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Clinical Investigations: SLEEP AND BREATHING |

What Are Obstructive Sleep Apnea Patients Being Treated for Prior to This Diagnosis?* FREE TO VIEW

Robert Smith, MD; John Ronald, MD; Kenneth Delaive, BSc; Randy Walld, BSc; Jure Manfreda, MD; Meir H. Kryger, MD, FCCP
Author and Funding Information

*From the Sleep Disorders Center (Drs. Smith, Ronald, and Kryger, and Mr. Delaive), St. Boniface General Hospital Research Center; and the Center for Health Policy and Evaluation (Dr. Manfreda and Mr. Walld), Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.

Correspondence to: Meir H. Kryger, MD, FCCP, Sleep Disorders Center, St. Boniface General Hospital, R2034, 351 Tache Ave, Winnipeg, Manitoba, Canada R2H 2A6; e-mail: kryger@sleep. umanitoba.ca



Chest. 2002;121(1):164-172. doi:10.1378/chest.121.1.164
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Published online

Background: Patients with obstructive sleep apnea syndrome (OSAS) use health-care resources at higher rates than control subjects for years prior to diagnosis. Although obesity and certain cardiovascular disorders are more common in OSAS patients, the precise cause of increased health-care utilization is unclear.

Objectives: To examine the causes of increased utilization, and what patients with OSAS were being treated for prior to this diagnosis.

Methods: We compared the records of 773 patients with OSAS to those of age-, gender-, geographic-, and physician-matched control subjects from the general population.

Results: We found that sleep apnea patients used 23 to 50% more resources (defined by physician fees, physician visits, and hospital nights) in the 5 years prior to diagnosis than did control subjects. We examined the diagnoses made and found that apnea patients are at higher risk for hypertension (odds ratio [OR], 2.5; 95% confidence interval [CI], 2.0 to 3.3), congestive heart failure (OR, 3.9; 95% CI, 1.7 to 8.9), cardiac arrhythmias (OR, 2.2; 95% CI, 1.2 to 4.0), cardiovascular disease (OR, 2.6; 95% CI, 2.0 to 3.3), chronic obstructive airways disease (OR, 1.6; 95% CI, 1.2 to 2.0), and depression (OR, 1.4; 95% CI, 1.0 to 1.9). To control for the confounding effects of obesity and to determine the independent effects of body mass index (BMI), gender, age, degree of hypoxemia, apnea-hypopnea index, and sleepiness in the 773 patients, we performed a logistic regression analysis with the dependent variable being diagnosis, and a linear regression analysis with the dependent variable being measures of health-care utilization. Age and BMI were significant independent predictors of most cardiovascular diagnoses and arthropathy. Male gender predicted ischemic heart disease (OR, 2.98; 95% CI, 1.36 to 6.54), and female gender was predictive of chronic obstructive airways disease (OR, 2.63; 95% CI, 1.85 to 3.72) and depression (OR, 2.24; 95% CI, 1.45 to 3.44). The best model predicting health-care utilization measures was comprised of age, gender, and BMI, and explained 9%, 14%, and 8% of the variability in physician fees, number of physician claims, and number of physician visits, respectively.

Conclusion: Of all comorbid diagnoses, significantly increased utilization was found for cardiovascular disease and especially hypertension in the OSAS patients.

Figures in this Article

Obstructive sleep apnea syndrome (OSAS) is a condition characterized by repetitive episodes of cessation of breathing followed by arousals from sleep. This syndrome may cause disabling symptoms, may be associated with other disorders, and may eventually lead to premature death.1The symptoms include excessive daytime sleepiness (if untreated) and the ensuing increased risk of accidents at work and while operating a motor vehicle. OSAS has been associated with psychiatric (mood) and cardiovascular disorders.2

OSAS, which is a common disorder affecting 2 to 4% of the adult general population, remains underdiagnosed and undertreated.34 This may be due in part to the commonly held belief by some that OSAS may not pose a serious health risk.5 Thus, the diagnosis and treatment of this condition may have a low priority in health-care systems.

At the time of diagnosis, patients often report a long history of symptoms going back many years. It has been shown that OSAS patients are heavy users of health-care resources, not only at the time of diagnosis, but also for years prior to diagnosis.67 It has also been found that diagnosis of OSAS and adherence to treatment results in a significant reduction in resource utilization (physician claims and hospital stays).8

The role of obesity in illness and its cost have been investigated910; however, these factors have not been studied in OSAS patients, who are often obese. There are no reports concerning the effect of body mass index (BMI), apnea severity, and gender on utilization of health-care resources in patients with OSAS. Our aim was to determine what patients are being treated for in the 5 years prior to the diagnosis of sleep apnea, what variables are associated with these diagnoses, and the use of health-care resources. We hypothesized that increased health-care utilization would be for a variety of diagnoses because of the broad spectrum of disorders and presentations associated with OSAS.

