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 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

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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 Related Breathing Disorders and Neurally Mediated Syncope (SRBD and NMS)

Damian Valencia, MD¹ 

Stella Pak, MD¹

Juan Linares, MD¹

Victor Valencia, BS²

Christopher Lee, MD¹

John-Philip Markovic, MD¹

Hemant Shah, MD¹ 

¹Department of Medicine, Kettering Medical Center, Kettering, Ohio USA

²Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois USA

Abstract

Introduction: Individuals with severe sleep related breathing disorders (SRBD) tend to experience intermittent hypoxia, sleep fragmentation and highly fluctuating intrathoracic pressures. Chronic exposure to these stressors sensitizes the parasympathetic system while suppressing the sympathetic system. Parasympathetic over-reactivity among patients with severe sleep related breathing disorders has been proposed as a predisposing factor for neurally mediated syncope.

Goal: We sought to determine the relative risk for neurally mediated syncope in patients with severe SRBD compared to the general population.

Methods: This is a retrospective cohort study of 228 cases selected from 2,598 patients who were referred for polysomnography on discharge from hospitalization. Incidence of neurally mediated syncope (NMS) was compared between patients with apnea-hypopnea-index (AHI) scores of 30 or greater and those with an AHI score below 5.

Results: Approximately 32% of patients with severe SRBD had a history of neurally mediated syncope compared to only 14% in patients with normal sleep breathing patterns (OR = 3.09, 95% CI: 1.25 - 7.62, p = 0.015).

Conclusion: Our multi-center retrospective study supports an association between SRBD and NMS.

Brief Summary

Current Knowledge/Study Rational. There are multiple reports that highlight a possible connection between sleep related breathing disorders and neurally mediated syncope. Deleterious effects on the autonomic and peripheral nervous system by severe sleep related breathing disorders have also been demonstrated. We sought to determine the association and relative risk of neurally mediated syncope in patients with severe sleep related breathing disorders.

Study Impact. Patients with severe sleep related breathing disorders are at increased risk for neurally mediated syncope. Early identification and appropriate treatment in this patient population may reduce rates of syncope, improve quality of life and clinical outcomes.

Introduction

Sleep related breathing disorders (SRBD), comprise a spectrum of disorders characterized by chronic intermittent apnea and hypopnea, which includes obstructive sleep apnea (OSA), central sleep apnea, sleep-related hypoventilation, and nocturnal hypoxemia (1). Neurally mediated syncope (NMS), also known as reflex syncope, is defined as a transient loss of consciousness secondary to decreased cerebral blood supply, typically as a result of reflexive cardiac inhibition and decreased vascular tone. NMS includes vasovagal syncope, situational syncope and carotid sinus syncope (2). Autonomic dysfunction may also play a role in cases of NMS (3). Researchers have previously documented the deleterious effects of SRBD on the autonomic and peripheral nervous system (4-7). A connection between SRBD and NMS has been proposed by some, detailing cases of patients suffering from incapacitating recurrent syncope which demonstrates dramatic improvement or resolution after diagnosis and treatment of OSA (8-10). In this study, we sought to determine the association and relative risk of neurally mediated syncope in patients with severe sleep related breathing disorders.

Methods

This retrospective cohort analysis was performed using electronic medical record data collection from hospitals within the Kettering Health Network, including Fort Hamilton Hospital (Hamilton, Ohio), Grandview Medical Center (Dayton, Ohio), Greene Memorial Hospital (Xenia, Ohio), Kettering Medical Center (Kettering, Ohio), Soin Medical Center (Beavercreek, Ohio), Southview Medical Center (Centerville, Ohio) and Sycamore Medical Center (Miamisburg, Ohio). Individuals who underwent and completed in-facility polysomnography were selected for study review. Patients under the age of 18 years old and those with a pacemaker or implantable cardiac defibrillator were excluded from the study. Patients were divided into two groups; those with severe SRBD, defined as having an Apnea-Hypopnea Index (AHI) score of/ or greater than 30, and a control group, defined as patients having an AHI score of/ or less than 5. This study was approved by the institutional review board (IRB) at Kettering Health Network.

Statistical Methods: The Kolmogorov-Smirnov and Shapiro-Wilk tests were utilized to compare baseline patient demographics between each group; control and severe SRBD group. These tests were selected to better represent the data, with median interquartile range (IQR), as outliers were included in the analysis. Categorical variables were compared using Pearson’s Chi-squared test. Continuous variables were compared using the Student’s t-test or Wilcoxon rank sum test (Mann-Whitney U test). All estimates were reported as 95% confidence intervals with p-values. Two-sided p-values less than 0.05 were considered statistically significant. Multivariate logistic regression modeling was used to determine the effects of each variable while controlling for confounding variables. Odds ratios (OR) were calculated for each type of syncope in both the severe SRBD group and control group. All statistical analyses were performed using IBM SPSS Statistics for Windows version 20.0 (IBM Corp., Armonk, NY, USA).

Results

A total of 2,598 patients were identified from the electronic medical record database, of which, only 228 patients fulfilled our inclusion criteria for severe SRBD (AHI score of/ or greater than 30), with 80 patients meeting criteria for the control group (AHI score of/ or less than 5). Among the 228 patients with severe SRBD, the most common subtype was obstructive sleep apnea (204 of 228 patients, 89.5%), followed by central sleep apnea (13 of 228 patients, 4.2%) and mixed type (11 of 228 patients, 3.6%). 

Initial comparison of demographic characteristics (Table 1) was done using univariate analysis.

Table 1: Baseline Characteristics of the Individuals with and without SRBD.

SRBD: sleep-related breathing disorder, IQR: interquartile range; BMI: body-mass index; AHI: apnea-hypopnea index; LEVF: left ventricular ejection fraction; COPD: chronic obstructive pulmonary disease; N: number; N/A: not applicable.

The SRBD group and control did not statistically significance differ in age (p = 0.79). Although gender differences were noted, 62.3% male in the SRBD group compared to 47.5% in the control group (p = 0.042), there were no statistically significant differences on multivariate logistic regression (p = 0.854). Differences in body mass index (BMI) between groups were noted on univariate (p = 0.042) and multivariate models (p = 0.041), with 36.5 (IQR 31.3 – 43.8) mean BMI of the SRBD group compared to 34.4 (IQR 27.3 – 40.4) in the control group.

The incidence of pre-existing comorbidities between groups was also compared. (Table 1) There were no statistically significant differences between the groups in terms of pulmonary artery pressure (p = 0.226), diabetes (p = 0.902) and coronary artery disease (p = 0.065). Univariate analysis did reveal differences amongst left ventricular ejection fraction (LVEF), 31.1% of patients with SRBD had LVEF <55% compared to only 17.5% in the control group (p = 0.19), hypertension (HTN), 75.4% of patients with SRBD had HTN compared to only 53.8% in the control group (p < 0.001), and chronic obstructive pulmonary disease (COPD), 29.8% of patients with SRBD compared to 43.8% in the control group (p = 0.023). These findings were not statistically significant on multivariate logistic regression; LVEF<55% (p = 0.326), HTN (p = 0.585), COPD (p = 0.576). 

The mean apnea hypopnea index (AHI) for the severe SRBD group was 53.2 (38.7-80.2), compared to 1.6 (0-2.6) in the control group (p < 0.001). The prevalence of NMS was higher in the SRBD group compared to the control group, 32% (73 of 228 patients) and 14% (11 of 80 patients), respectively; χ² (2, N=308) = 9.96, p = 0.001. Prevalence of non-neurally mediated syncope did not differ significantly between the SRBD group and control group, 1% (2 of 228 patients) and 0% (no patients), respectively; Pearson’s Chi-squared test p = 0.571. Approximately 32% of patients with severe SRBD had a history of neurally mediated syncope compared to only 14% in patients with normal sleep breathing patterns (OR = 3.09, 95% CI: 1.25 - 7.62, p = 0.015). (Table 2).

Table 2: Multivariate Logistic Regression Modeling, Odds Ratio (OR) for Neurally Mediated Syncope.

CI: confidence interval; BMI: body mass index; AHI: apnea-hypopnea index; LEVF: left ventricular ejection fraction; COPD: chronic obstructive pulmonary disease.

Situational syncope has not been consistently recorded, OR and CI were not calculated.

Discussion

This study suggests that individuals with severe sleep related breathing disorders are at increased risk for developing neurally mediated syncope. Chrysostomakis et al. (11) showed that parasympathetic activity is increased during the night in patients with obstructive sleep apnea and that continuous positive airway pressure (CPAP) treatment may restore autonomic balance. Puel et al. (8) suggested that intermittent hypoxia, sleep fragmentation and variations of intra-thoracic pressures may result in chronic adaptations to the autonomic nervous system, which may predispose patients to vasovagal syncope. Cintra et al. (12) noted that patients with vasovagal syncope exhibited sympathetic suppression during rapid eye movement (REM) sleep. Previous studies indicate that chronic intermittent hypoxia can also increase activation of free-radical oxidation, which in turn can elicit rapid and sustained expression of pro-inflammatory cytokines (12). This oxidative stress and inflammatory response can induce tissue damage and intermittent academia, eventually leading to up-regulation of pH sensitive ion channels on chemo-afferent neurons at the carotid bodies. Overexpression of these channels potentiates the carotid body response to changes in arterial oxygen saturation (12). It is possible that this mechanism also contributes to the higher prevalence of carotid sinus hypersensitivity and syncope among patients with severe SRBD. In accordance with our findings, there have been reported cases of recurrent syncope, which have resolved with correction of underlying SRBD (8-10). Appropriate management of SRBD in this patient population may reduce rates of NMS. Given the high prevalence of SRBD and neurally mediated syncope in the United States, further investigation is warranted to delineate the association between the two disease processes and the mechanisms which are involved.

Study Limitations

Our retrospective study design has limited control over consistency and accuracy. This study did not use any matching algorithm to match the control group to the individuals with severe SRBD for baseline characteristics. We did not utilize a caliper matching process to identify controls, and as a result, the control cohort was smaller than the study cohort. The diagnosis of vasovagal syncope was made based on clinical presentation. All patients who were diagnosed with vasovagal syncope did not have confirmatory tilt-table testing, limiting diagnostic accuracy and consistency.

Conclusion

Our study suggests that individuals with severe sleep-related breathing disorders (SRBD) are approximately 3 times (OR = 3.09, 95% CI: 1.25 - 7.62, p = 0.015) more likely to have experienced neurally mediated syncope (NMS) compared to case matched controls.

