify the impact of 0 values of the AHI, 0.1 was added to the ln function. Mortality rates were computed by dividing the number of deaths by accumulated person-years at risk.

Analysis of variance was used to test for differences within continuous variables and ² was employed for categorial variables. A Kaplan-Meir plot was computed to assess the overall relationship between severity of OSA and mortality. Cox proportional hazards regression models were calculated to examine the association between AHI as a categorical and continuous factor and mortality. Covariates included in the models were sex, race, age, BMI, cholesterol, high density lipoprotein (HDL), hypertension and/or diabetes and smoking status. Consistent with a previous study assessing mortality in SHHS, age was dichotomized into those <70 and those ≥ 70 years (3). Race was stratified as non-Hispanic White or other. Smoking was recategorized into those who were current or former smokers and those who were never smokers. Prevalent hypertension or self-reported diabetes was expressed as present or absent. Three models were constructed: Model 1 adjusted for age, race and sex, Model 2 adjusted for covariates in Model 1 plus BMI and Model 3 adjusted for covariates in Models 1 and 2 plus cholesterol, HDL, hypertension/diabetes and smoking status.

Analyses were performed using IBM SPSS Statistics v27 (Armonk, NY). The survival package in R was used to obtain the Kaplan Meir plot. A p value of <0.05 was considered statistically significant.

Results

Demographic and clinical characteristics of the cohort stratified by AHI are shown in Table 1.

Table 1. Baseline Characteristics Stratified by Apnea Hypopnea Index^a,b

Age and BMI increased across AHI strata as well as the % of men, current/ex-smokers, diabetic/hypertensives and non-Hispanic Whites. In contrast, HDL decreased. No changes were observed for cholesterol or % receiving OSA treatment.

Figure 2 depicts the Kaplan-Meir plot of survival over ~11 years of follow-up stratified by AHI categories.

Figure 2. Kaplan Meir plot of survival stratified by apnea hypopnea (AHI) severity.

There was a clear reduction in survival with apparent differences related to AHI severity. However, because several covariates also impacted survival across AHI strata, multivariate proportional hazard modelling was employed as shown in for all participants as shown in Table 2.

Table 2. Hazard Ratios (95% confidence intervals) for All-Cause Mortality

There were 1,050 deaths with full covariate data available for analysis. For the categorical modelling, there was an increase in the hazard ratio as the AHI severity increased, but this was only statistically significant at an AHI ≥30 /h (HR: 1.36, 95% CI: 1.09-1.69). Increasing model complexity did not alter this finding. A model using AHI as a continuous factor also demonstrated a significant association between severity of AHI and increasing mortality in a fully adjusted model. A sensitivity analysis where concurrent OSA treatment was included also did not change this relationship.

Because previous analyses have demonstrated differences in mortality between men and women, sex stratified analyses were performed as shown in Table 3.

Table 3. Hazard Ratios (95% confidence intervals) for All-Cause Mortality Stratified by Sex

These findings confirmed that in men AHI severity in both categorical and continuous analyses was associated with increased mortality. As observed in the combined analyses, this was only statistically significant in the continuous analysis (HR: 1.14, 95% CI: 1.01-1.28) although strong trends were noted in the categorical analyses in all models. In women, however, the relationship between AHI severity and mortality was less robust. In demographic (Model 1) and demographic/anthropometric (Model 2) adjusted analyses, an AHI ≥30 /h was associated with increased mortality, but this observation was attenuated and lost statistical significance in the fully adjusted categorical and continuous models.

Table 4 shows age stratified analyses comparing those <70 years to those ≥70 years of age.

Table 4. Hazard Ratios (95% confidence intervals) for All-Cause Mortality Stratified by Age at 70 years

In those who were <70 years, AHI severity was strongly associated with increased mortality. Although this finding was statistically significant only at AHI ≥30 /h in the fully adjusted model, it was significant at AHI 15-29.9/h in less complex models (HR: 1.45, 95% CI: 1.03-2.04) and approached significance in the fully adjusted model (HR: 1.41, 95% CI: 0.98-2.00). In contrast, AHI severity was not found to be associated with increased mortality among those ≥70 years of age in either categorial or continuous models.

Of the 1,050 deaths used in the proportional hazard models, 258 (24.7%) were classified as related to CVD. In analyses restricted to CVD deaths, a Kaplan-Meir plot (not shown) indicated a reduction in survival with increasing OSA severity (Log Rank ² = 11.2-20.4 for comparisons vs. AHI <5 /h, p<.001). However, in fully adjusted proportional hazard models, no differences in survival attributable to OSA were observed.

Discussion

The current study demonstrated using the AHI3%A definition of hypopnea, a significant association between increasing severity of AHI and all-cause mortality in a model adjusted for relevant anthropometric and demographic factors and clinical co-morbidities. In stratified analyses, this association was more robust among men than in women, and those below 70 years of age compared to the older subjects.

Notably, some earlier studies have demonstrated an increase in mortality associated with OSA. An 18-year follow-up from Wisconsin cohort revealed a significantly increased hazard ratio for all-cause mortality and cardiovascular mortality in severe OSA (5). Punjabi et al. (3) used data from SHHS and demonstrated an increase in all-cause mortality with severe OSA, particularly in men aged 40–70, during an average follow-up period of 8.2 years. Both these studies utilized the AHI4% criteria for OSA diagnosis. Similarly, Martínez-García (19) utilized AHI4% criteria in a clinic population of 939 elderly (median follow-up, 69 months) and found HR of 2.25 for cardiovascular mortality in the untreated severe OSA group. A study from Denmark included 22,135 OSA patients found that male gender, age>40 years, diabetes (types 1 and 2), hypertension, and heart failure were associated with greater mortality (criteria for hypopnea not specified (6). Marin et al. (10) also noted increased fatal and non-fatal cardiovascular events in men with untreated severe OSA diagnosed using the AHI4% criteria during a mean 10.1 years follow-up period. A meta-analysis with 11,932 patients from 6 prospective observational studies found severe OSA to be a strong independent predictor for cardiovascular and all-cause mortality (4). Finally, a meta-analysis of 27 cohort studies included 3,162,083 participants showed higher all-cause mortality in severe OSA and lower mortality in CPAP-treated than in untreated patients (7). Virtually all of these aforementioned studies utilized a definition of OSA requiring a minimum 4% oxygen desaturation to identify a hypopnea.

To our knowledge, our study is the first large community-based study to assess the association between OSA diagnosed using the AHI3%A criteria and mortality. Severe OSA was associated with a higher mortality, especially in those <70 years of age, and in men. Consistent with our findings, an earlier study in a clinical population of over 10,000 adults observed OSA diagnosed utilizing AHI3%A criteria predicted incident sudden cardiac death (20). The higher mortality risk in men and in younger people is similar to that reported in other analyses from this database using AHI4% criteria (3,21). Our results provide evidence that the more liberal AHI3%A criteria is associated with increased all-cause mortality thus providing further justification for its use in identifying persons with OSA who may benefit from treatment.

We observed that approximately 25% of the deaths in our analytic cohort were attributable to CVD. Data from the Wisconsin Sleep Cohort indicate that excess mortality associated with OSA over a 18 year follow-up is partially related to CVD (5). Our unadjusted analyses are consistent with this observation. However, our study did not have sufficient power in adjusted models to replicate it.

There are several factors that could explain the association between OSA and increased mortality. OSA increases the risk for hypertension, cardiovascular disease, diabetes, and stroke and can, thus, increase mortality. Hypoxemic burden has been suggested to be a conspicuous factor conferring an increased mortality risk (22). Other factors, however, may also play a notable role. Analyses from 5,712 participants revealed that short respiratory event duration, a marker for low arousal threshold, was associated with higher mortality risk (21). The authors hypothesized that the shorter event duration reflected greater “arousability”, resulting in greater sleep fragmentation, shorter sleep, and excess sympathetic tone, and hence increased mortality. Arousals are associated with an increase in the sympathetic activity and a decrease in the parasympathetic activity and data support their role in the development of hypertension.

From a clinical perspective, utilizing the AHI4% criteria in lieu of AHI3%A to identify persons as having OSA impacts those who are classified as having OSA by the latter standard, but not the former. Using the SHHS database, we found that 36.1% of individuals fall into this category. Importantly, similar to persons who were classified as having OSA by both criteria, we observed that this group who were designated as having OSA by only AHI3%A criteria had increased rates of prevalent and incident hypertension (23,24). There also was a significant association with CVD (25). Combined with these previous studies, the current analyses demonstrating increased mortality associated with OSA defined by AHI3%A criteria provide evidence that use of this more liberal definition will benefit patients.