This study was undertaken in the Canadian province of Manitoba. All residents have access to government-funded health-care services, including physician visits and hospitalizations. Each physician visit generates a standardized claim form submitted to a central government agency (Manitoba Health), which then renders payment. Manitoba Health maintains a detailed computer database, the Manitoba Health database (MHdb), tracking all visits to physicians, hospitalizations, and outpatient surgeries with relevant diagnoses and costs for all residents of Manitoba. The MHdb allows us to track health-care utilization of cases and control subjects over long time intervals and to determine the frequency with which diagnoses are made.11 International Classification of Disease (ICD-9) codes are embedded in the database based on the diagnoses stated on individual physician claim cards for each physician visit. The following comorbid diagnoses (along with their respective ICD-9 codes) were selected for assessment: hypertension (401–405), ischemic heart disease (410–414), congestive heart failure (428), cardiac arrhythmia (426–427), chronic obstructive airways disease (490–493, 496), arthropathy (715–716, 719, 727), and depression (296, 311). The diagnosis of cardiovascular disease (401–405, 410–414, 426–428) was comprised of the summation of the other individual cardiovascular diagnoses.

Confidentiality of OSAS patients and control subjects was ensured by“ encrypting” each person’s health insurance number and using the encrypted number as that person’s only unique identifier. This project was approved by the Human Ethics Committee of the University of Manitoba and the Access and Confidentiality Committee of Manitoba Health.

Selection of Patients

The data were collected at a university-based sleep-disorders center. We selected all patients with polysomnographically proven OSAS who had health-care utilization information in the MHdb going back 5 years before their laboratory diagnosis of sleep apnea.

Evaluation of Patients

All patients were Manitoba residents referred to the same sleep-disorders center for assessment of OSAS. They were all evaluated by one of the authors and underwent overnight polysomnography. This involved multichannel monitoring, including EEG, electro-oculography, electromyography, ECG, arterial oxygen saturation (Sao2), and end-tidal carbon dioxide levels. Thoracic and abdominal movements were continuously monitored by belt plethysmography; activity in the anterior tibialis muscles was monitored by standard electrodes. Diagnosis of OSAS was based on history and polysomnographic findings.

Control Subjects

Using the MHdb, one control subject from the general population was obtained for each OSAS patient. The control subjects were matched to the OSAS patients for age, gender, postal code, and the most frequent physician seen in an ambulatory setting in the previous 2 years. Matching for postal code was done to correct for socioeconomic factors and distance to health-care services. Matching for a specific primary physician was done to minimize biases that could occur if patients and control subjects had different doctors. Seven hundred sixty-six (99%) of the matches were exact for age. However, in the event that an age match within 1 year could not be obtained, up to a 5-year age difference was allowed. Patients were not matched to control subjects for BMI because that information is not included in the MHdb. The authors were not permitted to contact the control subjects because of privacy legislation.

Exclusion

Any case patients or control subjects with extreme health-care usage (> 50 days in hospital over 5 years) or who were institutionalized for chronic illness, or who required dialysis were excluded from the study. This exclusion was done to limit our sample to“ typical” OSAS patients and “typical” control subjects and resulted in exclusion of 31 of 809 eligible case patients. An additional five case patients were excluded due to lack of an appropriate matchable control subject. Our final working database consisted of 773 OSAS patients and their matched control subjects.

Statistical Analysis

In addition to descriptive statistics, we used the t test to compare continuous variables between groups (female vs male subjects) and the paired t test for comparison between matched case patients and control subjects. Wilcoxon’s test was used when nonparametric procedure appeared appropriate. Nonconditional and conditional logistic regression were used to calculate odds ratios (ORs) with 95% confidence intervals (CIs). Linear trend was tested using analysis of variance. Results were considered significant at p < 0.05.12 We first compared case patients and their matched control subjects. To determine the independent effects of BMI, gender, age, degree of hypoxemia, apnea-hypopnea index (AHI), and sleepiness in the 773 patients, we performed a logistic regression analysis with the dependent variable being diagnosis, and a linear regression analysis with the dependent variable being measures of health-care utilization.

Table 1 shows the characteristics of the OSAS patients. They were, on average, obese and had symptomatic OSAS. There were more men than women, reflecting the gender distribution of this disorder.3 The female OSAS patients were significantly older (p < 0.001) and more obese (p < 0.001) than the male patients; however, the male patients had higher apnea indexes (p < 0.001) and Epworth Sleepiness Scale (ESS) scores (p = 0.013) than the female patients.