Acknowledgement

Rosaria Jordan (table/figure formatting)

References

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2. Alboni P, Brignole M, Menozzi C, et al. Diagnostic value of history in patients with syncope with or without heart disease. J Am Coll Cardiol. 2001;37(7):1921-8. [CrossRef] [PubMed]
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5. Gozal D, Daniel JM, Dohanich GP. Behavioral and anatomical correlates of chronic episodic hypoxia during sleep in the rat. J Neurosci. 2001;21(7):2442-50. [PubMed]
6. Mayer P, Dematteis M, Pépin JL, et al. Peripheral neuropathy in sleep apnea: A tissue marker of the severity of nocturnal desaturation. Am J Respir Crit Care Med. 1999;159(1):213-9. [CrossRef] [PubMed]
7. Dziewas R, Schilling M, Engel P, et al. Treatment for obstructive sleep apnoea: effect on peripheral nerve function. J Neurol Neurosurg Psychiatry. 2007;78(3):295-7. [CrossRef] [PubMed]
8. Puel V, Pepin JL, Gosse P. Sleep related breathing disorders and vasovagal syncope, a possible causal link? Int J Cardiol. 2013;168(2):1666-7. [CrossRef] [PubMed]
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10. Willis FB, Isley AL, Geda YE, Shaygan A, Quarles L, Fredrickson P a. Resolution of syncope with treatment of sleep apnea. J Am Board Fam Med. 2008;21(5):466-8. [CrossRef] [PubMed]
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12. Cintra F, Poyares D, DO Amaral A, et al. Heart rate variability during sleep in patients with vasovagal syncope. Pacing Clin Electrophysiol. 2005;28(12):1310-6. [CrossRef] [PubMed]

Cite as: Valencia D, Pak S, Linares J, Valencia V, Lee C, Markovic J-P, Shah H. Sleep related breathing disorders and neurally mediated syncope (SRBD and NMS). Southwest J Pulm Crit Care. 2019;18(4):76-81. doi: https://doi.org/10.13175/swjpcc015-19 PDF 

Role of Spousal Involvement in Continuous Positive Airway Pressure (CPAP) Adherence in Patients with Obstructive Sleep Apnea (OSA)

Salma Batool-Anwar, MD, MPH ²

Carol M. Baldwin, PhD, MSN ³

Shira Fass, PhD⁴

Stuart F. Quan, MD ^1,2

¹University of Arizona College of Medicine, Tucson, AZ USA

²Brigham and Women’s Hospital, Boston, MA USA

³Arizona State University College of Nursing and Health Innovation and College of Health Solutions, Phoenix, AZ USA

⁴Case Western Reserve University, Cleveland, Ohio USA

Abstract

Introduction: Little is known about the impact of spousal involvement on continuous positive airway pressure (CPAP) adherence. The aim of this study was to determine whether spouse involvement affects adherence with CPAP therapy, and how this association varies with gender.  

Methods: 194 subjects recruited from Apnea Positive Pressure Long Term Efficacy Study (APPLES) completed the Dyadic Adjustment Scale (DAS). The majority of participants were Caucasian (83%), and males (73%), with mean age of 56 years, mean BMI of 31 kg/m². & 62% had severe OSA. The DAS is a validated 32-item self-report instrument measuring dyadic consensus, satisfaction, cohesion, and affectional expression. A high score in the DAS is indicative of a person’s adjustment to the marriage. Additionally, questions related to spouse involvement with general health and CPAP use were asked. CPAP use was downloaded from the device and self-report, and compliance was defined as usage > 4 h per night.

Results: There were no significant differences in overall marital quality between the compliant and noncompliant subjects. However, level of spousal involvement was associated with increased CPAP adherence at 6 months (p=0.01). After stratifying for gender these results were significant only among males (p=0.03). Three years after completing APPLES, level of spousal involvement was not associated with CPAP compliance even after gender stratification.

Conclusion: Spousal involvement is important in determining CPAP compliance in males in the 1^st 6 months after initiation of therapy but is not predictive of longer-term adherence. Involvement of the spouse should be considered an integral part of CPAP initiation procedures.

Abbreviations List

AASM: American Academy of Sleep Medicine

AHI: Apnena Hyponea Index

APPLES: Apnea Positive Pressure Long Term Efficacy Study

BMI: Body Mass Index

CPAP: Continuous positive airway pressure

DAS: Dyadic Adjustment Scale

EEG: Electroencephalogram

EMG: Electromyograms

EOG: Electroocculogram

OSA: Obstructive Sleep Apnea

PSG: polysomnography

Introduction

Obstructive Sleep apnea (OSA) is characterized by repetitive episodes of upper airway closure during sleep resulting in oxygen desaturation and frequent arousals. In addition to cardiovascular comorbidities, OSA has been linked to poor quality of life, depression and motor vehicle accidents. Recent data suggest an increase in the prevalence of OSA for both men and women (34% and 17.4% respectively) (1).

Continuous positive airway pressure (CPAP) is the treatment of choice for OSA. Poor adherence, however, remains a widely recognized problem limiting overall effectiveness of CPAP therapy. Prior studies have identified various factors and strategies to promote CPAP adherence (2). In addition to disease, educational, and technology-specific considerations that can affect CPAP adherence, social and psychological dynamics are important components of adherence as well.

Several studies have suggested that partner/spousal dyadic support can play a positive role in the patient’s overall health and health behaviors (3,4) . For example, higher CPAP adherence was reported among patients with bed partners (5), as well as persons who were married versus single (6). Little is known about the influence of spousal involvement on CPAP adherence. One study indicated that perceived spousal support predicted greater CPAP adherence among men with high disease severity; however, pressure to adhere to treatment by the wife was not of benefit and predicted poorer CPAP adherence (7). Another study indicated reduced marital conflict by OSA patients following 3 months of CPAP, suggesting that marital conflict resolution might serve as an intervention for CPAP adherence (8). Despite these hints that dyadic support may play a role in CPAP adherence, participants in both studies by Baron et al. (7.8) consisted primarily of men, and the studies focusing on CPAP adherence by Lewis et al. (5) and Gagnadoux et al. (6) included only men. Thus, the aim of the current study was to determine whether spouse involvement affects CPAP adherence and how this association differs by gender using data from a large randomized trial of CPAP versus sham CPAP to treat OSA. 

Methods

Study Population and Protocol

The Apnea Positive Pressure Long-term Efficacy Study (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. Three of the 5 APPLES Clinical Centers, the University of Arizona, Stanford University and St. Luke’s Hospital (Chesterfield, MO) participated in this ancillary study. A detailed description of the protocol has previously been published (9). Briefly, participants were either recruited through local advertisement, or from attending sleep clinics for evaluation of possible OSA. Symptoms indicative of OSA were used to prescreen potential participants. The initial clinical evaluation included administering informed consent, screening questionnaires, a history and physical examination, and a medical assessment by a study physician. Participants subsequently returned 2-4 weeks later for a 24-h sleep laboratory visit, during which polysomnography (PSG) was performed to confirm the diagnosis, followed by a day of neurocognitive, mood, sleepiness, and quality of life survey testing. Inclusion and exclusion criteria have been published previously and included age ≥ 18 years and a clinical diagnosis of OSA as defined by American Academy of Sleep Medicine (AASM) criteria. Only participants with an apnea hypopnea index (AHI) ≥ 10 by PSG were randomized to continue in the APPLES study. Exclusion criteria were previous treatment for OSA with CPAP or surgery, oxygen saturation on baseline PSG <75% for >10% of the recording time, history of motor vehicle accident-related to sleepiness within the past 12 months, presence of chronic medical conditions, use of various medications known to affect sleep or neurocognitive function, and various health and social factors that may impact standardized testing procedures (e.g., shift work).

Following the PSG, participants with an AHI ≥ 10 who met other enrollment criteria were randomized to CPAP or sham CPAP for continued participation in APPLES. After randomization, participants returned to the sleep laboratory for a CPAP or sham CPAP titration PSG. Subsequent assessments were made at 2, and 6 months post-randomization at which time a test battery was re-administered. At the conclusion of their 6-month post-randomization evaluations, each participant was informed of their treatment group assignment and offered CPAP treatment going forward. Approximately 36 months after the conclusion of APPLES, participants were sent the Dyadic Adjustment Scale (DAS) questionnaire with the addition of several additional questions related to health.

Assessment of Spouse involvement

Inclusion in the current analysis required that subjects were married during the APPLES study and remained married at the time of questionnaire administration. The DAS (10), a quality of marriage questionnaire, was utilized to assess marital relationship. It is a 32-item self-report instrument that incorporates four dimensions, including a 13 item dyadic consensus, 10 item dyadic satisfaction, 5 item dyadic cohesion, and 4 item affectional expression. A high DAS score is indicative of a person’s positive adjustment to the marriage. Additionally, questions related to spouse involvement with general health and CPAP use were asked (See Appendix for full questionnaire).

Polysomnography

The PSG montage included monitoring of the electroencephalogram (EEG, C₃-A₂ or C₄-A₁, O₂-A₁ or O₁-A₂), electro-oculogram (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 (11). Apneas and hypopneas were scored using American Academy of Sleep Medicine Task Force (1999) diagnostic criteria (12, 13). 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 persistence of chest or abdominal respiratory effort. Central events were noted if no displacement occurred on either the chest or abdominal channels. 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) (12).

CPAP adherence

The primary dependent variable of interest was CPAP adherence and was assessed by nightly use of CPAP at the 6-months follow up visit. CPAP use was downloaded from the device and the participants were considered to be adherent if the mean CPAP use was > 4 hours per night at 6-months. Long-term CPAP adherence was measured as self-reported adherence (hours per night) at the time of the DAS administration.

Statistical Analysis 

Statistical analyses were performed using STATA (Version 11, StataCorp TX USA). Univariate and multivariate logistic regression models were used to estimate the degree to which variables correlated with CPAP adherence. We examined the association between CPAP adherence and following variables: OSA severity as measured by the AHI, age, baseline body mass index (BMI, kg/m²), spousal involvement and the DAS. For these models, dichotomous variables were created for OSA severity (AHI < 15 vs. ≥ 15), obesity (BMI <30 kg/m² vs. ≥30 kg/m²) and CPAP adherence (< 4 hours/night vs. ≥4 hours/night). Spousal involvement was ascertained using a 5 point Lickert scale and analyzed as a continuous variable.

To assess predictors of CPAP adherence we used multiple regression models. Unpaired t-tests were used to assess the effect of gender, age, OSA severity, BMI, and CPAP adherence in both the CPAP and Sham CPAP groups. Data for continuous and interval variables were expressed as mean ± SD, and as a percentage for categorical variables. Statistical significance was set at a P value <0.05, two-tailed. The variables that produced P value of < 0.05 were included in the final model.

Results

Baseline demographic data on participants (N=194) who completed the DAS are outlined in Table 1.

Table1. Baseline Characteristics of APPLES Participants Who Completed Dyadic Data.

The majority of the participants were Caucasian (83%) and males (73%), with mean age of 56 years and a mean BMI of 31 kg/m². Over half of the participants had severe OSA (62%). Table 2a demonstrates CPAP adherence at 6 months using multivariate analysis.

Table 2A. Multivariate Analysis of Adherence to CPAP or Sham CPAP at 6 Months.

The CPAP adherence was independently associated with advanced age (p < 0.01) and increasing spousal involvement (p < 0.01). After stratifying by treatment group, the association between CPAP adherence and spousal involvement was seen only amongst the CPAP group (Table 2b).

Table 2B. Multivariate Analysis of Adherence to CPAP at 6 Months.

Adjustment to marriage as reflected by items on the DAS questionnaire, however, was not associated with CPAP adherence.

Notably, after gender stratification, significant association between spousal involvement and CPAP adherence was limited to men alone (p=0.03). Three years after completing APPLES, 82 participants were still adherent by self-report (Table 3).

Table 3. Multivariate Analysis CPAP Adherence 3 years After Completing APPLES Study (based on subjective adherence).

At this time point, spousal involvement was not associated with CPAP adherence even after gender stratification.

Discussion

This multicenter double blind study demonstrates that spousal involvement is important in determining CPAP adherence during the initial treatment period, but has no effect on long-term adherence. Notably, the positive results for adherence were seen only among husbands using CPAP, but there was no effect on wives using CPAP. In line with previous research, we also found that increase in age was associated with greater CPAP adherence among both men and women.

Prior studies have indicated that married versus single, CPAP patients with bed partners, perceived spousal support, and quality of marital relationship all play a role in promoting CPAP adherence (5-8). Although these studies support the idea of social support as a conduit to CPAP adherence, the role of spousal involvement was not clear, sample sizes in the spousal role studies were small, and CPAP users were men, which reduces generalizability.