This study has several strengths. SHHS is large, ethnically diverse cohort, making the results more generalizable. The cohorts were community-based, obviating any referral bias. Polysomnography, the gold standard diagnostic test for OSA, was performed on all individuals. The substantive database allowed controlling for multiple confounders. Finally, the participants were followed for an ample time with the average follow-up period of 11 years.

The study also has some limitations. First, being a community derived cohort, the severity of OSA seen in SHHS was generally mild to moderate. The outcomes, including mortality, would be expected to be worse in a clinical cohort with higher severity of sleep apnea. Secondly, while the current study included a substantial number of potential covariates in the models, residual confounding from other factors may have occurred. Thirdly, the severity of OSA may have changed over the follow up period. Fourthly, while the follow-up period of the study was long, it is possible that an even longer follow-up period may have allowed a better estimate of the long-term impact of OSA on mortality. Finally, although the study demonstrated increased mortality risk, elucidation of the mechanisms thereof was beyond the scope of this study.

In conclusion, the current study demonstrated in a large community-based cohort that even OSA defined by a more liberal AHI3%A is associated with increased mortality. Considering the adverse outcomes associated with OSA, a restrictive definition that excludes these persons from warranted OSA therapy is potentially deleterious to overall health with significant individual and healthcare implications.

References

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Abbreviations

• AASM            American Academy of Sleep Medicine
• AHI               Apnea hypopnea index
• AHI3%A        Apnea hypopnea index defined using a hypopnea definition requiring a minimum 3% O2 desaturation or arousal
• AHI4%          Apnea hypopnea index defined using a hypopnea definition requiring a minimum 4% O2 desaturation
• BMI              Body mass index
• CMS             Centers for Medicare and Medicaid Services
• CVD             Cardiovascular disease
• HDL              High density lipoprotein
• HR               Hazard ratio
• OSA             Obstructive sleep apnea
• SHHS           Sleep Heart Health Study

Acknowledgements

The Sleep Heart Health Study was supported by National Heart, Lung and Blood Institute cooperative agreements U01HL53940 (University of Washington), U01HL53941 (Boston University), U01HL53938 (University of Arizona), U01HL53916 (University of California, Davis), U01HL53934 (University of Minnesota), U01HL53931 (New York University), U01HL53937 and U01HL64360 (Johns Hopkins University), U01HL63463 (Case Western Reserve University), and U01HL63429 (Missouri Breaks Research). A list of SHHS investigators, staff and their participating institutions is available on the SHHS website, http://jhuccs1.us/shhs/details/investigators.htm.

Cite as: Budhiraja R, Quan SF. Long-term all-cause mortality risk in obstructive sleep apnea using hypopneas defined by a ≥3 percent oxygen desaturation or arousal. Southwest J Pulm Crit Care. 2021;23(1):23-35. doi: https://doi.org/10.13175/swjpcc025-21 PDF

The Association Between Obstructive Sleep Apnea Defined by 3 Percent Oxygen Desaturation or Arousal Definition and Self-Reported Cardiovascular Disease in the Sleep Heart Health Study

Stuart F. Quan, M.D.^1,2

Rohit Budhiraja, M.D.¹

Sogol Javaheri, M.A., M.D., M.P.H.¹

Sairam Parthasarathy, M.D.²

Richard B. Berry, M.D.³

¹Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA; ²Department of Medicine, University of Arizona College of Medicine, Tucson, AZ; ³Division of Pulmonary, Critical Care, and Sleep Medicine, University of Florida, Gainesville, FL

Editor's Note: Click here to see an accompanying editorial.

Abstract

Background: Studies have established that OSA defined using a hypopnea definition requiring a >4% oxygen desaturation (AHI4%) is associated with cardiovascular (CVD) or coronary heart (CHD) disease. This study determined whether OSA defined using a hypopnea definition characterized by a >3% oxygen desaturation or an arousal (AHI3%A) is associated with CVD/CHD.

Methods: Data were analyzed from 6307 Sleep Heart Health Study participants who had polysomnography. Self-reported CVD included angina, heart attack, heart failure, stroke or previous coronary bypass surgery or angioplasty. Self-reported CHD included the aforementioned conditions but not stroke or heart failure. The association between OSA and CVD/CHD was examined using logistic regression models with stepwise inclusion of demographic, anthropometric, social/behavioral and co-morbid medical conditions. A parsimonious model in which diabetes and hypertension were excluded because of their potential to be on the causal pathway between OSA and CVD/CHD also was constructed.

Results: For CVD, the odds ratios and 95% confidence intervals for AHI3%A >30/hour were 1.39 (1.03-1.87) and 1.45 (1.09-1.94) in the fully adjusted and parsimonious models. Results for CHD were 1.29 (0.96-1.74) and 1.36 (0.99-1.85). In participants without OSA according to more stringent AHI4% criteria but with OSA using the AHI3%A definition, similar findings were observed.

Conclusion: OSA defined using an AHI3%A is associated with both CVD and CHD. Use of a more restrictive AHI4% definition will misidentify a large number of individuals with OSA who have CVD or CHD. These individuals may be denied access to therapy, potentially worsening their underlying CVD or CHD. 

Introduction

Obstructive sleep apnea (OSA) is a common disorder characterized by recurrent episodes of either complete upper airway collapse (apneas) or partial collapse (hypopneas) during sleep. A number of large studies have established that OSA is a risk factor for the development of hypertension and cardiovascular disease (CVD) as well as higher mortality; individuals with more severe OSA are at greater risk (1-3). The most commonly used metric of OSA severity is the apnea hypopnea index (AHI). However, there is controversy regarding the definition of the AHI. In 2012, the American Academy of Sleep Medicine (AASM) recommended that the hypopnea definition include any decrease in airflow by at least 30% from the baseline with an oxyhemoglobin desaturation of at least 3%, or an arousal from sleep (4). However, several payors including the Centers for Medicare and Medicaid Services (CMS) continue to require a more stringent hypopnea definition necessitating a 4% or greater decrease in oxygen saturation (5) despite evidence documenting a relationship between the AASM recommended standard and daytime sleepiness (6). The resistance to universal acceptance of the AASM criteria is based in part on the lack of evidence that 3% desaturations or arousals have an adverse cardiovascular impact. This reluctance to adopt a more inclusive definition of sleep apnea has restricted access to OSA treatment for many patients (7). Therefore, determining if there is relationship between OSA characterized by at least 3% drop in saturation or an arousal from sleep and CVD may assist in identification of persons at risk for CVD, allow greater access to care and potentially improve other health-related outcomes.

Using the database from the Sleep Heart Health Study, a large well-characterized community based cohort that had undergone polysomnography, the current study aimed to determine the association between the AASM recommended definition of the AHI which incorporates hypopneas with at least a 3% desaturation or an arousal (AHI3%A) and self-reported CVD and coronary heart disease (CHD) in middle-aged and older adults. In addition, we sought to ascertain whether there was an association between CVD or CHD and OSA severity among individuals who were not identified as having OSA using the more restrictive standard of requiring at least a 4% oxygen desaturation irrespective of an arousal (AHI4%), but were classified as having OSA by the AHI3%A definition. We hypothesized that increasing OSA severity represented by the AHI3%A would be associated with a greater likelihood of having prevalent CVD or CHD, and that persons who were not identified as having OSA using the AHI4% criteria would have a higher likelihood as well.

Methods

This study analyzed data obtained from the Sleep Heart Health Study (SHHS) which was a prospective multicenter cohort study designed to investigate the relationship between OSA and cardiovascular diseases in the United States. The study’s rationale and design have been published elsewhere (8). Briefly, 6,441 subjects, 40 years of age and older were recruited starting in 1995 from several ongoing “parent” cardiovascular and respiratory disease cohorts that were initially assembled between 1976 and 1995 (9). These cohorts included the Offspring Cohort and the Omni Cohort of the Framingham Heart Study in Massachusetts; the Hagerstown, MD, and Minneapolis, MN, sites of the Atherosclerosis Risk in Communities Study; the Hagerstown, MD, Pittsburgh, PA, and Sacramento, CA, sites of the Cardiovascular Health Study; 3 hypertension cohorts (Clinic, Worksite, and Menopause) in New York City; the Tucson Epidemiologic Study of Airways Obstructive Diseases and the Health and Environment Study; and the Strong Heart Study of American Indians in Oklahoma, Arizona, North Dakota, and South Dakota. Because of sovereignty issues, 134 participants from the Arizona cohort of the Strong Heart Study withdrew consent. Analyses were performed on the remaining 6307 participants. The SHHS was approved by each site’s institutional review board for human subjects’ research, and informed written consent was obtained from all subjects at the time of their enrollment.