Health-Care Utilization

Each contact with a physician for OSAS patients and control subjects resulted in a claim form (for billing) that included a diagnosis. Over the 5-year period, OSAS patients were greater users of medical services than the control subjects (Table 2 ). OSAS patients incurred an average of $1,912 in physician fees (Canadian dollars), whereas the control subjects incurred only $1,495 (p < 0.001). OSAS patients had an average of 36.7 physician visits vs 29.9 visits for the control subjects (p < 0.001) over 5 years. Sleep apnea patients also had more hospitalizations than the control subjects. Two hundred seventy-one OSAS patients, compared to 197 control subjects, had at least one overnight hospital admission (p < 0.001). On average, sleep apnea patients spent 2.98 nights in hospital per patient compared to 1.98 nights per patient for the control subjects (p < 0.001) over 5 years. Significantly increased usage for the OSAS patients was evident when we analyzed the data for each gender separately as well (Table 2).

Comorbidity

We examined the probability of having a given diagnosis in the OSAS patients compared with their control subjects. OSAS patients had statistically significant increased odds of having a diagnosis in most of the selected disease categories (Table 3 ). The category with the highest odds for the entire OSAS patient group (female and male patients combined) was congestive heart failure (OR, 3.9; 95% CI, 1.7 to 8.9). For three specific cardiovascular diagnoses (hypertension, congestive heart failure, and cardiac arrhythmia) as well as overall cardiovascular disease, OSAS patients were more than twice as likely as their control subjects to have received the diagnosis. OSAS patients also were more likely to have diagnoses of chronic obstructive airways disease (OR, 1.6; 95% CI, 1.2 to 2.0) and depression (OR, 1.4; 95% CI, 1.0 to 1.9) than the control subjects. The gender-stratified ORs ratios for comorbid diagnoses are illustrated in Figure 1 .

The only significant gender difference for comorbidities was for chronic obstructive airways disease, which was more commonly diagnosed among the female OSAS patients than the male OSAS patients (p = 0.012)

Adjusted Predictors of Comorbid Diagnoses in OSAS Patients

To control for the nonmodifiable risk factors (age, gender), weight (BMI), sleepiness (ESS), and sleep study findings (AHI, percentage of time with Sao2< 90%), we performed a logistic analysis with the dependent variable being ICD-9 diagnosis in the group of 773 patients. The ESS is a subjective measure of sleepiness, with values ranging from 0 (no sleepiness) to 24 (sleepiness in most situations).1314 The results are summarized in Table 4. Female patients with sleep apnea were more than twice as likely as male patients to have chronic obstructive airways disease (OR, 2.63; 95% CI, 1.85 to 3.72) and depression (OR, 2.24; 95% CI, 1.45 to 3.44). Conversely, male sleep apnea patients were almost three times as likely to have ischemic heart disease (OR, 2.98; 95% CI, 1.36 to 6.54) as female patients. All cardiovascular diagnoses (hypertension, ischemic heart disease, congestive heart failure, arrhythmia, cardiovascular disease) and arthropathy were predicted by age. BMI was a significant predictor of several diagnoses, including hypertension, ischemic heart disease, congestive heart failure, cardiovascular disease, and arthropathy. AHI, ESS, and percentage of time with Sao2 < 90% added little to predict comorbidity in OSAS patients.

Correlates of Health-Care Utilization in OSAS Patients

In order to investigate whether any of the measured parameters for sleep apnea patients would allow us to predict heavy usage, a linear regression analysis was performed on the 5-year data for physician fees, number of physician claims, and number of physician visits. Age, gender, BMI, AHI, ESS score, and percentage of time with Sao2 < 90% were weighed as independent variables. The best model for all outcomes was comprised of age, gender, and BMI. This model explained 9% of the variability in physician fees, 14% of the variability in the number of physician claims, and 8% of the variability in the number of physician visits. The interaction of gender and BMI was not statistically significant.

To further delineate the role of obesity in health-care utilization of sleep apnea patients, we stratified the 773 OSAS patients for whom we had reliable BMI data based on the BMI groupings as defined by the World Health Organization Report on Obesity15and the Expert Panel on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults.16 The results are shown in Table 5 . Linear trend analysis of the individual usage measures across the five weight categories did not show a statistically significant trend for any of the three measures (p = 0.927 for physician fees, p = 0.830 for number of physician claims, and p = 0.359 for number of physician visits). Interestingly, the normal weight OSAS patients used resources at similar rates to the most obese (BMI ≥ 40 kg/m2) group for all three measures of utilization. When the linear trends were reanalyzed for the overweight groups (after removing the normal-weight sleep apnea patients), both the number of physician claims (p = 0.031) and the number of physician visits (p = 0.001) showed significantly increasing trends with increasing obesity. Total physician fees (p = 0.089) did not show a significant trend after reanalysis.