Baron et al. (3) used a spousal involvement measure, including positive and negative collaboration and one-sided items one week after beginning CPAP treatment (N=23 married men on CPAP), in addition to an interpersonal measure of supportive behaviors at 3 months to evaluate interpersonal qualities (n=16/23 responded). These investigators found that perceived collaborative involvement was related to greater CPAP adherence at 3 months (p=0.002). These findings are similar to our study in that spousal support, at least for husbands on CPAP, fostered greater adherence during the initial period of treatment.

Our observations and those of Baron et al. (14) fit well with the theories of motivation. The fundamental fact of motivation and adherence in healthcare is that individuals cannot be forced to change their behaviors. The behavior change, in this case the CPAP adherence, may be initiated by extrinsic motivation. External motivation may be rewards, punishments, or pressure from other people, such as family members or healthcare providers. However, extrinsic motivation, such as spousal pressure, is less effective in the long-term. In order to sustain long term behavioral change for CPAP adherence one needs to rely on intrinsic motivation which can be strengthened by examining the decisional balance of the ratio between a patient’s perceived pros and cons for engaging in a health behavior. The decisional balance has been found to be predictive of adherence to treatment in a variety of healthcare settings.

Our study also found increased age as an independent predictor of CPAP adherence at 6-months, yet the results were not significant for long-term adherence. Previous studies have also demonstrated conflicting results on the association between age and CPAP adherence. Sin et al. (15) found that a 10 year increment in age resulted in 0.24 ± 0.11-h increase in CPAP use. Alternatively, McArdle and colleagues (16) found that older patients were less likely to use their CPAP machines. Similarly, Janson et al. (17) found older age to be an independent risk factor for discontinuing CPAP treatment, and this finding was thought to be secondary to nasal, or pharyngeal problems. In another study, Russo-Magno et al. (18) found that adherent patients were younger in age compared to non-adherents, and increasing age made CPAP adherence difficult. Cognitive and physical impairments were thought to be contributing to difficulty with CPAP adherence. Mean age in this cohort was 73 years, which was higher than the mean age in our study. It is possible that these inconsistent associations of age on CPAP adherence may be related to the length of follow-up as well. With longer durations, the effect of time on comorbidities in the elderly may make adherence more difficult.

To our knowledge, this is the first study to demonstrate a gender bias in support for CPAP adherence. While men on CPAP were significantly more likely to adhere with support from their wives, there was no such effect for married women on CPAP, suggesting little to no support from their husbands. Although the effect of gender on CPAP adherence and spousal involvement has not been studied, previous research suggests that women are more likely to be the health caregivers in families, and thus exercise more social control (19). It is the social norm and expectation that women are often involved in their husbands’ health. As indicated in the literature regarding type 2 diabetes (20), male patients and their wives shared an expectation that the wives will be involved in their care while female patients and their husbands did not have similar expectations. We can support this finding in relationship to CPAP adherence.

Not surprisingly, spousal support for adherence did not apply to sham CPAP. This suggests that if an intervention is not having any perceived benefit, spousal involvement will have little impact on adherence. 

There are several limitations to this study. A major limitation is self-reported long term CPAP adherence. Additionally, our study was limited to husbands and wives on CPAP completing the DAS; their respective spouses were not asked about their degree of involvement. Moreover, it is unclear which components of spouse involvement played a role in CPAP adherence. We cannot assume that patients welcome all types of spouse involvement. Spouse involvement may be perceived by patients as control and nagging and may not be advantageous for all patients (21). In the context of chronic illness significant differences are demonstrated across couples in expectations for spouse involvement (20).

Despite these limitations, to our knowledge this is the first study of its type that examined spousal support for both men and women on CPAP supporting generalizability of our findings. Other strengths of this study include a large number of participants across multiple sites, randomized CPAP and Sham CPAP control groups, and objective documentation of CPAP adherence at 6 months.

Dyadic coping has been utilized in other health related interventions and can also be used to improve CPAP adherence. Ye et al. (4) has provided a comprehensive review of dyadic support in CPAP adherence, including methodological considerations, recommendations for future research, and implications for interventions. In tandem with the Ye et al. (4) review, our findings, particularly with respect to the need for spousal support of wives on CPAP, can provide a springboard for future clinical/intervention studies to promote CPAP adherence for men and women, to develop gender-relevant training needs to support their spouse on CPAP, and to determine spousal support activities that are the most efficient at promoting CPAP adherence.

Acknowledgments

APPLES 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 yrs), Thomas H. Murray, PhD (1 yr)

References

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16. McArdle N, Kingshott R, Engleman HM, Mackay TW, Douglas NJ. Partners of patients with sleep apnoea/hypopnoea syndrome: effect of CPAP treatment on sleep quality and quality of life. Thorax. 2001;56(7):513-8. [CrossRef] [PubMed]
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18. Russo‐Magno P, O'Brien A, Panciera T, Rounds S. Compliance with CPAP therapy in older men with obstructive sleep apnea. J Am Geriatr Soc. 2001;49(9):1205-11. [CrossRef] [PubMed]
19. Umberson D. Gender, marital status and the social control of health behavior. Soc Sci Med. 1992;34(8):907-17. [CrossRef] [PubMed]
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Cite as: Batool-Anwar S, Baldwin CM, Fass S, Quan SP. Role of spousal involvement in continuous positive airway pressure (CPAP) adherence in patients with obstructive sleep apnea (OSA). Southwest J Pulm Crit Care. 2017;14(5):213-27. doi: https://doi.org/10.13175/swjpcc034-17 PDF

Obstructive Sleep Apnea and Quality of Life: Comparison of the SAQLI, FOSQ, and SF-36 Questionnaires

Graciela E Silva PhD^a

James L Goodwin PhD^b

Kimberly D Vana, DNP, RN, FNP-BC, FNP- C^c

Stuart F Quan MD^b,d,e,f 

^aUniversity of Arizona College of Nursing, Tucson, AZ; ^bArizona Respiratory Center, University of Arizona, Tucson, AZ; ^cCollege of Nursing & Health Innovation, Arizona State University, Phoenix, AZ; ^dCollege of Medicine, University of Arizona, Tucson, AZ; ^eDivision of Sleep Medicine, Harvard Medical School, Boston, MA. ^fDivision of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA

Abstract 

Introduction: The impact of sleep on quality of life (QoL) has been well documented; however, there is a great need for reliable QoL measures for persons with obstructive sleep apnea (OSA). We compared the QoL scores between the 36-Item Short Form of the Medical Outcomes Survey (SF-36), Calgary Sleep Apnea Quality of Life Index (SAQLI), and Functional Outcomes Sleep Questionnaire (FOSQ) in persons with OSA.

Methods: A total of 884 participants from the Sleep Heart Health Study second examination, who completed the SF-36, FOSQ, and SAQLI, and in-home polysomnograms, were included. The apnea hypopnea index (AHI) at 4% desaturation was categorized as no OSA (<5 /hour), mild to moderate OSA (5-30 /hour) and severe OSA (>30 /hour). QoL scores for each questionnaire were determined and compared by OSA severity category and by gender.

Results: Participants were 47.6% male, 49.2% (n=435) had no OSA, 43.2% (n=382) had mild to moderate OSA, and 7.6% (n=67) had severe OSA. Participants with severe OSA were significantly older (mean age = 63.7 years, p <.0001), had higher BMI (mean = 34.3 kg/m², p <.0001) and had lower SF-36 Physical Component scores (PCS) (45.1) than participants with no OSA (48.5) or those with mild to moderate OSA (46.5, p= .006). When analyzed according to gender, no significant differences were found in males for QoL by OSA severity categories. However, females with severe OSA had significantly lower mean scores for the SAQLI (5.4, p= .006), FOSQ (10.9, p= .02), and SF-36 PCS (37.7, p<.0001) compared to females with no OSA (6.0, 11.5, 44.6) and those with mild to moderate OSA (5.9, 11.4, 48, respectively). Females with severe OSA also had significantly higher mean BMI (41.8 kg/m²,) than females with no OSA (26.5 kg/m²) or females with mild to moderate OSA (30.6 kg/m², p<.0001). The SF-36 PCS and Mental Component Scores (MCS) were correlated with the FOSQ and SAQLI (r=.37 PCS vs FOSQ; r=.31 MCS vs FOSQ; r=.42 PCS vs SAQLI; r=.52 MCS vs SAQLI; and r=.66 FOSQ vs SAQLI, p<.001 for all correlations). Linear regression analyses, adjusting for potential confounders, indicated that the impact of OSA severity on QoL is largely explained by the presence of daytime sleepiness. 

Conclusion: The impact of OSA on QoL differs between genders with a larger effect on females and is largely explained by the presence of daytime sleepiness. Correlations among QoL instruments are not high and various instruments may assess different aspects of QoL.

Introduction

Obstructive sleep apnea (OSA) is a highly prevalent condition occurring in as many as 17% and 9% of middle aged males and females, respectively (1). OSA is now recognized as an important risk factor for the development of hypertension and coronary heart disease as well as premature death (2). However, patients frequently present to health care providers with symptoms that are indicative of impairment in their quality of life (QoL). Improvement in QoL is an important determinant of whether patients adhere to continuous positive airway pressure (CPAP), the most commonly prescribed treatment for OSA. Additionally, measurement of QoL is one of the quality metrics recently developed for use in clinical practice (3) thus increasing the importance of evaluating tools used to assess QoL in OSA.

A variety of tools to measure QoL have been utilized in epidemiologic studies and clinical trials of OSA. The most common general QoL instrument used has been the Medical Outcomes Study Short-Form Health Survey SF-36 (4). More recently, two sleep specific QoL questionnaires have been developed, the Functional Outcomes of Sleep Questionnaire (FOSQ) (5) and the Sleep Apnea Quality of Life Inventory (SAQLI) (6). Whether these sleep specific QoL instruments are more sensitive in those with OSA than general QoL questionnaires is not clear. Furthermore, there have been few comparisons of the FOSQ to the SAQLI with respect to their sensitivity in those with OSA and whether QoL differs between males and females. Using data from a large cohort study, the purposes of these analyses were to compare these instruments to each other, to assess whether they were able to detect differences in QoL among groups with different severities of OSA and to determine whether there were differences between genders.

Methods

The Sleep Heart Health Study (SHHS) is a prospective multicenter cohort study designed to investigate the relationship between OSA and cardiovascular diseases in the United States. Details of the study design have been published elsewhere (7). Briefly, initial baseline recruitment began in 1995, enrolling 6,441 subjects, 40 years of age and older, from several ongoing geographically distinct cardiovascular and respiratory disease cohorts who were initially assembled between 1976 and 1995 (8). 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. A SHHS follow-up examination took place between February 2000 and May 2003, enrolling 4,586 of the original participants who had a repeat polysomnogram in addition to completing questionnaires and undergoing other measurements. The present study focused on 884 participants from the Tucson and Framingham sites of the Sleep Heart Health Study second examination in whom data were available from the sleep habits questionnaire, all quality of life questionnaires, and in-home polysomnograms. Data was limited to these sites because administration of the FOSQ was not done at the other field centers. 

The SHHS was approved by the respective institutional review boards for human subjects research, and informed written consent was obtained from all subjects at the time of their enrollment into each stage of the study.

Polysomnography

Participants underwent overnight in-home polysomnograms using the Compumedics Portable PS-2 System (Abbottsville, Victoria, Australia) administered by trained technicians (9). Briefly, after a home visit was scheduled, the Sleep Health Questionnaire, SF-36, SAQLI, and FOSQ questionnaires generally were mailed 1 to 2 weeks prior to the in-home polysomnography appointment. Each participant was asked to complete the questionnaire before the home visit, at which time the questionnaires were collected and verified for completeness. The home visits were performed by two-person, mixed-sex teams in visits that lasted 1.5 to 2 hours. There was emphasis on making the night of the polysomnographic assessment as representative as possible of a usual night of sleep. Participants were asked to schedule the visit so that it would occur approximately two hours prior to their usual bedtime. Participants’ weekday or weekend bedtime routines were encouraged to be consistent with the day of the week that the visits were made.