Polysomnography and Home Visit

Participants underwent overnight in-home polysomnograms using the Compumedics Portable PS-2 System (Abbottsville, Victoria, Australia) administered by trained technicians (10). The home visits were performed by two-person, mixed-sex teams in visits that lasted 1.5 to 2 hours. Participants were asked to schedule the visit so that it would occur approximately two hours prior to their usual bedtime. At the time of the home visit, an inventory of each participant’s medications was made. In addition, a health interview was completed that ascertained the presence of several health conditions. Questionnaires that were completed included the SHHS Sleep Habits Questionnaire which incorporated the Epworth Sleepiness Scale (ESS) (11) and the Medical Outcomes Study SF-36 (12). Blood pressure was measured manually in triplicate in a seated position after 5 minutes of rest (13). The average of the second and third measurements was used for this analysis. Body weight was obtained using a digital scale.

The SHHS recording montage consisted of electroencephalogram (C4/A1 and C3/A2), right and left electrooculogram, a bipolar submental electromyogram, thoracic and abdominal excursions (inductive plethysmography bands), airflow (detected by a nasal-oral thermocouple (Protec, Woodinville, WA), oximetry (finger pulse oximetry [Nonin, Minneapolis, MN]), electrocardiogram and heart rate (using a bipolar electrocardiogram lead), body position (using a mercury gauge sensor), and ambient light (on/off, by a light sensor secured to the recording garment). Sensors were placed, and equipment was calibrated during an evening home visit by a certified technician. After technicians retrieved the equipment, the data, stored in real time on PCMCIA cards, were downloaded to the computers of each respective clinical site, locally reviewed, and forwarded to a central reading center (Case Western Reserve University, Cleveland, OH). Comprehensive descriptions of polysomnography scoring and quality-assurance procedures have been previously published (14). In brief, sleep was scored according to guidelines developed by Rechtschaffen and Kales (15). Strict protocols were maintained to ensure comparability among centers and technicians. Intra-scorer and inter-scorer reliabilities were high (14).

The apnea hypopnea index (AHI) was calculated for each participant using two definitions of hypopnea, the AASM recommended definition [AHI3%A] and the AASM acceptable [CMS] definition [AHI4%]. For AHI3%A, hypopneas were identified if the amplitude of a measure of flow or volume (detected by the thermocouple or thorax or abdominal inductance band signals) was reduced discernibly (at least 30% lower than baseline breathing) for at least 10 seconds, did not meet the criteria for apnea and the event was associated with either a 3% oxygen desaturation from baseline or terminated with electroencephalographic evidence of an arousal. For AHI4%, hypopneas were identified if the aforementioned reduction in flow or volume occurred and the event was associated with a 4% oxygen desaturation from baseline. In both cases, an apnea was defined as a complete or almost complete cessation of airflow, as measured by the amplitude of the thermocouple signal, lasting at least 10 seconds.

Outcome Assessment

Self-reported CVD and CHD were the outcomes of interest for this analysis and were obtained from the standardized health interview performed at the time of each participant’s polysomnography home visit. Participants were asked if they had ever been told by a doctor that she or he had angina, heart attack, heart failure, or stroke and if the participant had ever undergone coronary bypass surgery or coronary angioplasty. Prevalent CVD was defined as a positive response to one or more of the aforementioned conditions or procedures. Prevalent CHD was defined as an affirmative response to the same questions with the exclusion of responses to the presence of heart failure or stroke.

Covariates

Selection of potential covariates was based on previous studies documenting an association with either CVD or CHD. These included various demographic (e.g., sex, race/ethnicity, education, marital status), anthropometric (e.g., height, weight and blood pressure [BP]), social/behavioral (e.g., smoking history, alcohol use, sleep duration, quality of life) indices as well as plasma lipids (cholesterol, high density lipoprotein [HDL], triglycerides), several diseases (depression, hypertension, diabetes) and spirometry.

The following definitions were used for those covariates that were not primarily recorded. Body mass index (BMI) was calculated as weight (kg)/height (m²). The ankle arm index (AAI) was computed as the ratio of blood pressure at the ankle to that in the arm. Waist to hip ratio was the waist divided by hip circumferences. Hypertension was defined as a self-report of hypertension or the use of anti-hypertensive medications. Diabetes was considered present if it was self-reported by the participant or if there was use of oral hypoglycemic agents or insulin. Depression was defined as present if the participant indicated on the SF-36 that he/she was feeling “blue” or “down” for at least “a good bit of the time” for the previous 4 weeks, or he/she was using antidepressant medications. Insomnia was defined as often or almost always having “trouble falling asleep”, “waking up during the middle of the night and having difficulty getting back to sleep” or “waking up too early in the morning and being unable to get back to sleep”. Sleepiness was assessed by the ESS as well as by self-report of being excessively sleepy during the day most or almost all of the time.

Statistical Analyses

Mean and standard deviation, and percentages were used to provide an overall description of the data used in the analyses. Unadjusted differences were compared using Student’s t test or c². For both definitions of the AHI, each participant’s AHI was assigned to one of 4 OSA severity categories: Normal (AHI <5 /hour), Mild (AHI ≥5 and <15 /hour), Moderate (AHI ≥15 and < 30/hour) and Severe (AHI ≥30/hour).

Missing data was present in 4.8% of observations and were felt to be missing at random. Inasmuch as using a “complete case analysis” would result in exclusion of a significant number of participants from our analyses with a consequent reduction in statistical power, multiple imputation using the multiple imputation by chained equation (MICE) package in R was employed to generate replacement values. Comparison of the imputed to the original dataset did not identify any outliers in the imputed dataset and means of the same variables between datasets were comparable.

To reduce the number of relevant predictors, overfitting of models, reduce potential collinearity and minimize prediction error, a Least Absolute Shrinkage and Selection Operator (lasso) regression was performed for both outcome variables using the glmnet package in R. This resulted in an analytic dataset for CVD that consisted of the following: age, sex, race/ethnicity, BMI, AAI, diastolic BP, smoking, SF-36 physical component summary (PCS), SF-36 general health rating (GenHlth), SF-36 ability to perform vigorous activity (VigActiv), hypertension, diabetes, depression and HDL. For CHD, the analytic dataset consisted of the following: age, sex, race/ethnicity, BMI, diastolic BP, smoking, PCS, GenHlth, VigActiv, hypertension, diabetes, triglycerides and HDL.

For the entire cohort, logistic regression using SPSS v27 (Armonk, NY) was used to generate increasingly complex models of the relationship between either CVD or CHD and severity of OSA adjusting for the covariates identified using the lasso regression. For CVD, after the unadjusted model, models were generated for the sequential addition of demographic factors (age, sex, race/ethnicity), anthropometric factors (BMI, AAI, diastolic BP), social/behavioral characteristics (smoking, PCS, GenHlth, VigActiv) and diseases/conditions (hypertension, diabetes, depression, HDL). For CHD after the unadjusted model, the corresponding sequential models were demographic factors (age, sex, race/ethnicity), anthropometric factors (BMI, diastolic BP), social/behavioral characteristics (smoking, PCS, GenHlth, VigActiv) and diseases/conditions (hypertension, diabetes, triglycerides, HDL). Because of the possibility that adjustment for a hypertension and a diabetes indicator would be “overadjustment” (i.e., adjustment for a variable on a causal pathway), we excluded both hypertension and diabetes from the final set of covariates in additional analyses and these are referred to as “parsimonious models.” Lastly, sensitivity analyses were performed in which the natural log of AHI3%A was used instead of categorial levels of that factor in the above models.

Associations between both CVD and CHD, and OSA severity were further analyzed in the subgroup of participants who were not classified as having OSA based on AHI4% criteria but were classified as OSA using AHI3%A criteria. The moderate and severe categories were combined because of the small number of cases in the severe OSA category. Otherwise, the modelling approaches employed were identical.

In Tables 2-5, odds ratios and 95% CI are presented versus the reference level of AHI <5 /hour. P values refer to the overall significance of the model with respect to OSA severity. Odds ratios, 95% CI and P values in Table 6 refer to AHI3%A expressed as the continuous factor lnAHI3%A+0.1 (0.1 added to mitigate 0 values of lnAHI3%A).

Results

Table 1 shows the univariate association of potential risk factors or characteristics with the presence of CVD or CHD.

