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(Chest. 1999;116:1537-1544.)
© 1999 American College of Chest Physicians

Value of Clinical, Functional, and Oximetric Data for the Prediction of Obstructive Sleep Apnea in Obese Patients*

Bertrand Herer, MD; Nicolas Roche, MD; Matthieu Carton, MD; Catherine Roig, MD; Vincent Poujol, MD and Gérard Huchon, MD, FCCP

* From Centre Médical de Forcilles (Drs. Herer, Roig, and Poujol), Férolles-Attilly, France; and Service de Pneumologie (Drs. Roche and Huchon) and Département d’Informatique Médicale et de Biostatistiques (Dr. Carton), Université de Paris René Descartes, Hôpital Ambroise-Paré, Boulogne, France.

Correspondence to: Gérard J. Huchon, MD, FCCP, Service de Pneumologie et Réanimation, Hôpital de l’Hôtel-Dieu 1 Place du Parvis de Notre Dame, F-75181 Paris Cedex 4, France; e-mail: gerard.huchon{at}htd.ap-hop-paris.fr


   Abstract
TOP
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Objective: To evaluate the diagnostic value of clinical features, pulmonary function testing, blood gas tensions, and oximetric data for case finding of obstructive sleep apnea (OSA) before polysomnography (PSG) in a series of consecutive overweight patients.

Methods: We studied a population of 102 consecutive patients referred by an obesity clinic for suspected OSA, in whom body mass index was >= 25 kg/m2. The following tests were performed: clinical score (CS), pulmonary function tests (PFTs), measurement of arterial blood gas tensions, nocturnal oximetry, and full-night PSG.

Results: Six of 34 women and 34 of 68 men had OSA, defined by an apnea-hypopnea index >= 15. CS and the cumulative time spent below 80% arterial oxygen saturation (SaO2) were higher, and PaO2, minimal SaO2, and mean nocturnal SaO2 (mSaO2) were lower in OSA patients than in non-OSA patients. Logistic regression showed that sex, CS, and the ratio of FEV1 over forced expiratory volume in 0.5 s (an index of upper airway obstruction on flow-volume curves) and mSaO2, expressed as categorical variables, were independent predictors of OSA. None of these individual variables had a satisfactory diagnostic value for the diagnosis of OSA. A logistic regression model including sex and all continuous variables would have allowed us to predict the presence or absence of OSA confidently in 72.5% of the population, in whom the positive predictive value of the model was 94% and the negative predictive value was 90%.

Conclusion: In obese patients referred to a respiratory sleep laboratory and evaluated by CS, PFTs, arterial blood gases, and oximetry, no individual sign or symptom may accurately predict the presence or absence of OSA. Provided that it is validated in prospective studies, a logistic regression model using these variables may be useful for the prediction of OSA.

Key Words: clinical score • obesity • obstructive sleep apnea • oximetry • polysomnography • upper airway obstruction


   Introduction
TOP
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
The prevalence of obstructive sleep apnea (OSA) has recently been reported to be 2% in women and 4% in men.1 Obesity and male sex are strongly associated with the presence of sleep disordered breathing.1 2 The male predominance may be the result of greater self-selection and referral bias, but may also reflect sex differences in endogenous (eg, upper airway anatomy) or exogenous (eg, alcoholic intoxication) etiologic agents.3 4

Despite recent debates on which measurements best describe OSA, the "gold standard" for the diagnosis of this disease remains polysomnography (PSG), which is expensive and time-consuming. Therefore, investigations have been proposed for appropriate case finding before PSG is performed in patients referred for sleep evaluation, in order to limit the number of PSG tests done.5 6 7 8 9 10 However, predictive factors and their diagnostic values are likely to differ according to the characteristics of the population studied (eg, sex, obesity, underlying respiratory diseases), making it inappropriate to extrapolate results from a given population to patients referred to another laboratory, and making it necessary to determine the best predictive factors for various populations.9 Besides, most studies on predictors of OSA have not included the results of pulmonary function tests (PFTs) and blood gas measures in the analysis.5 6 7 8 9 10

Because obesity is a strong risk factor for OSA,2 practitioners who care for obese patients must frequently suspect the presence of OSA. Therefore, we studied the predictive values of clinical evaluation, PFTs, arterial blood gas tensions, and nocturnal oximetry for OSA case finding in a population of consecutive overweight patients. Because female patients represent approximately one third of patients referred to our laboratory—quite a high proportion compared with that reported in other studies—we also assessed the effect of sex on these predictive values.