Medians for physician fees, number of physician claims, and number of physician visits for each ICD-9 diagnosis among only those OSAS patients and control subjects with the same comorbid diagnosis were compared. The results are shown in Table 6 . For the diagnoses of hypertension and cardiovascular disease, there was a statistically significant increase in all three measures of usage for the OSAS patients compared to their control subjects. Although the diagnosis of depression was more prevalent in OSAS patients than control subjects (OR, 1.4; Table 3), the control subjects with depression had significantly more physician fees and physician claims for this diagnosis than the patients with OSAS. Analysis of individual billing codes revealed that the increased usage for depression in the control subjects was for psychotherapeutic services.

We found that prior to diagnosis of OSAS (and not necessarily prior to disease onset), sleep apnea patients more frequently received diagnoses of a variety of other conditions compared to the control subjects. These include hypertension, congestive heart failure, cardiac arrhythmias, cardiovascular disease, obstructive airways disease, and depression. This indicates that apnea patients are more ill than control subjects, and also suggests that some may have incorrect diagnoses and may be treated for conditions (such as depression), which have symptoms that overlap with those of OSAS.

For five years before sleep apnea diagnosis, OSAS patients used medical resources at significantly higher rates than the control subjects. Analysis of three markers of usage, namely physician fees, number of physician visits, and number of nights spent in hospital, demonstrates the greater utilization in the OSAS patient group by 23 to 50% compared to the matched control subjects.

Previous work67 has shown an even greater difference in health-care utilization between OSAS patients and matched control subjects in the years prior to sleep apnea diagnosis than the current study. One of the major methodologic differences in this study is the matching of OSAS patients and control subjects for a specific primary physician. This was done to minimize biases in referral patterns. However, this may have selected out a group of control subjects who are more likely to seek regular physician contact and are sicker than the general population, and thus use more health-care resources. This only strengthens the association of OSAS with increased health-care usage prior to diagnosis and treatment.

Although we matched case patients and control subjects by age, gender, area of residence, and family doctor, we were precluded from matching by BMI because we were not permitted to contact the control subjects to obtain their BMI because of legislation protecting patient confidentiality. We, therefore, adjusted for the possible confound of weight, as well as gender, age, AHI, oxygen desaturation, and subjective sleepiness, by determining the independent effect of each of these variables (controlling for the others) on diagnosis as well as measures of health-care utilization within the patient group itself. We did not control for smoking and alcohol use. Peppard et al17found no evidence that these were important confounders in linking apnea to hypertension. Nieto et al18 similarly reported that controlling for smoking and alcohol use had very little impact on their findings in examining the link between apnea and hypertension.

Because obesity is so often seen in OSAS, there is a tendency to focus on obesity per se as the cause of the increased morbidity (eg, hypertension) of these patients. Although overweight is a possible confounder between OSAS and morbidities such as hypertension, it has been suggested by Nieto et al18 that data linking OSAS to hypertension are also consistent with an alternate model, whereby sleep apnea is one of the mechanisms causing hypertension in the obese. Nieto et al18 thus suggest that adjusting for BMI may be an “overadjustment.” When Peppard et al17 examined the relationship between AHI and hypertension, adjusting for more factors than age and gender did not change any of the findings from statistically significant to nonsignificant. Indeed, when we dealt with the possible confounding variables in our OSAS patients, we found that the impact of BMI was quite modest. People who are markedly overweight are not necessarily heavy users of health-care resources. We recently studied the data of a group of “healthy” obese individuals (mean BMI, 43.4 kg/m2) who participated in a Canadian population-based study of cardiovascular risk factors. They had health-care utilization over a 7-year period that was quite similar to a group of individuals chosen from the general population but matched for age, gender, and postal code.,19

The best model predicting measures of health-care utilization in OSAS was comprised of age, gender, and BMI; it explained < 15% of the variability in any individual utilization measure. We then further studied the role of BMI by category in predicting usage and found that even normal-weight OSAS patients have extreme usage. Linearity for two of the three usage measures (number of physician claims and number of physician visits) was shown for the overweight and obese groups; however, when all weight groups were considered in the analysis, none of the trends in usage measures were statistically significant. This may be somewhat surprising at first glance. However, as is the experience in most sleep laboratories, thin patients may have severe sleep apnea (caused, for example, by retrognathia), and very obese patients may not have apnea at all or may have mild apnea. Even within the obese population there is no a priori reason why, for example, a patient with a BMI of 38 kg/m2 should have greater expenditure than a patient with a BMI of 34 kg/m2. Similarly, the lack of correlation with AHI is of interest. Again, this is due to the fact that AHI is probably an imperfect linear measure of severity. For example, 30 long apneic episodes per hour with severe oxygen desaturation may have greater physiologic impact than 80 short episodes with little hypoxemia. This highlights the fact that there is no one measure that by itself adequately defines the severity of the disorder and would therefore predict health-care utilization.