Table 1. Univariate Association of Various Characteristics to Prevalent Cardiovascular (CVD) and Coronary Heart Disease (CHD)

N=6307 for all characteristics except AHI 3%/A (N=6131)

^ap≤0.05; ^bp≤0.01; ^cp≤0.001

There were 962 cases (15%) of CVD and 797 (13%) cases of CHD identified. Except for total cholesterol, all were either more prevalent or significantly higher or lower in participants with CVD or CHD. For both CVD and CHD, markedly higher prevalence rates were noted for sex (higher in men), hypertension, diabetes, depression, smoking (higher in ever smokers) and ability to engage in vigorous activity. Conversely, white race and good health status were much less common among those with CVD or CHD. Differences observed for the remaining characteristics were of lesser magnitude.

Figure 1 shows the prevalence rates of CVD or CHD as a function of OSA severity using the AHI3%A criteria. Both conditions were associated with increasing higher rates of disease as OSA became more severe.

Figure 1. Percentage of participants with either cardiovascular (CVD) or coronary heart (CHD) disease according to increasing severity of obstructive sleep apnea defined using a hypopnea definition characterized by a minimum 3% oxygen desaturation or an arousal (AHI3%A)

Table 2 shows the crude and adjusted odds ratios and their 95% confidence intervals for increasing complex models of the relationship between CVD and AHI3%A. The unadjusted model showed a strong, progressive association with increasingly severe OSA. However, as the models became increasingly complex, this relationship was attenuated and only approached statistical significance in the fully adjusted model (+Medical Conditions). Removal of hypertension and diabetes to create the Parsimonious model restored some of the association with a return of statistical significance.

Table 2. Adjusted Relative Odds (95% Confidence Interval) of Self-Reported Prevalent Cardiovascular Disease According to 3% or Arousal Apnea Hypopnea Index Severity Categories

^aDemographics model adds age, sex, race (White vs. American Indian)

^bAnthropometrics model adds BMI, Ankle Arm Index, diastolic blood pressure

^cSocial/Behavioral Factors model adds smoking, SF36 Physical Component Summary, SF36 General Health, SF36 Vigorous Activity

^dMedical Conditions model adds hypertension, diabetes, depression and HDL

^eParsimonious model includes factors in previous models, but removes hypertension and diabetes

^fN=6307

Presented in Table 3 are the models demonstrating the relationship between CHD and AHI3%A.

Table 3. Adjusted Relative Odds (95% Confidence Interval) of Self-Reported Prevalent Coronary Heart Disease According to 3% or Arousal Apnea Hypopnea Index Severity Categories^f

^aDemographics model adds age, sex, race (White vs. American Indian)

^bAnthropometrics model adds BMI, diastolic blood pressure

^cSocial/Behavioral Factors model adds smoking, SF36 Physical Component Summary, SF36 General Health, SF36 Vigorous Activity

^dMedical Conditions model adds hypertension, diabetes, triglycerides and HDL

^eParsimonious model includes factors in previous models, but removes hypertension and diabetes

^fN=6307

Similar to the findings for CVD, there was a progressively higher odds of having CHD as severity of OSA increased. The fully adjusted model (+Medical Conditions) was not significant, but the Parsimonious model approached statistical significance.

There were 3,326 participants who did not have OSA as defined by AHI4% criteria. Within this cohort, 2247 were classified as OSA using the AHI3%A criteria; 1966 (87.4%) were mild, 271 (12.0%) were moderate and 10 (0.4%) were severe. For this subgroup, Table 4 presents the increasingly complex models illustrating the relationship between the presence of CVD and increasing OSA severity.

Table 4. Adjusted Relative Odds (95% Confidence Interval) of Self-Reported Prevalent Cardiovascular Disease According to 3% or Arousal Apnea Hypopnea Index Severity Categories in Participants Without Obstructive Sleep Apnea According to 4% Desaturation Criteria^f

^aDemographics model adds age, sex, race (White vs. American Indian)

^bAnthropometrics model adds BMI, Ankle Arm Index, diastolic blood pressure

^cSocial/Behavioral Factors model adds smoking, SF36 Physical Component Summary, SF36 General Health, SF36 Vigorous Activity

^dMedical Conditions model adds hypertension, diabetes, depression and HDL

^eParsimonious model includes factors in previous models, but removes hypertension and diabetes

^fN=3326

Because of the relatively small number of cases with severe OSA, the moderate and severe cases were combined for these analyses. The unadjusted model showed a strong relationship with OSA severity; as model complexity increased, this finding was attenuated and only approached statistical significance in both the fully adjusted (+Medical Conditions) and Parsimonious models. As demonstrated in Table 5, similar findings were observed for CHD; the unadjusted model showed a strong association which was attenuated as the models became more complex; the fully adjusted (+medical conditions) and parsimonious models approached statistical significance.

Table 5. Adjusted Relative Odds (95% Confidence Interval) of Self-Reported Prevalent Coronary Heart Disease According to 3% or Arousal Apnea Hypopnea Index Severity Categories in Participants Without Obstructive Sleep Apnea According to 4% Desaturation Criterion^f

^aDemographics model adds age, sex, race (White vs. American Indian)

^bAnthropometrics model adds BMI, diastolic blood pressure

^cSocial/Behavioral Factors model adds smoking, SF36 Physical Component Summary, SF36 General Health, SF36 Vigorous Activity

^dMedical Conditions model adds hypertension, diabetes, triglycerides and HDL

^eParsimonious model includes factors in previous models, but removes hypertension and diabetes

^fN=3326

In sensitivity analyses, the natural log of AHI3%A was used as the index of OSA severity in lieu of a categorial representation. As shown in Table 6, in the entire cohort, for both CVD and CHD, a significant linear relationship with increasing severity of OSA was demonstrated in parsimonious models, but not the fully adjusted models. In the subgroup who did not have OSA as defined by AHI4% criteria but did have OSA using the AHI3%A criteria, linear relationships noted for both CVD and CHD in the fully adjusted and parsimonious models. For CHD in the fully adjusted model, the relationship was statistically significant and approached statistical significance in the others.

Table 6. Linear Adjusted Relative Odds (95% Confidence Interval) of Self-Reported Prevalent Cardiovascular and Coronary Heart Disease According to 3% or Arousal Apnea Hypopnea Index Severity

^aCohort restricted participants without OSA according to AHI4% criteria, N=3326

^bCovariates for CVD: age, sex, race, BMI, ankle-arm index, diastolic blood pressure

smoking, SF36 Physical Component Summary, SF36 General Health, SF36 Vigorous Activity

hypertension, diabetes, depression and HDL; Covariates for CHD: age, sex, race, BMI, diastolic blood pressure,smoking, SF36 Physical Component Summary, SF36 General Health, SF36 Vigorous Activity, hypertension, diabetes, triglycerides and HDL

^cExcludes diabetes and hypertension from fully adjusted model

Discussion

In this large community-based study, we demonstrated that OSA defined by apneas and hypopneas characterized by 3% desaturation events or arousals is associated with an increased likelihood of self-reported CVD and CHD after controlling for a number of relevant covariates. Importantly, in a subset of this cohort who did not have OSA as defined by apneas and hypopneas requiring a minimum 4% oxygen desaturation but did have OSA using the 3% desaturation or arousal criteria, we found that the association with both CVD and CHD remained, albeit weaker. Nevertheless, our analyses overall suggest that the regulatory requirement by the Centers for Medicare and Medicaid Services (CMS) in the United States of using a 4% desaturation definition to identify patients with OSA denies a substantial proportion of these individuals the opportunity to be treated for their OSA and thus reduce the risk of worsening or recurrence of their CVD or CHD.

Results from several large cohort studies including SHHS have found that OSA is associated with the presence of CVD and CHD, and that this association is stronger when the AHI as a metric of OSA severity increases (1, 2). These previous studies have used a definition of hypopnea that requires a minimum 4% oxygen desaturation (16-18). This definition has been adopted by CMS and other insurers to identify individuals as having OSA (5). However, the AASM recommends defining hypopneas with a minimum 3% desaturation or an arousal (4). This is based on evidence indicating that daytime sleepiness and other symptoms of OSA are associated with this less stringent definition of OSA (6). This distinction has important clinical implications because there are a large number of patients who do not meet the AHI4% criteria and but do meet the AHI3%A criteria (7, 19). In the former case, they are not considered to have OSA, but do have it in the latter.