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 (9, 10). In brief, sleep was scored according to guidelines developed by Rechtschaffen and Kales (11, 12). Strict protocols were maintained to ensure comparability among centers and technicians. Intra-scorer and inter-scorer reliabilities were high (10). As in previous analyses of SHHS data, 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. 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 and did not meet the criteria for apnea. For this study, only apneas or hypopneas associated with a 4% or greater oxyhemoglobin desaturation were considered in the calculation of the apnea hypopnea index (AHI, apneas plus hypopneas per hour of total sleep time).

Sleep Habits Questionnaire and Covariates

Participants completed the SHHS Sleep Habits Questionnaire (13). The Sleep Habits Questionnaire contained questions regarding sleep habits. Height and weight were measured directly to determine body mass index (BMI, kg/m2). Sex and ethnicity were derived from data obtained from the SHHS parent cohorts. Participants answered yes or no to having a healthcare provider diagnosing them as having chronic obstructive pulmonary disease (COPD), chronic bronchitis, or asthma.

Sleepiness

The level of daytime sleepiness was determined using the Epworth Sleepiness Scale (ESS), a validated 8-item questionnaire that measures subjective sleepiness (14). Subjects were asked to rate how likely they are to fall asleep in different situations. Each question was answered on a scale of 0 to 3. ESS values ranged from 0 (unlikely to fall asleep in any situation) to 24 (high chance of falling asleep in all 8 situations). Mean ESS scores between 14 and 16 have been reported for patients with OSA (14, 15). Scores of 11 or greater are considered to represent an abnormal degree of daytime sleepiness (16). Sleepiness was defined as an ESS of at least 10.

Quality of Life Measures

Medical Outcomes Study Short-Form Health Survey (SF-36). Quality of life was evaluated using the Medical Outcomes Study Short-Form Health survey (SF-36) (4). The SF-36 is a multipurpose self-administered health survey consisting of 36 questions divided into 8 individual domains: (1) physical functioning (limitations in physical activity because of health problems), (2) role physical (limitations in usual role activities because of physical health problems), (3) bodily pain, (4) general health perceptions; (5) vitality (energy and fatigue), (6) social functioning (limitation in social activities because of physical or emotional problems), (7) role emotional (limitation in usual role activities because of emotional problems), and (8) general mental health. In addition, the 8 scales are used to form 2 distinct high-order summary scales: the physical component summary (PCS) and the mental component summary (MCS) (17). The PCS includes the physical functioning, role physical, bodily pain, and general health scales, and the MCS includes the vitality, social functioning, role emotional, and general mental health scales. The raw scores for each subscale and the 2 summary measures are standardized, weighted, and scored according to specific algorithms. The scores for the multifunction item scales and the summary measures range from 0 to 100, with higher scores indicating better quality of life. For the present study, we use only the PCS and MCS scales.

Functional Outcomes Sleep Questionnaire (FOSQ). The FOSQ was developed as a self-report instrument to assess the disorders of sleepiness on quality of life. It consists of 30 items with 5 factor-based subscales: activity level, vigilance, intimacy and sexual relationships, general productivity and social outcome. A mean weighted item score is obtained for each subscale. The subscales are summed to produce a global score (5). In SHHS, questions related to sexual intimacy were omitted because there were concerns that some participants would find these embarrassing or offensive.

Sleep Apnea Quality of Life Index (SAQLI). The SAQLI was developed as a sleep apnea specific quality of life instrument (6). 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 better quality of life. In SHHS, the short form of the SAQLI was administered, because it allowed for self-completion by the participants (18).

Statistical Analysis

Differences in proportions for descriptive characteristics between OSA severity categories, and categorical variables were analyzed using Chi-square tests with 2 degrees of freedom. Fisher’s exact test was used when the expected frequency was less than 5 in any cell. One-way analyses of variance (ANOVA) were used to compare differences in mean values for continuous variables (BMI, total sleep time, SAQLI, FOSQ, SF-36 MCS, and SF-36 PCS) by OSA severity categories and by these categories separately for males and females. Pearson’s correlations were used to test for correlation coefficients between the four quality of life scales, SAQLI, FOSQ, SF-36 MCS, and SF-36 PCS.

Separate multivariate linear regression models were fitted to evaluate scores from each of the four QoL scales by OSA categories for males and females. Potential confounders (age, race, COPD, chronic bronchitis, ESS and asthma) were evaluated and adjusted for in the models; only those variables with significant coefficients were kept in the models. Thus, OSA severity, ESS, and asthma were the only variables retained in the final models. All statistical tests were performed using statistical software (Stata SE, version 13.0 for Windows; Stata Corp; College Station, TX) and a significance level of 0.05.

Results

Participants were 47.6% male and 52.4% female, 49.2% (n=435) had no OSA, 43.2% (n=382) had mild to moderate OSA, and 7.6% (n=67) had severe OSA. Approximately 21% of participants with mild to moderate OSA and 39% of those with severe OSA reported excessive daytime sleepiness (ESS >10) (Table 1).

Participants with severe OSA were significantly older (mean age = 63.7 years, p <.0001), had higher BMI (mean = 34.3 kg/m², p <.0001) and had lower SF-36 PCS scores (45.1, p= .006) than participants with no OSA or those with mild to moderate OSA. There was also a trend towards lower scores on the MCS of the SF-36, the SAQLI, and the FOSQ (Table 2).

When analyzed according to gender, no significant differences were found in males for QoL by OSA severity categories (Table 3).

Males with severe OSA had significantly higher BMI (mean 31.9, p<.0001) than males with no OSA or males with mild to moderate OSA. However, as shown in Table 4, females with severe OSA had significantly lower mean scores for the SAQLI (5.4, p= .006), FOSQ (10.9, p= .02), and SF-36 PCS (37.7, p<.0001) compared to females with no OSA and those with mild to moderate OSA.

Females with severe OSA also had significantly higher BMI (mean 41.8, p<.0001) than females with no OSA or females with mild to moderate OSA.

As shown in Table 5, comparisons between the QoL measures showed small correlations between the FOSQ and the SF-36 MCS (r=.31, p < .001) and the SF-36 PCS (r=.37, p <.001), and medium correlations between the SAQLI and the SF-36 MCS (r=0.52, p <.001) and the SF-36 PCS (r=.42, p < .001).

The correlation between the SAQLI and FOSQ was 0.66, p <.001, and the correlation between SF-36 MCS and SF-36 PCS was -.024, however this was not significant (p = .142). In addition, ESS scores were inversely correlated with the SAQLI (r = -.36), FOSQ (r = -.43), MCS (r = -.17), and PCS (r = -.16) (data not shown).

Because categorical analyses showed no difference for males in QoL scores, we, therefore, ran linear regression models separately for females and males (Table 6).

 

Discussion

In these analyses using a general (SF-36) and two sleep specific QoL assessment tools (FOSQ and SAQLI), we found that QoL was reduced in those with severe OSA; substantial differences were not apparent among participants with mild to moderate OSA and those with no OSA. However, there were significant gender disparities. Females with severe OSA demonstrated a substantial reduction in QoL with all instruments, but there was a lack of differences among males by OSA severity. The reductions in QoL were explained primarily by the presence of sleepiness. Furthermore, correlations among QoL questionnaires were modest at best, indicating that they assess different QoL domains.

When males and females were analyzed together in our study, only the PCS of the SF-36 showed a significant reduction in QoL in participants with OSA, but this was limited solely to participants with severe OSA. Additional studies also have found lower QoL only in those with severe OSA (19, 20). Moreover, other studies have failed to find any differences in QoL among participants with a broad spectrum of OSA severity (21-23). In one of these studies, Lee and colleagues (22) found that the AHI was not associated with differences in the PCS or MCS of the SF-36 in a large group of patients seen in a sleep clinic. In their study, other factors, such as age, gender, minimum oxygen saturation, sleepiness, and depression were associated with the PCS or MCS scores. Our study also found a strong trend between sleepiness and QoL scores for females and males. Similarly, in a smaller study, Lee et al. (22) did not find differences in the SAQLI among OSA patients of different severities. Our data also are consistent with a previous analysis from the first examination of SHHS in which severe OSA was associated with worse QoL on most subscales of the SF-36, but only the vitality subscale was reflective of poorer QoL in participants with OSA of less severity. In contrast, even mild OSA was associated with reduced QoL in comparison to no OSA among the middle-aged males and females of the Wisconsin Sleep Cohort (24). However, our cohort was older than participants in the Wisconsin Sleep Cohort and only a small sample from the SHHS was analyzed in the present study. Thus, age and other demographic differences among the cohorts may provide explanations for these discrepancies. Nevertheless, despite the absence of large cross-sectional differences in QoL as a function of OSA severity, in most studies, the SF-36, SAQLI, and FOSQ have been shown to be sensitive to changes in QoL after OSA treatment.

When analysis of our data was performed separately according to gender, we observed that the reduction in QoL with severe OSA was limited to females irrespective of the QoL instrument. Other studies (22) also have noted that QoL in participants with OSA is worse in women. However, in a study of a large cohort of males, Appleton et al.,(25) found that increasing AHI was associated with lower QoL on the SF-36, but only in those less than 69 years of age. The median age of the SHHS cohort is 60 years with substantial numbers of participants older than 70 years.  Thus, our results and those of Appleton et al. (25) may not be discrepant necessarily.

Excessive daytime sleepiness is one of the most common symptoms in OSA, and sleepiness can have a profound negative impact on QoL. Thus, not surprisingly, our multivariate analyses demonstrated that the negative impact of severe OSA was explained primarily by the presence of sleepiness. Our finding is consistent with the findings of some, (19, 22, 23, 26) but not all previous studies (27). The explanation for these inconsistent findings is not readily apparent, but possibilities include whether study populations were recruited from the general population or clinic, as well as whether the cohorts had other co-morbidities that would impact QoL. A differential perception of sleepiness between males and females offers a possible explanation of the greater impact of OSA on QoL in the latter. However, this assertion seems unlikely inasmuch as previous studies indicate females with OSA are more likely to report fatigue rather than sleepiness (28-30).

We observed that correlations among the SF-36, SAQLI, and FOSQ were relatively weak to moderate. Our results are consistent with the few studies that have done similar comparisons. In a Spanish multicenter study (21), correlations of the FOSQ and several scales of the SF-36 with the 4 domains of the SAQLI were poor to moderate. They ranged from r=.179 between the FOSQ and SAQLI Emotional Functioning domain to r=.579 for the SF-36 Vitality and SAQLI Daily Functioning domain. In a Polish study (31), the correlation between the SF-36 and the FOSQ was r=.46 and between the SF-36 and the SAQLI was r=.47. Other studies have compared the SF-36 to other general QoL instruments in patients with OSA, with some, but not all, demonstrating reasonable correspondence (32, 33).  Considering our results with other studies, various instruments may sample different aspects of QoL. Care should be exercised when selecting a tool to assess health outcomes in OSA.