When we looked at diagnosis specific expenditure, we found significantly increased usage in the OSAS group for hypertension and cardiovascular disease. This is not surprising, in that others20 have recently shown OSAS to be an independent risk factor for the development of hypertension. In a large community-based cross-sectional analysis from the Sleep Heart Health Study, Nieto et al18 showed ORs for hypertension (adjusted for demographics, anthropometric variables, alcohol intake, and smoking) of 1.37 (95% CI, 1.03 to 1.83) and 1.46 (95% CI, 1.12 to 1.88) in comparing the highest and lowest categories of AHI (≥ 30 events per hour vs < 1.5 events per hour) and percentage sleep time at < 90% oxygen saturation (≥ 12% vs < 0.05%), respectively. In a prospective population-based study, Peppard and colleagues17 showed ORs for hypertension (adjusted for base-line hypertension status, age, sex, habitus variables, and alcohol and cigarette use) ranging from 1.42 to 2.89 at 4-year follow-up based on baseline AHIs.

The health-care utilization costs that we have determined actually underestimate expenditure because they do not include the costs of medications. To estimate the medication costs, we examined a new database that includes medication use by all residents of Manitoba. This database allowed us to examine drug use in a subset of 422 OSAS patients (we were unable to examine the data in the patients who received a diagnosis prior to the establishment of this database), and we found that OSAS patients were much more likely to receive antihypertensive medications (OR, 2.5; 95% CI, 1.3 to 4.8) than control subjects. The average hypertensive patient with sleep apnea received more prescriptions during the course of a year (6.4 prescriptions vs 4.4 prescriptions) than hypertensive control subjects, and the average prescription was more expensive ($39.88 vs $33.92), resulting in higher annual medication use. Thus, it appears that treatment of hypertension in an OSAS patient is more expensive than treatment of hypertension in an individual from the general population. This is consistent with the previously published observation that patients with sleep apnea may have difficult-to-treat hypertension.21 Future studies will examine medication use in detail.

Although depression was more commonly diagnosed in the OSAS patients than in the control subjects, a paradoxical finding was that medical costs associated with depression in the control subjects were actually higher than those in OSAS patients. On examining the services associated with this increased expenditure, we found that psychotherapy was much more commonly used in the treatment of the control subjects with depression compared to OSAS patients with depression. We hypothesized that sleep apnea patients who are sleepy would not be good candidates for such therapy and might be more likely to be prescribed medication to treat their depression. This was confirmed when we analyzed the annual use of antidepressants. We found that the average sleep apnea patient receiving antidepressants used $324.76 of medication compared to $232.21 in the control subjects receiving antidepressants. The average prescription in the OSAS patients was $60.17 vs $38.97 for the control subjects. This supports the hypothesis that sleep apnea patients with a diagnosis of depression are treated differently than control subjects with depression, and that they are more likely to receive drug therapy. What might be even more disturbing is the possibility that sleep apnea patients are being treated for an illness (depression) that they may not have. This is because some of the classic symptoms of apnea (sleepiness, loss of energy) may be misinterpreted to represent depression.

We found that in the years leading up to diagnosis of OSAS, patients with sleep apnea are more likely than control subjects to be treated for cardiovascular diseases, chronic obstructive airways disease, and depression. Depression appears to be treated differently in sleep apnea patients (with higher usage of antidepressant medications) than in the general population. Age and BMI are significant predictors of many of the cardiovascular diagnoses in OSAS patients, while other variables such as AHI, ESS score, and percentage of time with Sao2 < 90% have little predictive value. Hypertension and cardiovascular disease accounted for the majority of the increased resource utilization in OSAS patients; however, no single variable was a good marker of this increased usage in OSAS patients.

Abbreviations: AHI = apnea-hypopnea index; BMI = body mass index; CI = confidence interval; ESS = Epworth Sleepiness Scale; ICD-9 = International Classification of Disease; MHdb = Manitoba Health database; OR = odds ratio; OSAS = obstructive sleep apnea syndrome; Sao2 = arterial oxygen saturation

Supported in part by National Institutes of Health grant No. R01 HL63342–01A1.