To our knowledge, our study is the first to assess the association between OSA using the AHI3%A criteria and CVD and CHD. We found that as OSA severity increased, there was a greater likelihood of having CVD and CHD after adjusting for a number of relevant covariates. We acknowledge that in the fully adjusted model, this association only approached statistical significance. However, in our parsimonious model which removed the presence of hypertension and diabetes, likely mediators of this relationship, the association was strengthened. Sensitivity analyses using the natural log of AHI3%A validated the results we observed with categories of AHI severity. It has been well-established that hypertension and diabetes are independent risk factors for the development of CVD and CHD. However, a number of studies have demonstrated that OSA is a risk factor for the development of both hypertension and diabetes (1, 20). Therefore, both of the latter conditions lie on the causal path by which OSA may increase the risk for the development of CVD and CHD. Hence, we believe that inclusion of both these conditions in our fully adjusted model may be over-adjustment and that our parsimonious model best represents the association between OSA defined by AHI3%A and CVD or CHD.

We identified there was a large subset of our cohort that had OSA using the AHI3%A definition, but not the AHI4% definition of hypopnea. In this subset, we also observed an association between OSA severity and both CVD and CHD. This finding is analogous to the relationship we recently observed between OSA and the prevalence of hypertension (19). Similar to our findings with the full cohort, the fully adjusted model for both CVD and CHD was not statistically significant. However, it approached or became statistically significant in the parsimonious models. Although most of these cases were in the mild OSA category, 12.4% were moderate to severe where treatment is almost always recommended. Individuals with prevalent CVD or CHD and OSA are at risk for further complications of their CVD or CHD (21-23). However, if they do not meet the AHI4% definition of OSA, access to OSA treatment would be denied by CMS and some insurers.

Our findings with respect to CVD and CHD are consistent with recent analyses demonstrating that OSA defined by AHI3%A is associated with prevalent and incident hypertension in the SHHS cohort (19, 24). Similar findings also have been observed in other cohorts providing additional evidence that use of a hypopnea definition incorporating a minimum 3% oxygen desaturation or an arousal is important in the identification of individuals with OSA (25-27).

Although there is substantial evidence emerging that intermittent hypoxemia plays an important role in the cardiovascular consequences of OSA (28), the importance of arousals remains uncertain (29). Arousals involve an increase in the sympathetic activity and a decrease in the parasympathetic activity (28) and there is some evidence linking them in the development of hypertension (30). Data from our study would suggest that they may contribute to the development of CVD or CHD as well.

Some (31, 32), but not all (33) studies have suggested that the impact of OSA on the development of CVD or CHD is in part enhanced by the presence of sleepiness. However, in our initial assessment of potential covariates using a lasso regression, sleepiness did not emerge as a significant factor. Thus, our findings do not support the contention that sleepiness is an important factor impacting the relationship between OSA and CVD or CHD.

Our study does have a few limitations. Most important is that we identified prevalent CVD and CHD by self-report. While it is possible that some misclassification occurred, we do not think it was large. A large number of potential covariates were considered for inclusion in the models; we used a lasso regression to reduce the possibility of over-adjustment and collinearity. Furthermore, the possibility of residual confounding remains. Finally, this is a cross-sectional analysis, and causality cannot be assumed.

This study has several strengths. It uses a large, well characterized cohort with the availability of data from a number of potential covariates. Additionally, the cohort had a diverse racial/ethnic, age and sex distribution. Polysomnography was used to document the presence of OSA, and not more limited sleep apnea testing.

In summary, OSA as defined by apneas and hypopneas requiring a minimum 3% oxygen desaturation or arousal is associated with an increased likelihood of having CVD or CHD. Use of a more restrictive definition requiring a minimum 4% desaturation will misidentify a large number of individuals with OSA, and CVD or CHD. These individuals may be denied access to therapy which may prevent worsening of their underlying CVD or CHD. 

Acknowledgements

SHHS acknowledges the Atherosclerosis Risk in Communities Study, the Cardiovascular Health Study, the Framingham Heart Study, the Cornell/Mt. Sinai Worksite and Hypertension Studies, the Strong Heart Study, the Tucson Epidemiologic Study of Airways Obstructive Diseases (TESAOD), and the Tucson Health and Environment Study for allowing their cohort members to be part of the SHHS and for sharing such data for the purposes of this study. SHHS is particularly grateful to the members of these cohorts who agreed to participate in SHHS as well. SHHS further recognizes all the investigators and staff who have contributed to its success. A list of SHHS investigators, staff, and their participating institutions is available on the SHHS website (www.jhsph.edu/shhs).

The opinions expressed in this paper are those of the authors and do not necessarily reflect the views of the Indian Health Service.

This work was supported by National Heart, Lung and Blood Institute cooperative agreements U01HL53940 (University of Washington), U01HL53941 (Boston University), U01HL53938 (University of Arizona), U01HL53916 (University of California, Davis), U01HL53934 (University of Minnesota), U01HL53931 (New York University), U01HL53937 and U01HL64360 (Johns Hopkins University), U01HL63463 (Case Western Reserve University), U01HL63429 (Missouri Breaks Research).

SP was supported by Patient Centered Outcomes Research Institute (CER-2018C2-13262; PCS-1504-30430; DI-2018C2-13161; DI-2018C2-13161 COVID supplement, EADI-16493), NIH (HL126140, HL151254, AI135108, AG059202, HL158253) and American Academy of Sleep Medicine Foundation during the writing of this manuscript.

Authors’ Declarations: Dr. Budhiraja reports no conflicts of interest or grant funding. Dr. Quan reports research funding from the National Institutes of Health, serves as a consultant to Jazz Pharmaceuticals, Whispersom and is a committee chair and hypopnea taskforce member for the American Academy of Sleep Medicine. Dr. Javaheri serves as a consultant for Jazz Pharmaceuticals and Harmony Biosciences. Dr. Berry reports research funding from Philips Respironics, Res Med and the University of Florida Foundation. Dr. Parthasarathy reports grants from NIH/NHLBI as PI (HL138377, HL126140; IPA-014264-00001; HL095799) or site PI (HL128954; UG3HL140144), grants from Patient Centered Outcomes Research Institute as PI (IHS-1306-02505; EAIN-3394-UOA) or site-investigator (PCS-1504-30430), grants from US Department of Defense as co-investigator (W81XWH-14-1-0570), grants from NIH/NCI as co-investigator (R21CA184920) and NIH/NIMHD as co-investigator (MD011600), grants from Johrei Institute, personal fees from American Academy of Sleep Medicine, non-financial support from National Center for Sleep Disorders Research of the NIH (NHLBI), personal fees from UpToDate Inc., grants from Younes Sleep Technologies, Ltd., personal fees from Vapotherm, Inc., personal fees from Merck, Inc., grants from Philips-Respironics, Inc., personal fees from Philips-Respironics, Inc., personal fees from Bayer, Inc., personal fees from Nightbalance, Inc, personal fees from Merck, Inc, grants from American Academy of Sleep Medicine Foundation (169-SR-17); In addition, Dr. Parthasarathy has a patent UA 14-018 U.S.S.N. 61/884,654; PTAS 502570970 (Home breathing device) issued.

A preprint of this paper is available at: medRxiv, https://doi.org/10.1101/2020.09.22.20199745

Abbreviation List

AHI                             Apnea Hypopnea Index

AHI3%A                     Hypopneas with at least a 3% oxygen desaturation or an arousal

AHI4%                        Hypopneas with at least a 4% oxygen desaturation

AAI                              Ankle arm index

BP                               Blood pressure

BMI                             Body mass index

CHD                           Coronary heart disease

CVD                           Cardiovascular disease

CMS                           Centers for Medicare and Medicaid Services

ESS                            Epworth sleepiness scale

GenHlth                     General health rating subscale of SF36

Glmnet                       A statistical package used in R that fits a generalized linear model via penalized maximum likelihood

HDL                            High density lipoprotein

Lasso                         Least Absolute Shrinkage and Selection Operator

MICE                          Multiple imputation by chained equation

OSA                            Obstructive sleep apnea

PCS                            Physical component summary of the SF36

R                                 An open source programming language used for statistical computing and graphics

SHHS                         Sleep Heart Health Study

VigActiv                     Vigorous activity rating subscale of the SF36

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Cite as: Quan SF, Budhiraja R, Javaheri S, Parthasarathy S, Berry RB. The Association Between Obstructive Sleep Apnea Defined by 3 Percent Oxygen Desaturation or Arousal Definition and Self-Reported Cardiovascular Disease in the Sleep Heart Health Study. Southwest J Pulm Crit Care. 2020;21(4):86-103. doi: https://doi.org/10.13175/swjpcc054-20 PDF 

The Effect of CPAP on HRQOL as Measured by the Quality of Well-Being Self-Administered Questionnaire (QWB-SA)

Salma Batool-Anwar, MD, MPH¹
Olabimpe Omobomi, MD, MPH¹
Stuart F. Quan, MD^1,2

¹Division of Sleep and Circadian Disorders Medicine, Brigham and Women’s Hospital and Division of Sleep Medicine, Harvard Medical School, Boston, MA, ²Arizona Respiratory Center, University of Arizona College of Medicine, Tucson, AZ.