There are several important limitations to our findings. First, the SHHS cohort was recruited from participants enrolled in other longitudinal studies, many of whom were long-time participants. These individuals may represent a group of survivors who would generally have better QoL regardless of OSA-severity status. Second, as a group, the SHHS cohort is older (mean age = 61.6 years) and may not be representative of the US adult population. Third, SHHS is a general population cohort, and thus, unlike a clinical cohort, some did not have symptoms of OSA. Finally, severity of OSA may not be best reflected by the AHI. Other markers of severity such as amount of oxygen desaturation or degree of sleep fragmentation may be better surrogates to show differences in QoL. Nevertheless, despite these limitations, our analyses have some unique qualities such as a well-characterized, racially and ethnically diverse cohort, use of home-based polysomnography to assess the severity of OSA, and data related to QoL derived from 3 different instruments.

In conclusion, in a middle-aged to elderly cohort, QoL is poorer only in females with severe OSA. To a large extent, these findings can be explained by the presence of daytime sleepiness. Correlations among 3 commonly used QoL instruments used in persons with OSA were weak to moderate, suggesting that each samples different aspects of QoL. Therefore, care should be exercised in selecting a QoL tool for documenting health care outcomes for research or clinical care.

Acknowledgments

The Sleep Heart Health Study (SHHS) acknowledges the Atherosclerosis Risk in Communities Study (ARIC), the Cardiovascular Health Study (CHS), the Framingham Heart Study (FHS), the Cornell/Mt. Sinai Worksite and Hypertension Studies, the Strong Heart Study (SHS), the Tucson Epidemiologic Study of Airways Obstructive Diseases (TES) and the Tucson Health and Environment Study (H&E) for allowing their cohort members to be part of the SHHS and for permitting data acquired by them to be used in the study.  SHHS is particularly grateful to the members of these cohorts who agreed to participate in SHHS as well. SHHS further recognizes all of 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, http://www.jhucct.com/shhs/

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

This work was supported by HL U01HL53940 (University of Washington), U01HL53941 (Boston University), U01HL53938 and U01HL53938-07S (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).

Dr. Silva was supported by NHLBI grant HL 062373-05A2. 

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Cite as: Silva GE, Goodwin JL, Vana KD, Quan SF. Obstructive sleep apnea and quality of life: comparison of the SAQLI, FOSQ, and SF-36 questionnaires. Southwest J Pulm Crit Care. 2016;13(3):137-49. doi: http://dx.doi.org/10.13175/swjpcc082-16 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

Obstructive Sleep Apnea and Cardiovascular Disease: Back and Forward in Time Over the Last 25 Years

Stuart F. Quan, M.D.

Division of Sleep Medicine, Harvard Medical School and Brigham Womens Hospital, Boston, MA; Arizona Respiratory Center, University of Arizona College of Medicine, Tucson, AZ

Abstract

Over the past 25 years, there have been significant advances made in understanding the pathophysiology and cardiovascular consequences of obstructive sleep apnea (OSA). Substantial evidence now implicates OSA as an independent risk factor for the development of hypertension, coronary artery disease, congestive heart failure and stroke, as well as increased risk of death. Pathophysiologic mechanisms include release of inflammatory mediators, oxidative stress, metabolic dysfunction, hypercoagulability and endothelial dysfunction. Although non-randomized intervention studies suggest that treatment of OSA with continuous positive airway pressure may mitigate its impact of the development of cardiovascular disease, randomized clinical trials are lacking.

Introduction

The first report of the physiologic events occurring in obstructive sleep apnea (OSA) was published in 1965 by Gastaut and colleagues (1). However, literary and historical accounts of what most likely was OSA have existed since antiquity (2). Most well-known of these is the description of Burwell’s case of a man falling asleep while playing poker. This led to the widespread use of the “Pickwickian Syndrome” because of the similarity of the patient’s symptoms to the literary imagery of “Joe, the fat boy”, a character in Charles Dicken’s Posthumous Papers of the Pickwick Club (3). In the 50 years since Gastaut’s original description of OSA, there has been an exponential growth in the recognition that it is a clinical condition with a high prevalence, subtle to disabling clinical symptoms, and more recently, substantial cardiovascular morbidity and mortality. However, much of the progress in our understanding of OSA with respect to cardiovascular disease has occurred in the past 25 years. Thus, it is appropriate to look back to where we were 25 years ago, our current state of knowledge and identify avenues for future research. Before doing so, however, one must examine whether there is sufficient evidence suggest that a biologic association between OSA and cardiovascular disease is plausible.           

Obstructive Sleep Apnea and Cardiovascular Disease: Biologic Plausibility

Obstructive sleep apnea is characterized by repetitive episodes of occlusion or near occlusion of the upper airway at the level of the pharynx despite increasing inspiratory efforts (4). These episodes are terminated by brief arousals from sleep resulting in sleep fragmentation. There are a number of physiologic consequences to what essentially are repetitive involuntary Müeller maneuvers (5,6). With cessation or near cessation of airflow, transient oxygen desaturation and hypercarbia occur (4,6). Inspiratory efforts against an occluded airway result in large intrathoracic pressure swings (5,6). As a result, there is increased sympathetic nervous system activity (7), fluctuations in parasympathetic tone (8), large cyclical changes in heart rate, arterial and pulmonary vasoconstriction and hypertension, and increases in cardiac preload and afterload (9-11). Given the chronicity of OSA, it is not difficulty project that such physiologic changes might lead to daytime cardiovascular dysfunction. Indeed, daytime hypertension has been induced in a canine model of OSA (12) as well as in rodent models of intermittent hypoxia (13). Thus, there are potential biologic mechanisms to explain why OSA might be an independent risk factor for cardiovascular disease.

Obstructive Sleep Apnea and Cardiovascular Disease: Circa 1970s-1980s

In the 1980’s, studies in Europe found cross-sectional associations between snoring as a surrogate for OSA and both hypertension and cardiovascular disease (14,15). Slightly earlier, evidence also was emerging of an association between obstructive sleep apnea and hypertension. These studies noted a greater than 50% prevalence of hypertension among patients with OSA (16,17) and conversely, a 20-30% prevalence of OSA was observed in patients with hypertension (18,19). Furthermore, several case control and cohort studies indicated that the risk of stroke or heart disease was 2.1 to 10.3 fold greater for those with snoring (20-23). Cardiac arrhythmias were frequently noted to occur in OSA patients as well (24).

Later during this time period, there were 3 retrospective studies analyzing the relationship between OSA and mortality. From the Henry Ford Sleep Disorders Center, the vital status of 385 male patients with OSA studied with polysomnography between 1978 and 1986 was determined. Mortality was significantly greater in those with an apnea index (not apnea hypopnea index [AHI]) greater than 20 events/hour. Uvulopalatopharyngoplasty did not attenuate the mortality rate, but better survival was reported in those who had a tracheostomy or were prescribed nasal continuous positive airway pressure (CPAP) (25). At the same time, the Stanford Sleep Disorders Center analyzed their experience in 198 patients who had either a tracheostomy (n=71) or were treated conservatively with a recommendation for weight loss (n=127).  There were 14 deaths of which 8 were from stroke or myocardial infarction over a 5 year follow-up period.  All occurred in the conservatively treated group (26). In contrast, another series from the University of Florida failed to find any mortality differences between 91 treated and untreated OSA patients and 35 patients with symptoms consistent with OSA, but negative findings on polysomnography over a 7-98 month follow-up (27).

Given the known acute effects of repetitive obstruction of the upper airway on systemic and pulmonary blood pressure, and heart rate, as well as the recurrent episodes of hypoxemia (11), linkages between these physiologic findings and the development of cardiovascular disease were proposed (28). One such pathophysiologic pathway highlighted important roles for hypoxemia and increased sympathetic activity, both of which are currently considered important mechanistic factors for the development of cardiovascular disease (28).

Obstructive Sleep Apnea And Cardiovascular Disease 2012: Epidemiology

Starting in the mid 1990’s, important observations were made that solidified linkages between OSA and cardiovascular disease. In the Wisconsin Sleep Cohort, the first large prospective population-based cohort study to use polysomnography to confirm the presence of OSA, Peppard et al. (29) demonstrated that OSA was an independent risk factor for the development of hypertension. Furthermore, the risk progressively increased with greater levels of OSA severity (OR: 1.42, 2.03, 2.89 [AHI: <5, 5-<15, >15 /hour vs. referent=0]) (29). These findings were subsequently confirmed in the Sleep Heart Health Study (OR: 1.13, 1.54, 2.119 [AHI: 5-<15, 15-<30, >30 /hour vs. referent=<5], although they were significantly attenuated when body mass index (BMI) was included in the analytic models (30). In addition, the development of hypertension was found to be associated with the presence of nocturnal hypoxemia. A more recent prospective study from a clinical cohort showed similar findings (31). Moreover, a number of studies have shown that blood pressure will decrease after treatment of OSA with continuous positive airway pressure (32). Although the magnitude of improvement in large clinical trials is only 2-3 mm Hg, such changes are large from an epidemiologic and public health perspective, and may be greater in individual patients and those with resistant hypertension (33). Given this evidence, the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure has concluded that OSA is an identifiable cause for the development of hypertension (34).

Evidence now indicates that OSA is an independent risk factor for the development of coronary heart disease (CHD). In a Spanish clinical cohort of only men, there was an increased risk of incident CHD over a 12 year follow-up period (35). Subsequently, this observation was confirmed in the Sleep Heart Health Study (36). In this latter study, however, increased risk (Hazard Ratio: 1.75 for AHI > 30 /hour vs. AHI < 5) was only observed in men less than 70 years of age. It is possible that the absence of an impact of OSA on risk of CHD in older men is related to a “healthy survivor” effect (37). Thus, whether OSA increases the likelihood of developing CHD in older men and women remains unclear.

In addition to findings linking OSA to greater risk of incident CHD, OSA appears to enhance the likelihood of new events in those with prevalent CHD. In a study of 407 consecutive patients with CHD, those with an oxygen desaturation index greater than 5 per hour had a 70% relative increase and a 10.7% absolute increase in the composite endpoint (cerebrovascular event, myocardial infarction or death) (38). In another study, 89 patients who had undergone percutaneous coronary intervention had polysomnography with OSA found in 51. In comparison to those who did not have OSA, 23.5% vs. 5.3% had a major adverse coronary event (cardiac death, reinfarction, and target vessel revascularization) in the ensuing follow-up period (mean=227 days) (39).

Sleep apnea, in particular central sleep apnea, frequently is observed in association with congestive heart failure (CHF). However, recent data from the Sleep Heart Health Study suggest that severe OSA is a risk factor for incident CHF in men (Hazard Ratio: 1.71 for AHI > 30 /hour vs. AHI < 5), but not women (36).

Recent observations indicate that stroke incidence also may be increased in those with OSA. In the Wisconsin Sleep Cohort over a 4 year follow-up, a significantly increased odds ratio for incident stroke after age and sex adjustment of 4.48 was observed in those with an AHI > 20 /hour (40). This was attenuated to 3.08 after controlling for body mass index. In the Sleep Heart Health Study, a significant increase in incident stroke risk also was found in those with an AHI > 20 /hour, but only in men (41).

As initially reported over 25 years ago (24), more recent observations have confirmed an association between OSA and cardiac arrhythmias. In the Sleep Heart Health Study, those with severe OSA were more likely to have both ventricular and atrial ectopy (42). Furthermore, those with severe OSA were 4.5 and 1.8 times more likely to have episodes of atrial fibrillation, and complex ventricular ectopy or non-sustained ventricular tachycardia.42 Additional analyses reveal that the relative risk of having an episodes of atrial fibrillation is 17 times greater after an apnea or hypopnea episode and there is 1 excess episode of paroxysmal atrial fibrillation or nonsustained ventricular tachycardia for every 1000 hours of sleep or 40000 respiratory disturbances (43). Obstructive sleep apnea with attendant hypoxemia also may be a risk factor for recurrence of atrial fibrillation after cardioversion (44).