Table Graphic Jump Location
Table 1. Characteristics of OSAS Patients*
* 

Values are presented as mean ± SEM unless otherwise indicated.

 

Group t test.

Table Graphic Jump Location
Table 2. Health-Care Utilization Over 5 Years*
* 

Data are presented as mean ± SEM.

 

Paired t test.

 

Expenditure (Canadian dollars) for each OSAS patient.

§ 

No. of physician visits for each OSAS patient.

 

No. of nights spent in hospital for each OSAS patient.

Table Graphic Jump Location
Table 3. Odds of Having a Comorbid Diagnosis Over 5 Years in OSAS Patients vs Control Subjects*
* 

CVD = cardiovascular disease; HTN = hypertension; IHD = ischemic heart disease; CHF = congestive heart failure; COAD = chronic obstructive airways disease (includes asthma, emphysema, bronchitis, chronic airway obstruction not classified elsewhere).

 

Lower confidence limit was rounded down to 1.0.

Figure Jump LinkFigure 1. ORs for comorbid diagnoses in OSAS patients vs control subjects over 5 years prior to sleep apnea diagnosis. Cardiovascular diseases, especially hypertension and congestive heart failure, were more frequently diagnosed in both male and female OSAS patients than in control subjects. IHD = ischemic heart disease; COAD = chronic obstructive airway disease.Grahic Jump Location
Table Graphic Jump Location
Table 4. Demographic and Sleep Variables and Comorbid Diagnoses in OSAS Patients*
* 

Data are presented as adjusted OR (95% CI). See Table 3 for expansion of abbreviations. Values in parenthesis are adjusted ORs. For each variable in column 1, the remaining variables in the column have been controlled for. NS = not statistically significant.

 

Female OSAS patients are 0.34 times as likely to have a diagnosis of IHD as male OSAS patients.

 

For every 1-yr increase in age, there is an 8% increase in risk of cardiovascular disease.

§ 

For every 1-kg/m2 increase in BMI, there is a 4% increase in risk of cardiovascular disease.

 

For every 1-unit increase in AHI, there is a 1% increase in risk of cardiovascular disease.

 

For every 1-point increase in ESS score, there is a 3% decrease in risk of arthropathy.

# 

For every 1% increase in percentage of time with Sao2 < 90%, there is a 2% increase in risk of congestive heart failure.

Table Graphic Jump Location
Table 5. Health-Care Utilization by Weight Category in OSAS Patients Over 5 Years
* 

Mean physician fees ± SD for each OSAS patient.

 

Mean No. of physician claims ± SD for each OSAS patient.

 

Mean No. of physician visits ± SD for each OSAS patient.

Table Graphic Jump Location
Table 6. Diagnosis-Specific Physician Billing for OSAS Patients vs Control Subjects Over 5 Years (Medians)*
* 

See Table 3 for expansion of abbreviations.

 

Median physician fees (in Canadian dollars) for cardiovascular disease per OSAS patient with cardiovascular disease.

 

Median No. of physician claims for cardiovascular disease per OSAS patient with cardiovascular disease.

§ 

Median No. of physician visits for cardiovascular disease per OSAS patient with cardiovascular disease.

 

Wilcoxon’s test.

He, J, Kryger, MH, Zorick, FJ, et al (1988) Mortality and apnea index in obstructive sleep apnea: experience in 385 male patients.Chest94,9-14. [PubMed] [CrossRef]
 
Yamashiro, Y, Kryger, MH Why should sleep apnea be diagnosed and treated?Clin Pulm Med1994;1,250-259
 
Young T, Palta M, Dempsey, et al. The occurrence of sleep-disordered breathing among middle-aged adults. N Engl J Med 1993; 328:1230–1235.
 
Ohayon, MM, Guilleminault, C, Priest, RG, et al Snoring and breathing pauses during sleep: telephone interview survey of a United Kingdom sample.BMJ1997;314,860-863. [PubMed]
 
Wright, J, Johns, R, Watt, I, et al Health effects of obstructive sleep apnoea and the effectiveness of continuous positive airway pressure: a systematic review of the research evidence.BMJ1997;314,851-860. [PubMed]
 
Kryger, MH, Roos, L, Delaive, K, et al Utilization of health care services in patients with severe obstructive sleep apnea.Sleep1996;19,S111-S116. [PubMed]
 
Ronald, J, Delaive, K, Roos, L, et al Health care utilization in the 10 years prior to diagnosis in obstructive sleep apnea syndrome patients.Sleep1999;22,225-229. [PubMed]
 
Bahammam, A, Delaive, K, Ronald, J, et al Health care utilization in males with obstructive sleep apnea syndrome two years after diagnosis and treatment.Sleep1999;22,740-747. [PubMed]
 
Ferrannini, E Physiological and metabolic consequences of obesity.Metabolism1995;44(9 suppl 3),15-17
 
Wolf, A, Colditz, GA Social and economic effects of body weight in the United States.Am J Clin Nutr1996;63(suppl),466S-469S
 
Roos NP, Shapiro E, Health and health care: experience with a population-based health information system. Med Care 1999; 37(6 suppl):JS1–JS308.
 