Abstract

Background: To examine the effect of continuous positive airway pressure (CPAP) on Health-related quality of life (HRQoL) as measured by the Quality of Well Being Self-Administered questionnaire (QWB-SA).

Methods: Participants from The Apnea Positive Pressure Long-term Efficacy Study (APPLES); a 6-month multicenter randomized, double-blinded intention to treat study, were included in this analysis. The participants with an apnea-hypopnea index >10 events/hour initially randomized to CPAP or Sham group were asked to complete QWB-SA at baseline, 2, 4, and 6-month visits.

Results: There were no group differences among either the CPAP or Sham groups. Mean age was 52±12 (SD] years, AHI 40±25 events/hr, BMI 32±7.1 kg/m2, and Epworth Sleepiness Score (ESS) 10±4 of 24 points. QWB-SA scores were available at baseline, and 2, 4 & 6 months after treatment in CPAP (n 558) and Sham CPAP (n 547) groups. There were no significant differences in QWB scores among mild, moderate or severe OSA participants at baseline. Modest improvement in QWB scores was noted at 2, 4 and 6- months among both Sham and CPAP groups (P <0.05).  However, no differences were observed between Sham CPAP and CPAP at any time point. Comparison of the QWB-SA data from the current study with published data in populations with chronic illnesses demonstrated that the impact of OSA is no different than the effect of AIDS and arthritis.

Conclusion: Although the QoL measured by the QWB-SA was impaired in OSA it did not have direct proportionality to OSA severity.

Introduction

Obstructive Sleep Apnea (OSA) is characterized by recurrent episodes of upper airway narrowing and oxygen desaturation with resultant frequent nighttime awakenings and daytime sleepiness (1). A strong association between OSA and obesity has been described (2), and with the global epidemic of obesity (3), the prevalence of OSA is anticipated to increase. Recent studies have reported an increase in prevalence from 22 to 37% among men, and 17 to 50% among women (4).

Health related quality of life (HRQoL) relates to a World Health Organization definition of health comprised of physical, mental, spiritual and social wellbeing (5). A variety of questionnaires are used in epidemiologic studies to assess quality of life (QoL). Studies demonstrate that QoL is worse in persons with OSA (6). Continuous positive airway pressure (CPAP) is the gold standard for treating OSA and improves daytime sleepiness among adherent patients (7). However, studies examining the effect of CPAP on quality of life have not found consistent results (8,9). These discrepancies are attributed to the fact that there are two types of questionnaires which are used to assess QoL; generic or disease specific. Utilizing data from the Apnea Positive Pressure long term Efficacy Study (APPLES), a randomized controlled trial of CPAP vs Sham CPAP, we analyzed whether CPAP improved HRQoL using the self-administered version of the Quality of Well-Being Scale (QWB-SA), a well-validated generic HRQoL instrument, that has not been validated in OSA.

Materials and Methods

 
Study Population and Protocol. APPLES was a 6-month multicenter, randomized, double-blinded, 2-arm, sham-controlled, intention-to-treat study of CPAP efficacy on three domains of neurocognitive function in OSA. A detailed description of the protocol has previously been published (10). Briefly, the participants were recruited either through local advertisement or from those attending sleep clinics for evaluation of possible OSA. Symptoms indicative of OSA were used to screen potential participants. The initial clinical evaluation included administering informed consent and screening questionnaires as well as history and physical examination and medical assessment by a study physician. Participants subsequently returned 2-4 weeks later for a baseline 24-h sleep laboratory visit, during which polysomnography (PSG) was performed to confirm the diagnosis followed by a day of neurocognitive, mood, sleepiness, and QoL testing. Inclusion criteria have been published previously and included age ≥ 18 years and a clinical diagnosis of OSA, as defined by the American Academy of Sleep Medicine (AASM) criteria. Only participants with an apnea-hypopnea index (AHI) ≥ 10 by PSG were randomized to CPAP or sham CPAP and continued in the APPLES study. Exclusion criteria included previous treatment for OSA with CPAP or surgery, oxygen saturation on the baseline PSG <75% for >10% of the recording time, history of a motor vehicle accident related to sleepiness within the past 12 months, presence of several chronic medical conditions, use of various medications known to affect sleep or neurocognitive function, and other health and social factors that may impact standardized testing procedures (e.g., shift work). After randomization, participants underwent a CPAP or sham CPAP titration and were followed for 6 months on their assigned intervention. Subsequent study visits occurred at 2, 4 and 6 months after the titration PSG. The APPLES study was approved by an institutional review board for human studies at each clinical site; informed consent was obtained from all participants at the time of enrollment as previously described.

Quality of Well-Being Scale (QWB). The QWB is a comprehensive measure of HRQoL. It has been extensively validated and can be used to calculate quality-adjusted life years (QALYs) (11). Because of its complexity, a self-administered version, the QWB-SA was developed (12). The questionnaire is sensitive to changes at the higher levels of functioning and can also produce estimates of QALY for cost-effectiveness analyses. The QWB-SA includes 5 sections. The first assesses the presence/absence of 19 chronic symptoms or problems (e.g., blindness, speech problems). These chronic symptoms are followed by 25 acute (or more transient) physical symptoms (e.g. headache, coughing, pain), and 14 mental health symptoms and behaviors (e.g., sadness, anxiety, irritation). The remaining sections of the QWB-SA include assessments of mobility (including use of transportation), physical activity (e.g., walking and bending over) and social activity including completion of role expectations (e.g., work, school, or home). Scores from each subscale are coupled with population derived weights to yield one composite score ranging from 0.09 (lowest possible health state to 1 for perfect health, with zero meaning death.

The QWB-SA was administered at the baseline study visit and at each subsequent study visit. At each visit, we collected three scores (QWB1, QWB2, and QWB3) corresponding to the day of the survey and the immediate 2 previous days. These scores included combinations of questions from the 5 sections as follows:

• Part I: Acute and chronic symptoms
• Part II: Self Care
• Part III: Mobility
• Part IV: Physical activity
• Part V: Social activity

To calculate the QWB-SA the scores for each section were computed and combined according to guidelines provided by the University of California, San Diego (UCSD) Health Services Research Center to yield the QWB score for each day. From the daily scores, the QWB Average Score was derived as the mean of QWB1+QWB2+QWB3. We used the QWB Average Score in subsequent analyses.

Polysomnography (PSG). The PSG montage included monitoring of the electroencephalogram (EEG, C₃-A₂ or C₄-A₁, O₂-A₁ or O₁-A₂), electrooculogram (EOG, ROC-A₁, LOC-A₂), chin and anterior tibialis electromyograms (EMG), heart rate by 2-lead electrocardiogram, snoring intensity (anterior neck microphone), nasal pressure (nasal cannula), nasal/oral thermistor, thoracic and abdominal movement (inductance plethysmography bands), and oxygen saturation (pulse oximetry). All PSG records were electronically transmitted to a centralized data coordinating and PSG reading center. Sleep and wakefulness were scored using Rechtschaffen and Kales criteria (13). Apneas and hypopneas were scored using the American Academy of Sleep Medicine Task Force diagnostic criteria (14). Briefly, an apnea was defined by a clear decrease (> 90%) from baseline in the amplitude of the nasal pressure or thermistor signal lasting ≥ 10 sec. Hypopneas were identified if there was a clear decrease (> 50% but ≤ 90%) from baseline in the amplitude of the nasal pressure or thermistor signal, or if there was a clear amplitude reduction of the nasal pressure signal ≥ 10 sec that did not reach the above criterion, but was associated with either an oxygen desaturation > 3% or an arousal. Obstructive events were scored if there was a persistence of chest or abdominal respiratory effort. Central events were noted if no displacement occurred on either the chest or abdominal channels. The AHI was computed as the number of apneas and hypopneas divided by the total sleep time. Sleep apnea was classified as mild (AHI 10.0 to 15.0 events per hour), moderate (AHI 15.1 to 30.0 events per hour), and severe (AHI more than 30 events per hour) (14).

CPAP Adherence. Nightly use of CPAP was downloaded from the device and was assessed at 2, 4, 6-month intervals. The participants were considered adherent if CPAP use was ≥ 4 hours per night for >70% of nights.

Epworth Sleepiness Scale (ESS). The ESS is a validated self-completion tool that asks subjects to rate the likelihood of falling asleep in eight common situations using four ordinal categories ranging from 0 (no chance) to 3 (high chance) (15). Scores range from 0 to 24 with a score >10 suggesting EDS (15).