Finally, there is now relatively conclusive evidence from 3 longitudinal cohort studies demonstrating that OSA contributes to excess mortality. In the Busselton Health Study of 380 individuals followed for a mean of 13.4 years, a respiratory disturbance index of greater than 15 /hour yielded a hazard ratio of 6.24 for excess mortality (45). Subsequently, in an 18 year follow-up of 1496 participants in the Wisconsin Sleep Cohort, the adjusted hazard ratio for excess all cause mortality related to severe OSA was 3.0 in comparison to no OSA (46). Furthermore, the hazard ratio related to cardiovascular disease mortality was 5.2 (46). More recently, the Sleep Heart Health Study reported a hazard ratio of 1.46 for all cause mortality over an 8.2 year average follow-up. Similar to the Wisconsin Sleep Cohort, it appeared that cardiovascular deaths accounted for much of the excess risk (47). In this latter study, indices of nocturnal hypoxemia also were associated with excess all cause mortality. Although sudden cardiac death in the general population usually occurs during the 6 am to 12 noon time frame, in those with OSA, it is shifted to night-time hours, 12 midnight to 6 am providing further evidence of the adverse impact of OSA on the heart (48).

Obstructive Sleep Apnea and Cardiovascular Disease: Mechanistic Observations

Since the initial observations of the physiologic events that might be operative in the pathogenesis of cardiovascular sequelae of OSA over 25 years ago, substantial progress has been made towards understanding how OSA is a risk factor for cardiovascular disease. These findings include OSA induced changes in cardiac structure and function, abnormalities in metabolic function, and increases in inflammation, coagulability and sympathetic nervous system activity. These latter issues then interact to enhance atherogenesis and cardiac dysfunction.

Obstructive sleep apnea is associated with increases in left ventricular mass. In the Sleep Heart Health Study, those with an AHI > 30 /hour in comparison to those with an AHI < 5 were more likely to have left ventricular hypertrophy on echocardiography, and estimates of left ventricular mass were higher (49). These associations were even stronger when indices of nocturnal hypoxemia were used instead of the AHI. This further highlights the potential role of hypoxemia in the pathogenesis of cardiovascular disease attributable to OSA. Given the presence of left ventricular hypertrophy related to OSA, it is not surprising that diastolic dysfunction is more common among individuals with OSA (50). However, use of CPAP may improve left ventricular function (50). This raises the possibility that early intervention to treat OSA may reduce cardiac morbidity and mortality.    

A number of studies have demonstrated that OSA is associated with metabolic abnormalities. For example, in the Sleep Heart Health Study, levels of cholesterol and triglycerides increased as a function of increasing OSA severity (51). These findings may be related to OSA induced intermittent hypoxia (52). Furthermore, the prevalence of metabolic syndrome is higher among persons with OSA in comparison to those without OSA (53). This finding appears to be driven primarily by the higher frequency of hypertension in persons with OSA. Whether these findings are causally related to OSA remains to be determined. However, OSA might potentially increase the risk of CHD by promoting dyslipidemia.

The prevalence of OSA among persons with type 2 diabetes mellitus is high with one study observing that 86% of obese type 2 diabetics had an AHI >5 /hour, indicative of mild OSA (54). Therefore, it is not surprising that considerable evidence now implicates OSA as a determinant of glucose regulation. Both cross-sectional and prospective studies have demonstrated that OSA is a risk factor for glucose dysregulation and in some studies incident diabetes mellitus (55). Furthermore, some studies have demonstrated that treatment of OSA with CPAP results in improved glucose control (55). Data suggest that OSA induced hypoxemia may be a causative mechanism (56). The close association between OSA and type 2 diabetes mellitus raises the distinct possibility that a positive interaction exists to increase the risk of CHD in persons with both conditions.

It is generally accepted that obesity is a risk factor for the development of OSA (57). However, a few studies have reported that reverse causality may be present such that OSA promotes weight gain (58,59). Recent data from the Sleep Heart Health Study support this hypothesis. Over an approximate 5 year follow-up, weight gain was greater among those with an AHI > 15 /hour in comparison to those with an AHI < 5 /hour (60). Thus, promotion of weight gain may be another mechanism by which OSA increases cardiovascular risk.

Especially in those with severe OSA, recurring apneas and hypopneas result in repetitive episodes of hypoxia and reoxygenation. This produces oxidative stress leading to an increase in the flux of free radicals, induction of endothelin expression, suppression of nitric oxide generation, local vasoconstriction and changes in vascular permeability (61). All of these effects have the potential of enhancing the development of cardiovascular disease.

Substantial data now is available demonstrating that OSA is associated with release of a number of inflammatory mediators such as IL6, sIL6R, IL-8, TNFα, CRP and NF-Kappa β (62). In addition, there is evidence for elevated levels of pro-thrombotic factors such as PAI-1, P-selectin, fibrinogen and VEGF in persons with OSA (62). With the recent findings of the importance of inflammation and thrombosis in the pathogenesis of cardiovascular disease, these observations may be important causal mechanistic links that lead OSA to cardiovascular disease.

Using observations from 25 years ago in combination with current data, a plausible pathogenic pathway from OSA to cardiovascular can be summarized as follows (Figure 1). Recurrent episodes of apnea and hypopnea lead to intermittent hypoxia, increased sympathetic activity, hypercapnia, sleep fragmentation with arousals and large swings in intrathoracic pressure. These physiologic perturbations result in increased oxidative stress, release of inflammatory mediators, metabolic dysfunction, weight gain, hypercoagulability, glucose dysregulation and endothelial dysfunction All of these mechanisms can lead to the development of hypertension, diabetes mellitus, CHD, CHF, stroke and increased risk of death.

Figure 1. Proposed mechanisms leading from physiologic alterations occurring during obstructive sleep apnea and hypopnea to the development of cardiovascular disease.

Obstructive Sleep Apnea and Cardiovascular Disease: Knowledge Gaps

Although significant advances have been made in our understanding of the relationship between OSA and CVD in the past 25 years, there are a number of important areas which require further investigation. With respect to OSA and hypertension, it remains unclear whether treatment of OSA reduces the risk of developing hypertension. Most studies to date have used non-randomized cohorts. In the most recently published randomized controlled trial, CPAP treatment did not decrease the incidence of hypertension in nonsleepy subjects over a median 4 year follow-up (63). However, post-hoc analyses did suggest an effect in subjects who were compliant with CPAP for more than 4 hours per night (63). Furthermore, if CPAP or other treatments for OSA are beneficial in reducing CVD risk, are there subsets of the population for whom it is more advantageous?

As to the impact of OSA on CVD and stroke, there also have not been any published large scale clinical trials demonstrating an impact of treatment on changing the incidence of disease. Similar to hypertension, published studies are from non-randomized cohorts. However, there are several large clinical trials such as Randomized Intervention with CPAP in Coronary Artery Disease and Sleep Apnoea (RICCADSA), Sleep Apnea Cardiovascular Endpoints Study (SAVE), and Heart Biomarker Evaluation in Apnea Treatment study (HeartBEAT). In the RICCADSA study, 400 CAD participants will be randomized to one of 4 groups: 1) non-sleepy with OSA treated with CPAP, 2) non-sleepy with OSA and no CPAP treatment, 3) sleepy with OSA treated with CPAP, 4) CAD but no OSA. The participants will be followed for 3 years for CVD morbidity and mortality (64).  In the multinational SAVE trial, participants with OSA at high risk for CVD will be randomized to CPAP or conventional medical therapy, and followed for 3-5 years (65). Because of the length of follow-up required and expense, and some would argue the ethical dilemmas in performing a long-term interventional trial, studies such as the recently completed HeartBEAT have attempted to assess intermediate outcomes. In the HeartBEAT trial, 270 subjects with CHD or at high risk for CHD were randomized to healthy lifestyle instruction, CPAP or nocturnal oxygen with a primary endpoint of 24 hour blood pressure (66). Whether findings from trials using intermediate endpoints will be predictive of an impact on “hard” endpoints such as incident myocardial infarction or stroke remains to be determined.

Conclusions

Substantial progress has been made in the past 25-30 years in our understanding of the relationship between OSA and CVD. Accumulating evidence implicates OSA as an independent risk factor for hypertension, CHD and stroke. However, the risk may not be the same for all segments of the population. A variety of mechanisms may be operative. Non-randomized trials suggest that treatment appears to mitigate the risk in some clinical populations, but it is unclear whether treatment is beneficial in patients without symptoms. Large scale randomized clinical trials are needed to clearly demonstrate that current treatment modalities for OSA can mitigate CVD risk and to delineate which populations will accrue the most benefit.

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Contact Information:

Stuart F. Quan, M.D.

Division of Sleep Medicine

Harvard Medical School

401 Park Dr., 2^nd Floor East

Boston, MA 02215

Voice: 617-998-8842

Fax: 617-998-8823

Email: Stuart_Quan@hms.harvard.edu

Conflicts of Interest: The author does not have any conflict of interests pertinent to the subject matter of this review.

Reference as: Quan SF. Obstructive sleep apnea and cardiovascular disease: back and forward in time over the last 25 years. Southwest J Pulm Crit Care 2012;5:206-17. 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)

The Impact of Sleep-Disordered Breathing on Body Mass Index (BMI): The Sleep Heart Health Study (SHHS)

Mark A. Brown, M.D. ¹

James L. Goodwin, Ph.D.²

Graciela E. Silva, Ph.D, MPH.³

Ajay Behari, M.D.⁴

Anne B. Newman, M.D., M.P.H^5,6

Naresh M. Punjabi, M.D., Ph.D.⁷

Helaine E. Resnick, Ph.D., M.P.H.⁸

John A. Robbins, M.D., M.S.H.⁹

Stuart F. Quan, M.D.^2,10

¹Department of Psychiatry, Kaiser Permanente, Portland, OR (markbrownmd@gmail.com); 

²Sleep and Arizona Respiratory Centers, University of Arizona College of Medicine, Tucson, AZ(jamieg@arc.arizona.edu);

³College of Nursing & Health Innovation, Arizona State University, Tempe, AZ (Graciela.Silva@asu.edu); 

⁴Pulmonary and Critical Care Associates of Baltimore, Baltimore, MD (ajaybehari@yahoo.com);

⁵Graduate School of Public Health, Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA

⁶Division of Geriatric Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA (NewmanA@edc.pitt.edu);

⁷Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (npunjabi@jhmi.edu);

⁸American Association of Homes and Services for the Aging, Washington, DC (heresnick@gmail.com); 

⁹Center for HealthCare Policy and Research, University of California, Davis, Sacramento, CA (jarobbins@ucdavis.edu);

¹⁰Division of Sleep Medicine, Harvard Medical School, Boston, MA (squan@arc.arizona.edu)

Address for correspondence and reprint requests: Stuart F. Quan, M.D., Division of Sleep Medicine, Harvard Medical School, 401 Park Dr., 2^nd Floor East, Boston, MA 02215, Tel (617) 998-8842, Fax (617) 998-8823, Email: squan@arc.arizona.edu

Conflict of Interest Statement: None of the authors have conflicts of interest pertinent to the subject matter of this manuscript. 

Reference as: Brown MA, Goodwin JL, Silva GE, Behari A, Newman AB, Punjabi NM, Resnick HE, Robbins JA, Quan SF. The impact of sleep-disordered breathing on body mass index (BMI): the sleep heart health study (SHHS). Southwest J Pulm Crit Care 2011;3:159-68. (Click here for PDF version of the manuscript)

Abstract

Introduction: It is well known that obesity is a risk factor for sleep-disordered breathing (SDB). However, whether SDB predicts increase in BMI is not well defined. Data from the Sleep Heart Health Study (SHHS) were analyzed to determine whether SDB predicts longitudinal increase in BMI, adjusted for confounding factors.