Woodward, M Epidemiology: study design and data analysis.1999,1-699 Chapman & Hall/CRC. Boca Raton, FL:
 
Johns,, MW Daytime sleepiness, snoring, and obstructive sleep apnea: the Epworth Sleepiness Scale.Chest1993;103,30-36. [PubMed]
 
Johns, MW Reliability and factor analysis of the Epworth Sleepiness Scale.Sleep1992;15,376-381. [PubMed]
 
Obesity: preventing and managing the global epidemic; report of a WHO consultation on obesity. Geneva, Switzerland: World Health Organization, 1997; 1–276.
 
Executive summary of the clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults Arch Intern Med 1998; 158:1855–1867.
 
Peppard, PE, Young, T, Palta, M, et al Prospective study of the association between sleep-disordered breathing and hypertension.N Engl J Med2000;342,1378-1384. [PubMed]
 
Nieto, FJ, Young, TB, Lind, BK, et al Association of sleep-disordered breathing, sleep apnea, and hypertension in a large community-based study.JAMA2000;283,1829-1836. [PubMed]
 
Berg, G, Delaive, K, Manfreda, J, et al The use of health-care resources in obesity-hypoventilation syndrome.Chest2001;120,377-383. [PubMed]
 
Peretz, L, Herer, P, Hoffstein, V Obstructive sleep apnoea syndrome as a risk factor for hypertension: population study.BMJ2000;320,479-482. [PubMed]
 
Isaksson, H, Svanborg, E Obstructive sleep apnea syndrome in male hypertensives, refractory to drug therapy: nocturnal automatic blood pressure measurements; an aid to diagnosis?Clin Exp Hypertens A1991;13,1195-1212. [PubMed]
 

Figures

Figure Jump LinkFigure 1. ORs for comorbid diagnoses in OSAS patients vs control subjects over 5 years prior to sleep apnea diagnosis. Cardiovascular diseases, especially hypertension and congestive heart failure, were more frequently diagnosed in both male and female OSAS patients than in control subjects. IHD = ischemic heart disease; COAD = chronic obstructive airway disease.Grahic Jump Location

Tables

Table Graphic Jump Location
Table 1. Characteristics of OSAS Patients*
* 

Values are presented as mean ± SEM unless otherwise indicated.

 

Group t test.

Table Graphic Jump Location
Table 2. Health-Care Utilization Over 5 Years*
* 

Data are presented as mean ± SEM.

 

Paired t test.

 

Expenditure (Canadian dollars) for each OSAS patient.

§ 

No. of physician visits for each OSAS patient.

 

No. of nights spent in hospital for each OSAS patient.

Table Graphic Jump Location
Table 3. Odds of Having a Comorbid Diagnosis Over 5 Years in OSAS Patients vs Control Subjects*
* 

CVD = cardiovascular disease; HTN = hypertension; IHD = ischemic heart disease; CHF = congestive heart failure; COAD = chronic obstructive airways disease (includes asthma, emphysema, bronchitis, chronic airway obstruction not classified elsewhere).

 

Lower confidence limit was rounded down to 1.0.

Table Graphic Jump Location
Table 4. Demographic and Sleep Variables and Comorbid Diagnoses in OSAS Patients*
* 

Data are presented as adjusted OR (95% CI). See Table 3 for expansion of abbreviations. Values in parenthesis are adjusted ORs. For each variable in column 1, the remaining variables in the column have been controlled for. NS = not statistically significant.

 

Female OSAS patients are 0.34 times as likely to have a diagnosis of IHD as male OSAS patients.

 

For every 1-yr increase in age, there is an 8% increase in risk of cardiovascular disease.

§ 

For every 1-kg/m2 increase in BMI, there is a 4% increase in risk of cardiovascular disease.

 

For every 1-unit increase in AHI, there is a 1% increase in risk of cardiovascular disease.

 

For every 1-point increase in ESS score, there is a 3% decrease in risk of arthropathy.