Calgary Sleep Apnea Quality of Life Index (SAQLI). The SAQLI was developed as a sleep apnea specific quality of life instrument (16). It is a 35-item instrument that captures the adverse impact of sleep apnea on 4 domains: daily functioning, social interactions, emotional functioning and symptoms. Items are scored on a 7- point scale with “all of the time” and “not at all” being the most extreme responses. Item and domain scores are averaged to yield a composite total score between 1 and 7. Higher scores represent a better quality of life.

Statistical Analysis. Simple linear and multiple regression models were used to estimate the degree to which variables correlated with QWB scores.  We examined the association between the QWB-SA and the following variables: OSA severity as measured by the AHI, sleepiness as assessed by ESS, age, and baseline body mass index (BMI, kg/m²). Severity of OSA in this study was defined according to the AHI as follows: Mild (10-<15 /h), Moderate (15-<30 /h), Severe (>30 /h). Changes in QWB-SA over the duration of the study were analyzed using a mixed model repeated measures analysis of variance with participants stratified by their randomization group (CPAP or Sham CPAP). Analyses were performed using STATA (version 11, StataCop TX USA) and IBM SPSS v24 (Armonk, NY). Finally, we compared the sample means to the normative means using GraphPad Prism8.

Results

Initially, 558 participants were randomized to CPAP and 547 to Sham CPAP. As shown in Table 1, age, gender, ethnicity, body mass index (BMI, kg/m²), AHI, and ESS were similar between the CPAP and Sham CPAP groups.

Table 1. Baseline Characteristics.

SD: Standard Deviation, BMI: Body Mass Index, AHI: Apnea Hypopnea Index, ESS: Epworth Sleepiness Scale, SAQLI: Sleep Apnea Quality of Life Index, QWB: Quality of wellbeing

Men comprised of 50% of the study population and the population was generally obese (CPAP: BMI 32.4 ± 7.3; Sham: BMI 32.1 ± 6.9 kg/m²). The participants overall had at least 15 years of education, and over 50% of the participants were either married or living with someone. The sample population did not report severe excessive daytime sleepiness with the reported ESS approximately 10 in both the CPAP and SHAM groups. Similarly, there were no significant differences in SAQLI score, total sleep time or arousal index among the two treatment groups.

Scores for the QWB-SA were available at baseline and 2, 4 and 6 months after treatment in both groups. As shown in Table 2, there were no significant differences in QWB-SA at baseline between both groups.

Table 2. Mixed model analysis for the effect of time on QWB average score among CPAP and SHAM groups (N=1104).

*QWB-SA scores improved in both groups over the 6 months of follow-up, p<0.05.

In addition, scores among mild, moderate or severe OSA participants at baseline also were not different (data not shown). Modest improvement in QWB scores was noted at 2, 4 and 6- month among both Sham and CPAP groups (P<0.05). However, no differences were observed between Sham CPAP and CPAP at any time point. Furthermore, multiple regression analyses stratified by OSA severity, gender, and mean adherence to CPAP or Sham CPAP suggested significant improvement in QWB scores only among women with severe OSA in the CPAP group (data not shown, P <0.05).

Table 3 shows comparisons of the QWB-SA from the current study with published data in populations with acquired immune deficiency syndrome (AIDS), chronic obstructive lung disease (COPD), arthritis and prostate cancer (17-20).

Table 3. Comparison of sample mean to normative means.

QWB: Quality of Wellbeing, CF: Cystic Fibrosis, OSA: obstructive Sleep Apnea, AIDS: Acquired Immunodeficiency syndrome, COPD: Chronic Obstructive pulmonary Disease.

The impact of OSA is not different than the effect of AIDS and arthritis and only slightly less than with COPD and prostate cancer.

Discussion

In this study, we analyzed the effect of CPAP therapy on QoL using the QWB-SA questionnaire. We found that the cross-sectional mean QWB-SA scores were comparable to the scores found in other chronic illnesses (COPD, arthritis, cystic fibrosis, prostate cancer, and AIDS) (17-20) indicating that quality of life is adversely affected by sleep apnea similar to these chronic conditions. Although the QWB-SA modestly declined over a treatment duration of 6 months, the instrument was unable to distinguish any differences between CPAP and sham CPAP. Moreover, these findings remained after stratifying based on PAP adherence and OSA severity.

Assessment of quality of life (QoL) is an integral part of OSA management and various scales are being used by researchers. Studies using these instruments generally find that QoL is impaired in persons with OSA (6). However, to our knowledge, there have not been previous studies using the QWB-SA in a population with OSA. Our findings which demonstrate that the QWB-SA is low in OSA are consistent with these prior investigations.  However, in contrast to our observations, some but not all studies have noted a greater impact of OSA on QoL in those with more severe disease. For example, Baldwin et al. (21) in the Sleep Heart Health Study found that there was a higher risk of having an impact on the vitality subscale of SF-36 with greater OSA severity. In contradistinction, Fornas et al. (22) using the Nottingham Health Profile found no relationship between OSA severity and differences in QoL in a moderate size group of OSA patients. This discrepancy may relate to whether a general population as in Baldwin et al or a clinical population as in Fornas et al. was studied. Additionally, instruments used to quantify QoL may assess different domains, thus leading to different conclusions. Thus, while the QWB-SA can detect that QoL is impaired in those with OSA, it does not have the capability to distinguish subtleties related to differences in OSA severity.

At baseline, we observed that scores on the QWB-SA were comparable to those found for patients with AIDS (23) and arthritis (20) but were slightly higher than those with COPD (18), cystic fibrosis (CF) (24), and prostate cancer (19). They are notably better than chronic renal failure on hemodialysis (0.49) (25). Thus, it appears that the impact of OSA on QoL is approximately the same as several but not all other chronic conditions that are viewed by the general public as having considerably greater health consequences.

Contrary to its use in cystic fibrosis and AIDS where QWB-SA has validity as an outcome measure (18-24) we did not find that the QWB-SA was able to detect changes in QoL with the use of CPAP. This observation also is contradistinction to results from the CPAP Apnea Trial North American Program using the Functional Outcomes of Sleep Questionnaire (FOSQ) as well as analyses of the Sleep Apnea Quality of Life Inventory (SAQLI) in the APPLES (26,27) study. In contrast to QWB-SA, both the FOSQ and SAQLI are sleep specific QoL instruments. Thus, the results of our study provide additional evidence that a generic HRQoL instrument may not be sensitive to the specific QoL domains impacted by treatment of OSA using CPAP. Other studies have concluded that changes in QoL in response to CPAP therapy may vary depending on the QoL measure used and that some measures may be more sensitive to detecting changes to QoL with CPAP therapy than others (28). A randomized control trial with a total of 1256 patients comparing various QoL tools concluded that generic QoL tools may not be sufficient at detecting important changes in QoL in OSA patients as CPAP may not improve general QoL scores but rather specific QoL domains. For instance, in that analysis, the SF-36 tool demonstrated positive changes only in physical function and energy levels with CPAP (29). In contrast, a study comparing 2 sleep specific QoL instruments to the generic 36-item short form survey (SF-36), found that the FOSQ and SAQLI provided unique information about health outcomes in treated OSA patients (30) and correlated well with the SF36 survey domains. In that study, the FOSQ was found to be more sensitive to differences in CPAP adherence than the SAQLI.

To our knowledge, this is the first study examining the effect of CPAP on QoL using the QWB-SA questionnaire. A major strength of the study is that it utilized data from a large multicenter randomized controlled trial with follow up and interval documentation of CPAP adherence for up to 6 months. However, there were several limitations. First, the study population was a mixture of patients recruited from sleep clinics and the general population; this may have resulted in a differential impact on QoL. Second, overall adherence to both CPAP and sham CPAP was relatively poor although not inconsistent with the results from other studies. Finally, QoL was assessed using the average QWB-SA total scores and hence it is unclear whether there may have been improvements in specific domains over time with CPAP treatment.

In conclusion, despite the limitations, we found that QoL measured by the QWB-SA was impaired in OSA but was not found to have direct proportionality to OSA severity. Furthermore, it was not sufficiently sensitive for detecting QoL changes in OSA patients on CPAP therapy. Our data support the use of sleep apnea specific QoL questionnaires for measurement of QoL after initiation of CPAP.