Methods: A full-montage unattended home polysomnogram (PSG) and body anthropometric measurements were obtained approximately five years apart in 3001 participants. Apnea-hypopnea index (AHI) was categorized using clinical thresholds: < 5 (normal), ≥ 5 to <15 (mild sleep apnea), and ³ 15 (moderate to severe sleep apnea). Linear regression was used to examine the association between the three AHI groups and increased BMI. The model included age, gender, race, baseline BMI, and change in AHI as covariates.

Results: Mean (SD) age was 62.2 years (10.14), 55.2% were female and 76.1% were Caucasian. Five-year increase in BMI was modest with a mean (SD) change of 0.53 (2.62) kg/m² (p=0.071). A multivariate regression model showed that subjects with a baseline AHI between 5-15 had a mean increase in BMI of 0.22 kg/m² (p=0.055) and those with baseline AHI ≥ 15 had a BMI increase of 0.51 kg/m² (p<0.001) compared to those with baseline AHI of <5.

Conclusion: Our findings suggest that there is a positive association between severity of SDB and subsequent increased BMI over approximately 5 years. This observation may help explain why persons with SDB have difficulty losing weight.

Key Words: Sleep Apnea, Weight Gain, Obesity

Abbreviation List: PSG-polysomnogram, SDB-sleep disordered breathing, AHI-apnea hypopnea index, SHHS-Sleep Heart Health Study, BMI-body mass index, SD-standard deviation, SEM-standard error of the mean, ANOVA-analysis of variance

Introduction

There is overwhelming epidemiological and clinical data indicating that obesity is a risk factor for sleep disordered breathing (SDB).^1-8 The association between obesity and SDB is substantial, with high body mass index (BMI) contributing to moderate to severe SDB in 58% of affected persons.⁹ The effect of obesity is greater in men than women^1,10-12 although it decreases with increasing age.^6,7 In addition, weight loss has been demonstrated to decrease the severity of SDB.^10,13,14 Longitudinal data from population studies including the Sleep Heart Health Study (SHHS),¹⁰ the Wisconsin Sleep Cohort,¹⁵ and the Cleveland Family Study⁶ have initially focused on the impact of increased weight on SDB severity. However, examination of the opposite causal pathway has yet to be prospectively addressed.

Anecdotally, patients with SDB appear to have more difficulty losing weight than obese patients without SDB. They also report marked weight gain prior to confirmation of their diagnosis. Two small studies support these empiric observations.^5,16  Given this limited information on the impact of SDB on BMI, data from SHHS was analyzed to examine the impact of SDB on BMI after controlling for change in AHI and severity of SDB.

Methods

Study Design and Population. The SHHS is a multi-center, community-based prospective cohort study of the natural history and cardiovascular consequences of SDB. Details of the study design, sampling, and procedures have been reported.¹⁷ Briefly, between November 1995 and January 1998 participants were recruited from several ongoing prospective cohort studies--the Framingham Offspring and Omni Studies, the Atherosclerosis Risk in Communities Study, the Cardiovascular Health Study, the Strong Heart Study, and the cohort studies of respiratory disease in Tucson and of hypertension in New York. Participants were eligible if they were ≥ 40 years of age and were not being treated for sleep apnea with positive pressure therapy, an oral appliance, oxygen, or a tracheostomy. Habitual snorers < 65 years were over sampled to increase the prevalence of obstructive sleep apnea. Subjects were required to provide written consent and the protocol was approved by the institutional review boards of each of the eight investigative sites. 

Data Collection. A total of 6,441 subjects completed the baseline polysomnogram (PSG), and 4,586 consented to have a second evaluation approximately five years later. This analysis focuses on the 3,040 participants who had PSG and BMI data at both time points. Data from all 215 participants who had a follow-up PSG from the New York center were excluded because they did not meet quality standards for the follow-up examination. The remaining participants died, were too ill to participate, refused to participate, were lost to follow-up or had incomplete covariate data such as weight. This latter group had a higher percentage of Whites (85%) compared with the study group (75.5%) (p-value <0.001). There also were statistically significant differences in baseline BMI, baseline AHI, and age between the study group compared with the excluded group, however, these differences were very small and were not clinically significant. There was no gender difference between the two groups.

Weight was measured on the night of the PSG examination with the participant in light clothes on a calibrated portable scale. Height was obtained at the baseline home visit if not already measured within + 3 months of the parent study. BMI was calculated as weight in kilograms divided by the square of height in meters. Baseline height was used for baseline and follow-up BMI calculations. Age, sex, and ethnicity were self-reported. 

The PSG was conducted using a portable monitor (PS-2 System; Compumedics Limited, Abbotsford, Victoria, Australia), using methods previously described.¹⁸ Apnea was present if there was an absence or near absence of airflow or thoracoabdominal movement (at least < 25% of baseline) for > 10 seconds. Hypopnea was defined as a decrease in the amplitude of the airflow or thoracoabdominal movement below 70% of baseline for > 10 seconds. The apnea-hypopnea index (AHI) was calculated as the number of apnea and hypopnea events, each associated with at least a 4% decrease in oxygen saturation, divided by total sleep time in hours.

Results

Participant characteristics are provided in Table 1. 

Table 1: Characteristics of participants of the Sleep Heart Health Study cohort with complete baseline and follow-up polysomnography and weight measurements as a function of sleep apnea severity.

As expected, women were over-represented in the baseline AHI < 5 group (64.9%) and men were over-represented in the AHI ³ 15 group (63.5%) (p<0.001). Baseline BMI increased as baseline AHI severity increased.  Overall unadjusted five-year increase in BMI was modest with a mean (SD) BMI change of 0.53 (2.61) kg/m². The unadjusted five-year increase in BMI was 0.63 (2.54) kg/m² for those with baseline AHI < 5, 0.43 (2.48) kg/m² among those with AHI ≥ 5 to < 15 and 0.37 (3.05) kg/m² for the AHI group ≥ 15. These values were not statistically different from each other.

A multivariate regression model was constructed predicting five-year change in BMI by baseline AHI category adjusted for age, gender, race, baseline BMI, and AHI change (Table 2).

Table 2: Adjusted β coefficients of BMI change according to AHI and continuous variables in the Sleep Heart Health Study*.

Compared to baseline AHI group of < 5, those with AHI between ≥ 5 to < 15 had a mean adjusted increase in BMI of 0.21 that approached statistical significance (p=0.055). However, those with AHI ≥ 15 had a statistically significant adjusted BMI increase of 0.51 (p<0.001).  Younger age, lower baseline BMI and greater AHI change also were associated with a larger BMI increase. There was a trend for women to have a greater increase in BMI, but no effect of race was observed. However, the model only accounted for 7% of the total variance. Adjusted means by baseline AHI group are displayed graphically in Figure 1.

Figure 1: Estimated Adjusted Means of BMI increase according to AHI in the Sleep Heart Health Study. Data are adjusted for baseline age (continuous), race (categorical), gender (categorical), baseline BMI (continuous), change in AHI (continuous). Covariates fixed at: baseline BMI = 28.7, baseline age 62.1, change in RDI = 2.7. Bars represent 95% confidence intervals.

Discussion

Our findings indicate that there is a positive association between severity of SDB and five-year increase in BMI. The finding was demonstrated after controlling for key covariates including age, gender, race, baseline BMI, and AHI change. This observation may help explain the difficulty patients with SDB have in trying to lose weight.

Two previous small studies have demonstrated a positive association between newly diagnosed SDB and weight gain. A retrospective study by Phillips et al. compared one-year weight histories of 53 men and women patients who were recently diagnosed with SDB with 24 control subjects matched for gender, age, BMI and percent body fat.⁵ Subjects in that study were somewhat younger than the SHHS cohort with an age difference of approximately 10 years. The SDB among subjects in the previous study tended to be moderate to severe with mean ± SEM AHI 33 ± 5 /h for men and 37 ± 10 /h for women. Mean ± SEM of BMI at time of diagnosis was somewhat higher than in the SHHS with 35 ± 1 kg/m² for men and 44 ± 2 kg/m² for women.  Men and women patients with SDB had reported a recent weight gain of 7.4 ± 1.5 kg compared with a weight loss of 0.5 ± 1.7 kg (p=0.001) in obese controls without SDB. However, given the design of this study it is not possible to determine whether weight gain contributed to the onset of SDB or was a result of SDB. The study was also limited by reliance on self-report of weight gain history.

Another study by Traviss et al. prospectively evaluated 49 obese patients with newly diagnosed SDB.¹⁶ Mean ± SD of AHI at diagnosis was severe at 45 ± 27 /h.  BMI at diagnosis was elevated at 36.5 ± 6.2 kg/m². Of the 49 subjects, 43 could estimate the duration of their symptoms with 84% reporting weight gain since becoming symptomatic.  Weight gain was relatively large, with a reported 17 ± 15 kg over 5.3 ± 4.8 years. However, this study was limited by the lack of a control group and reliance on self-report of weight history.  These two small studies, in addition to our findings, suggest that there is an association between SDB and increased BMI.

Interestingly, unadjusted BMI change in our study was quite modest and not statistically different as a function of SDB severity. However, BMI change over time is a complex phenomenon influenced by several variables. A large (29,799 subjects) prospective study examining 5-year change in weight in a multi-ethnic cohort of men and women explored several of these relationships.¹⁹ In that study, younger men and to a greater degree, younger women were at greater risk for weight gain compared to older adults. This is consistent with our initial findings. In addition, there was a trend for women in the higher baseline BMI categories of ‘overweight’ (BMI >25– 30 kg/m²) and ‘obese’ (BMI >30 kg/m²) in the aforementioned cohort to gain more weight than men in the higher baseline BMI categories. In order to more precisely examine the effect of AHI on weight change, we controlled for these confounders in our final multivariate model thus resulting in the finding of an increase in BMI as a function of SDB severity in this study. 

Several mechanisms could explain why SDB contributes to increased BMI. First, persons with SDB may have a reduction in the quantity and quality of their sleep. Recent data indicate that insufficient sleep may be a risk factor for obesity.²⁰ Experimental sleep restriction increases ghrelin and reduces leptin production favoring appetite enhancement,²¹ a finding that also has been observed in a large population cohort.²² Second, those with SDB may eat a diet that favors weight gain. In support of this hypothesis, sleep restriction has been shown to increase craving for calorie dense food with high carbohydrate content. The Apnea Positive Pressure Long-Term Efficacy Study (APPLES) demonstrated that those with severe SDB consumed a diet higher in cholesterol, protein, total fat and total saturated fatty acids, even after adjusting for BMI, age, and daytime sleepiness.²³ Third, a cardinal symptom of SDB is excessive daytime sleepiness. Thus, it is possible that persons with SDB engage in less physical activity because they are too fatigued to exercise. Data from APPLES indicate that recreational physical activity is less in those with SDB. However, this finding appears to be principally explained by concomitant obesity. 

Weight loss frequently results in an improvement and sometimes resolution in SDB. This is most evident in those who undergo bariatric surgical procedures.^24,25 Persons with SDB are frequently counseled to treat their SDB by losing weight through diet and exercise,²⁶ an approach that is usually unsuccessful.²⁵ Failure to primarily address SDB in conjunction with a weight reduction program may diminish the latter’s success. However, evidence to date indicates that treatment of SDB does not consistently result in weight loss. In a sample of clinical patients with SDB, treatment with CPAP did not result in weight loss. Moreover, in female patients, there was actually an increase in weight.²⁷ In addition, consistent weight reduction was not observed in a small number of patients with severe OSA who underwent tracheostomy.²⁸ Thus, it appears that weight gain engendered by the presence of OSA is not easily reversed despite therapy. Prospective studies will be required to determine whether primary treatment for OSA enhances weight loss programs in those with OSA.