# 

For every 1% increase in percentage of time with Sao2 < 90%, there is a 2% increase in risk of congestive heart failure.

Table Graphic Jump Location
Table 5. Health-Care Utilization by Weight Category in OSAS Patients Over 5 Years
* 

Mean physician fees ± SD for each OSAS patient.

 

Mean No. of physician claims ± SD for each OSAS patient.

 

Mean No. of physician visits ± SD for each OSAS patient.

Table Graphic Jump Location
Table 6. Diagnosis-Specific Physician Billing for OSAS Patients vs Control Subjects Over 5 Years (Medians)*
* 

See Table 3 for expansion of abbreviations.

 

Median physician fees (in Canadian dollars) for cardiovascular disease per OSAS patient with cardiovascular disease.

 

Median No. of physician claims for cardiovascular disease per OSAS patient with cardiovascular disease.

§ 

Median No. of physician visits for cardiovascular disease per OSAS patient with cardiovascular disease.

 

Wilcoxon’s test.

References

He, J, Kryger, MH, Zorick, FJ, et al (1988) Mortality and apnea index in obstructive sleep apnea: experience in 385 male patients.Chest94,9-14. [PubMed] [CrossRef]
 
Yamashiro, Y, Kryger, MH Why should sleep apnea be diagnosed and treated?Clin Pulm Med1994;1,250-259
 
Young T, Palta M, Dempsey, et al. The occurrence of sleep-disordered breathing among middle-aged adults. N Engl J Med 1993; 328:1230–1235.
 
Ohayon, MM, Guilleminault, C, Priest, RG, et al Snoring and breathing pauses during sleep: telephone interview survey of a United Kingdom sample.BMJ1997;314,860-863. [PubMed]
 
Wright, J, Johns, R, Watt, I, et al Health effects of obstructive sleep apnoea and the effectiveness of continuous positive airway pressure: a systematic review of the research evidence.BMJ1997;314,851-860. [PubMed]
 
Kryger, MH, Roos, L, Delaive, K, et al Utilization of health care services in patients with severe obstructive sleep apnea.Sleep1996;19,S111-S116. [PubMed]
 
Ronald, J, Delaive, K, Roos, L, et al Health care utilization in the 10 years prior to diagnosis in obstructive sleep apnea syndrome patients.Sleep1999;22,225-229. [PubMed]
 
Bahammam, A, Delaive, K, Ronald, J, et al Health care utilization in males with obstructive sleep apnea syndrome two years after diagnosis and treatment.Sleep1999;22,740-747. [PubMed]
 
Ferrannini, E Physiological and metabolic consequences of obesity.Metabolism1995;44(9 suppl 3),15-17
 
Wolf, A, Colditz, GA Social and economic effects of body weight in the United States.Am J Clin Nutr1996;63(suppl),466S-469S
 
Roos NP, Shapiro E, Health and health care: experience with a population-based health information system. Med Care 1999; 37(6 suppl):JS1–JS308.
 
Woodward, M Epidemiology: study design and data analysis.1999,1-699 Chapman & Hall/CRC. Boca Raton, FL:
 
Johns,, MW Daytime sleepiness, snoring, and obstructive sleep apnea: the Epworth Sleepiness Scale.Chest1993;103,30-36. [PubMed]
 
Johns, MW Reliability and factor analysis of the Epworth Sleepiness Scale.Sleep1992;15,376-381. [PubMed]
 
Obesity: preventing and managing the global epidemic; report of a WHO consultation on obesity. Geneva, Switzerland: World Health Organization, 1997; 1–276.
 
Executive summary of the clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults Arch Intern Med 1998; 158:1855–1867.
 
Peppard, PE, Young, T, Palta, M, et al Prospective study of the association between sleep-disordered breathing and hypertension.N Engl J Med2000;342,1378-1384. [PubMed]
 
Nieto, FJ, Young, TB, Lind, BK, et al Association of sleep-disordered breathing, sleep apnea, and hypertension in a large community-based study.JAMA2000;283,1829-1836. [PubMed]
 
Berg, G, Delaive, K, Manfreda, J, et al The use of health-care resources in obesity-hypoventilation syndrome.Chest2001;120,377-383. [PubMed]
 
Peretz, L, Herer, P, Hoffstein, V Obstructive sleep apnoea syndrome as a risk factor for hypertension: population study.BMJ2000;320,479-482. [PubMed]
 
Isaksson, H, Svanborg, E Obstructive sleep apnea syndrome in male hypertensives, refractory to drug therapy: nocturnal automatic blood pressure measurements; an aid to diagnosis?Clin Exp Hypertens A1991;13,1195-1212. [PubMed]
 
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