Acknowledgments

The Apnea Positive Pressure Long-term Efficacy Study (APPLES) study was funded by contract 5UO1-HL-068060 from the National Heart, Lung and Blood Institute. The APPLES pilot studies were supported by grants from the American Academy of Sleep Medicine and the Sleep Medicine Education and Research Foundation to Stanford University and by the National Institute of Neurological Disorders and Stroke (N44-NS-002394) to SAM Technology. In addition, APPLES investigators gratefully recognize the vital input and support of Dr. Sylvan Green, who died before the results of this trial were analyzed, but was instrumental in its design and conduct.

Administrative Core: Clete A. Kushida, MD, PhD; Deborah A. Nichols, MS; Eileen B. Leary, BA, RPSGT; Pamela R. Hyde, MA; Tyson H. Holmes, PhD; Daniel A. Bloch, PhD; William C. Dement, MD, PhD

Data Coordinating Center: Daniel A. Bloch, PhD; Tyson H. Holmes, PhD; Deborah A. Nichols, MS; Rik Jadrnicek, Microflow, Ric Miller, Microflow Usman Aijaz, MS; Aamir Farooq, PhD; Darryl Thomander, PhD; Chia-Yu Cardell, RPSGT; Emily Kees, Michael E. Sorel, MPH; Oscar Carrillo, RPSGT; Tami Crabtree, MS; Booil Jo, PhD; Ray Balise, PhD; Tracy Kuo, PhD

Clinical Coordinating Center: Clete A. Kushida, MD, PhD, William C. Dement, MD, PhD, Pamela R. Hyde, MA, Rhonda M. Wong, BA, Pete Silva, Max Hirshkowitz, PhD, Alan Gevins, DSc, Gary Kay, PhD, Linda K. McEvoy, PhD, Cynthia S. Chan, BS, Sylvan Green, MD

Clinical Centers

Stanford University: Christian Guilleminault, MD; Eileen B. Leary, BA, RPSGT; David Claman, MD; Stephen Brooks, MD; Julianne Blythe, PA-C, RPSGT; Jennifer Blair, BA; Pam Simi, Ronelle Broussard, BA; Emily Greenberg, MPH; Bethany Franklin, MS; Amirah Khouzam, MA; Sanjana Behari Black, BS, RPSGT; Viola Arias, RPSGT; Romelyn Delos Santos, BS; Tara Tanaka, PhD

University of Arizona: Stuart F. Quan, MD; James L. Goodwin, PhD; Wei Shen, MD; Phillip Eichling, MD; Rohit Budhiraja, MD; Charles Wynstra, MBA; Cathy Ward, Colleen Dunn, BS; Terry Smith, BS; Dane Holderman, Michael Robinson, BS; Osmara Molina, BS; Aaron Ostrovsky, Jesus Wences, Sean Priefert, Julia Rogers, BS; Megan Ruiter, BS; Leslie Crosby, BS, RN

St. Mary Medical Center: Richard D. Simon Jr., MD; Kevin Hurlburt, RPSGT; Michael Bernstein, MD; Timothy Davidson, MD; Jeannine Orock-Takele, RPSGT; Shelly Rubin, MA; Phillip Smith, RPSGT; Erica Roth, RPSGT; Julie Flaa, RPSGT; Jennifer Blair, BA; Jennifer Schwartz, BA; Anna Simon, BA; Amber Randall, BA

St. Luke's Hospital: James K. Walsh, PhD, Paula K. Schweitzer, PhD, Anup Katyal, MD, Rhody Eisenstein, MD, Stephen Feren, MD, Nancy Cline, Dena Robertson, RN, Sheri Compton, RN, Susan Greene, Kara Griffin, MS, Janine Hall, PhD

Brigham and Women's Hospital: Daniel J. Gottlieb, MD, MPH, David P. White, MD, Denise Clarke, BSc, RPSGT, Kevin Moore, BA, Grace Brown, BA, Paige Hardy, MS, Kerry Eudy, PhD, Lawrence Epstein, MD, Sanjay Patel, MD

Sleep HealthCenters for the use of their clinical facilities to conduct this research

Consultant Teams

Methodology Team: Daniel A. Bloch, PhD, Sylvan Green, MD, Tyson H. Holmes, PhD, Maurice M. Ohayon, MD, DSc, David White, MD, Terry Young, PhD

Sleep-Disordered Breathing Protocol Team: Christian Guilleminault, MD, Stuart Quan, MD, David White, MD

EEG/Neurocognitive Function Team: Jed Black, MD, Alan Gevins, DSc, Max Hirshkowitz, PhD, Gary Kay, PhD, Tracy Kuo, PhD

Mood and Sleepiness Assessment Team: Ruth Benca, MD, PhD, William C. Dement, MD, PhD, Karl Doghramji, MD, Tracy Kuo, PhD, James K. Walsh, PhD

Quality of Life Assessment Team: W. Ward Flemons, MD, Robert M. Kaplan, PhD

APPLES Secondary Analysis-Neurocognitive (ASA-NC) Team: Dean Beebe, PhD, Robert Heaton, PhD, Joel Kramer, PsyD, Ronald Lazar, PhD, David Loewenstein, PhD, Frederick Schmitt, PhD

National Heart, Lung, and Blood Institute (NHLBI)

Michael J. Twery, PhD, Gail G. Weinmann, MD, Colin O. Wu, PhD

Data and Safety Monitoring Board (DSMB)

Seven-year term: Richard J. Martin, MD (Chair), David F. Dinges, PhD, Charles F. Emery, PhD, Susan M. Harding MD, John M. Lachin, ScD, Phyllis C. Zee, MD, PhD

Other term: Xihong Lin, PhD (2 y), Thomas H. Murray, PhD (1 y).

Abbreviations

• AASM: American Academy of Sleep Medicine
• AHI: Apnea Hypopnea Index
• AIDS: Acquired immune deficiency syndrome
• APPLES: Apnea Positive Pressure Long-term Efficacy Study
• BMI: Body mass Index
• CF: Cystic Fibrosis
• COPD: Chronic obstructive pulmonary disease
• CPAP: Continuous positive airway pressure.
• EDS: Excessive daytime sleepiness
• EEG: Electroencephalogram
• ESS: Epworth sleepiness scale
• EMG: Electromyogram
• EOG: Electrooculogram
• FOSQ: Functional Outcomes of Sleep Questionnaire
• HRQoL: health related quality of life
• OSA: Obstructive Sleep apnea
• PSG: polysomnograpgy
• QALY: Quality Adjusted life years
• QoL: Quality of Life
• QWB: Quality of well being
• QWB-SA: Quality of well being-Self administered.
• SAQLI: Sleep apnea quality of life Index
• SD: Standard deviation

References

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Cite as: Batool-Anwar S, Omobomi O, Quan SF. The effect of CPAP on HRQOL as Measured by the quality of Well-Being Self-Administered Questionnaire (QWB-SA). Southwest J Pulm Crit Care. 2020;20(1):29-40. doi: https://doi.org/10.13175/swjpcc070-19 PDF 

Sleep Board Review Question: Insomnia in Obstructive Sleep Apnea

Rohit Budhiraja, MD

Department of Medicine, Southern Arizona Veterans Affairs Health Care System (SAVAHCS) and University of Arizona, Tucson, AZ.

What is the estimated prevalence of insomnia symptoms in patients with obstructive sleep apnea?

1. Less than 1%
2. 5%-10%
3. 20-30%
4. 40%-60%
5. Greater than 80% 

Reference as: Budhiraja R. Sleep board review question: insomnia in obstructive sleep apnea. Southwest J Pulm Crit Care. 2013;7(5):302-3. doi: http://dx.doi.org/10.13175/swjpcc150-13 PDF

Sleep Board Review Questions: CPAP Adherence in OSA

Carmen Luraschi-Monjagatta, MD¹

Rohit Budhiraja, MD^1,2

¹ Department of Medicine^, Section of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Arizona, Tucson, AZ, 85724, USA. mdelcarmen@deptofmed.arizona.edu

² Department of Medicine, Southern Arizona Veterans Affairs Health Care System (SAVAHCS), Tucson, AZ 85723, USA. rohit.budhiraja@va.gov

Which of the following has been shown to be associated with a better adherence to positive airway pressure (PAP) therapy in adults with obstructive sleep apnea (OSA)?

1. Use of auto-titrating positive airway pressure (autoPAP) instead of CPAP.
2. Cognitive behavioral therapy prior to initiation of PAP therapy.
3. Severe sleep apnea on diagnostic polysomnography.
4. Epworth Sleepiness Scale (ESS) score at initiation of PAP therapy.

Reference as: Luraschi-Monjagatta C, Budhiraja R. Sleep board review questions: CPAP adherence in OSA. Southwest J Pulm Crit Care 2012;5:135-7. (Click here for a PDF version)