Although this analysis demonstrated a positive association of severity of SDB on five-year increase in BMI, there are several caveats that deserve consideration. The BMI of participants tended to be lower than that seen in clinical SDB populations and a relatively small number of subjects had large changes in BMI. As previously noted, the mean BMI increase was, at best, quite modest. When converted for illustrative purposes to weight using an average height of 167 cm of the participants, those with an AHI between ≥ 5 to < 15 had a mean adjusted increase in BMI of 0.21 kg/m² equal to 0.59 kg or 1.30 lbs. Similarly, those with AHI ≥ 15 had an adjusted BMI increase of 0.51 kg/m² equal to 1.42 kg or 3.13 lbs. Thus, the magnitude of the changes we observed may not be applicable to clinical populations where patients with SDB may have a higher BMI. In addition, it is not known when the participants developed SDB, thus definitive inference of causality cannot be made. However, following a large undiagnosed cohort over an extended period of time to determine incidence of SDB onset and subsequent change in weight would be exceedingly difficult and costly.  Additionally, the model only accounted for a small amount of the total variance in five-year BMI increase, suggesting that there are likely other unmeasured variables influencing the amount of BMI increase over time in this cohort. Finally, while not statistically significant, the unadjusted mean change in BMI was slightly less in the high RDI group in comparison to the lower RDI groups. This observation underscores the biological complexity of the interactions among weight change, SDB, age, gender and other factors.

In conclusion, our findings suggest that although weight gain is a risk factor for developing or worsening SDB, SDB may, in a reciprocal fashion, lead to increased weight gain. This may help explain why patients with SDB find it difficult to lose weight.

Acknowledgements

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), and U01HL63429 (Missouri Breaks Research).

Sleep Heart Health Study (SHHS) acknowledges the Atherosclerosis Risk in Communities Study (ARIC), the Cardiovascular Health Study (CHS), the Framingham Heart Study (FHS), the Cornell/Mt. Sinai Worksite and Hypertension Studies, the Strong Heart Study (SHS), the Tucson Epidemiologic Study of Airways Obstructive Diseases (TES) and the Tucson Health and Environment Study (H&E) for allowing their cohort members to be part of the SHHS and for permitting data acquired by them to be used in the study.  SHHS is particularly grateful to the members of these cohorts who agreed to participate in SHHS as well. SHHS further recognizes all of 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.jhucct.com/shhs.

The opinions expressed in the paper are those of the author(s) and do not necessarily reflect the views of the Indian Health Service.

These data have been presented in part at the Annual Meeting of the Associated Professional Sleep Societies, June 11, 2009, Seattle, WA.

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A 45-YEAR-OLD MAN WITH EXCESSIVE DAYTIME SOMNOLENCE, AND WITNESSED APNEA AT ALTITUDE

Rebecca Keith MD

University of Colorado Denver, Denver, CO

rebecca.keith@ucdenver.edu

Carolyn H. Welsh MD

Veterans Affairs Medical Center, Denver, CO

Professor of Medicine

Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Denver, Denver, CO

carolyn.welsh@va.gov

Study performed at Denver Veterans Affairs Medical Center

Reference as: Keith R, Welsh CH. A 45-year-old man with excessive daytime somnolence, and witnessed apnea at altitude. Southwest J Pulm Crit Care 2011;2:53-57. (Click here for PDF version)

Abstract

A sleepy man without sleep apnea at 1609m (5280 feet) had disturbed sleep at his home altitude of 3200m (10500 feet).  In addition to common disruptors of sleep such as psychophysiologic insomnia, restless leg syndrome, alcohol and excessive caffeine use, central sleep apnea with periodic breathing can be a significant cause of disturbed sleep at altitude.  In symptomatic patients living at altitude, a sleep study at their home altitude should be considered to accurately diagnose the presence and magnitude of sleep disordered breathing as sleep studies performed at lower altitudes may miss this diagnosis. Treatments options differ from those to treat obstructive apnea.  Supplemental oxygen is considered by many to be first-line therapy.

Case Report 

A 45 year old man presents for evaluation of poor sleep, daytime somnolence, and morning headaches.  His bed partner reports that he has loud snoring, gasping arousals, and frequent episodes of apnea.  He is an obese man with diabetes, hypertension, rhinosinusitis, and gastroesophageal reflux disease who is a long time resident at 3200m (10500 feet).  Polysomnography performed at 1609m (5280 feet) showed a normal apnea-hypopnea index (AHI) of 1.4.  He spent less than one minute with an oxygen saturation (SaO2) less than 88%. 

Given the severity of his symptoms, a home cardiopulmonary sleep study was performed at 3200m (10500 feet).  AHI was 39 non-supine and 56 supine. A representative portion of his study is shown in Figure 1.  It demonstrates the absence of respiratory effort during apnea events, consistent with central sleep apnea from periodic breathing at altitude.

Figure 1. This is a representative example of breathing during sleep. The upper two channels show nasal (nasal flow) and oral air flow (thermistor).  Respiratory effort signals in the abdomen and thorax are shown in the next two channels and oxygen saturation in the bottom tracing. A ten minute period of recording is displayed. (Click here for JPEG image)

Discussion

Central apnea is characterized by recurrent episodes of apnea resulting from temporary loss of ventilatory effort.  These episodes generally result from a strong dependence on the metabolic control of ventilation during sleep.¹   Conditions inducing a reduced partial pressure of carbon dioxide in arterial blood (PaCO2) can precipitate a central apnea, often associated with an arousal and leading to daytime somnolence.    

At higher altitudes, the fraction of inspired oxygen (FiO2) is the same as it is at sea level, but the barometric pressure and oxygen tension are less, so the partial pressure of oxygen in arterial blood (PaO2) and SaO2 tend to be reduced.  This tendency toward hypoxemia elicits a series of physiologic responses including increased alveolar ventilation, lowering the resting PaCO2, shrinking the gap between the resting PaO2 and the PaCO2 associated with apnea (the apnea threshold).  People with sensitive chemoreceptors are more likely to develop periodic breathing at altitude.  An increased ventilatory response to hypoxemia may reduce PaCO2 below the apnea threshold.  The resultant apnea will eventually result in worsened hypoxemia, which will trigger hyperventilation setting up the oscillating pattern of apnea and hyperventilation typical of periodic breathing.¹  Of note, these responses seem primarily related to hypoxemia rather than low barometric pressure as they are present in simulated normobaric hypoxia equivalent to 2000m (6562 feet) altitude .² 

Central sleep apnea encompasses multiple disorders including narcotic-induced central apnea, obesity-hypoventilation syndrome, Cheyne-Stokes breathing often seen with heart failure or cerebrovascular disease, and high altitude-induced periodic breathing.  Periodic breathing at altitude has the pattern of apneic episodes with cycles of hypoxemia, subsequent hyperventilation, hypocapnia, and resulting apnea described above.  The skeletal muscle atonia and reduced respiratory drive³ that normally occurs during rapid eye movement (REM) sleep typically terminates the periodicity by eliminating hyperventilation and restoring regular breathing.  Thus, periodic breathing at altitude is seen primarily during non-REM sleep.⁴   This aberrant respiratory pattern may initially be beneficial at high altitude since the hyperventilation increases SaO2 by 2.9±1.5%, thus improving hypoxemia.⁴  In our patient, this potential benefit was overcome by a significant increase in sleep disturbance leading to sleep deprivation as well as mental and physical impairment.

Treatment options for periodic breathing at altitude include nighttime oxygen therapy, acetazolamide, and sedative-hypnotics.  Supplemental oxygen breaks the apnea cycle by eliminating the hypoxemia that results in hyperventilation, hypocapnia, and apnea.  Studies of supplemental oxygen at altitude have shown that subjects spend more time in deep sleep and have improvements in SaO2, tidal volume and AHI.⁵  

Acetazolamide, a carbonic anhydrase inhibitor, induces a metabolic acidosis by urinary elimination of bicarbonate. This is thought to increase ventilation, left-shift the hypercapnic ventilatory response and thus reduce apnea events.  Acetazolamide can significantly improve sleep disordered breathing and oxygen saturation during sleep.⁶ Sedative-hypnotic agents have also improved slow wave sleep at altitude without adversely affecting respiration or performance.  This improvement is thought to be secondary to a decrease in wakefulness after sleep onset and improved sleep efficiency.⁷

This patient presented with classic symptoms of sleep apnea with a negative sleep study at 1609m (5280 feet), arousing clinical suspicion for periodic breathing at altitude.  A repeat home sleep study revealed moderate to severe central sleep apnea.  After treatment with oxygen and acetazolamide his symptoms significantly improved and repeat home monitoring demonstrated an AHI of 6.  Treatment with oxygen alone resulted in improvement as well with an AHI of 13.

Central sleep apnea and periodic breathing are common causes of disturbed sleep at altitude.  Symptomatic patients living at altitude should have a sleep study at their home altitude to accurately diagnose the presence and magnitude of sleep disordered breathing.  Oxygen is considered by many to be first line therapy.

References

1.  Eckert DJ, Jordan AS, Merchia P, Malhotra A. Central sleep apnea: Pathophysiology and Treatment. Chest 2007; 131(2): 595-607.

2. Kinsman TA, Hahn AG, Gore CJ, Wilsmore BR, Martin DT, Chow CM. Respiratory events and periodic breathing in cyclists sleeping at 2,650-m simulated altitude. Journal of Applied Physiology 2002; 92 (5): 2114-8.

3. Schafer, T, Schlafke ME, Respiratory changes associated with rapid eye movements in normo- and hypercapnia during sleep. Journal of Applied Physiology 1998; 85: 2213-2219.

4. Salvaggio A, Insalaco G, Marrone O, et al. Effects of high-altitude periodic breathing on sleep and arterial oxyhaemoglobin saturation. European Respiratory Journal 1998; 12 (2): 408-13.

5. Windsor JS, Rodway GW. Supplemental oxygen and sleep at altitude. High Altitude Medicine & Biology 2006; 7 (4): 307-11.

6. Fischer R, Lang SM, Leitl M, Thiere M, Steiner U, Huber RM. Theophylline and acetazolamide reduce sleep-disordered breathing at high altitude. European Respiratory Journal 2004; 23 (1): 47-52.

7. Beaumont M, Batejat D, Pierard C, et al. Zaleplon and zolpidem objectively alleviate sleep disturbances in mountaineers at a 3,613 meter altitude. Sleep 2007; 30 (11): 1527-33.

Acknowledgments:

Rebecca Keith was the primary author writing the manuscript. Carolyn Welsh also contributed to content and manuscript preparation and revision.

Corresponding author: Rebecca Keith MD

                                     National Jewish Health

                                     1400 Jackson Street

                                     Denver, CO 80206

                                     Phone: 303-398-1511

                                     FAX: 303-398-1381

No financial support was provided for this manuscript and there is no off-label or investigational use of medications or devices.

Dr Keith has no conflict of interest to disclose

Dr. Welsh has no conflict of interest to disclose

Abbreviation List:  

AHI - Apnea hypopnea index, the number of apneas or hypopneas per hour of sleep or recording

m – Meters

FiO2 – Fraction of inspired oxygen

PaCO2 - Partial pressure of carbon dioxide in arterial blood

PaO2 – Partial pressure of oxygen in arterial blood

REM – Rapid eye movement

SaO2 – Percentage of available hemoglobin that is saturated with oxygen