   Materials and Methods
TOP
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
Study Population
We analyzed the data from 102 consecutive new overweight patients referred to the respiratory sleep laboratory by the obesity clinic between May 1992 and November 1994. All patients had a body mass index (BMI), calculated as (weight [kg])/(height [m])2, of >= 25 kg/m2. In each patient, a clinical score (CS) was established and PFTs, nocturnal oximetry, and full-night PSG were performed.

Clinical Score
We used a CS derived from Williams et al,10 including four features: habitual snoring, interrupted nocturnal breathing as reported by the spouse or roommates, excessive daytime sleepiness, and arterial hypertension. Each feature was assigned a score of 0 or 1, with a highest possible value of 4 for the whole score. BMI was not included in the score because it was >= 25 kg/m2 in all patients.

Pulmonary Function Testing
PFTs included spirometry and flow-volume curve analysis (Medical Graphics Corp; St. Paul, MN). The predicted values of the European Community for Coal and Steel were used for PFTs.11 Flow-volume curves were examined for the presence of "saw-toothing,"12 and the following flow ratios used for the diagnosis of upper airway obstruction (UAO)13 14 15 were calculated: the ratio of the forced expiratory flow after 50% of the FVC over the forced inspiratory flow after 50% of the FVC (FEF50/FIF50), the ratio of peak expiratory flow over FEF50 (PEF/FEF50), and the ratio of FEV1 over forced expiratory volume in 0.5 s (FEV1/FEV0.5). Arterial blood samples were obtained by radial artery puncture while the patient was seated.

Oximetry
Pulse oximetry was performed overnight 1 to 7 days prior to PSG. The recording was done in the sleep laboratory with portable systems, ie, either a Biox 3740 (Ohmeda; Louisville, CO), a Pulsox-8 (Minolta, AVL Medical Instruments; Cergy-Pontoise, France), or an OLV-1100 (Nihon-Kohden; Tokyo, Japan) oximeter. Stored data were digitized for computer analysis, and the following indices were calculated: minimal nocturnal arterial oxygen saturation (minSaO2), mean nocturnal SaO2 (mSaO2), and cumulative time spent with an SaO2 below 90% (CT90) and below 80% (CT80). Oximetry was not performed during the same night as PSG.

PSG
All patients underwent a full-night PSG including two channels of EEG, one channel of electro-oculogram, and one channel of submental electromyogram. Thoracoabdominal movements were recorded with inductance plethysmography. Airflow at the nose and mouth was assessed by a thermistor. All signals were recorded and stored on semiautomated scoring systems (Minisomno; SEFAM; Nancy, France; or Medatec; Brussels, Belgium). Apnea was defined as cessation of oronasal airflow for > 10 s. Obstructive apneas were scored when airflow was absent but respiratory efforts were present. Hypopnea was defined as a reduction of oronasal airflow to <= 50% of the value prevailing during a preceding period of normal breathing of >= 10 s. OSA was defined as a combined obstructive apnea-hypopnea index (AHI) of >= 15 events/h.16 17

Statistical Analysis
We used Student’s t tests to study differences in CS, pulmonary function variables, arterial blood gases, and results of nocturnal pulse oximetry between patients with OSA (AHI >= 15/h) and patients without OSA (AHI < 15/h). We assessed the correlations between these variables and AHI using the nonparametric Spearman rank test because AHI was not normally distributed according to the Shapiro-Wilks test.

Then, we used receiver operating characteristics (ROC) curves to determine the most accurate diagnostic thresholds for variables that correlated to AHI or differed in OSA and non-OSA patients. The obtained thresholds were used to transform the continuous variables into categorical variables, and Pearson’s {chi}2 test was performed to study differences in distribution of these categorical variables between OSA and non-OSA patients. Multivariate logistic regression analysis was used to determine which categorical variables were independently predictive of OSA and to study the interaction between predictive variables and sex. Logistic regression analysis was also used to develop a model for prediction of OSA. The probability of having OSA (P) was calculated using the following equation:

where y = c (constant) + x1 Variable1 + x2 Variable2 + x3 Variable3, etc. The constant (c) and parameter estimates (x1, x2, x3, etc) were determined by a logistic regression analysis in which presence or absence of OSA was the dependent variable, and in which sex and all continuous variables were first introduced.9 The step-by-step Wald method was then used to restrict variables introduced in the equation to those that independently correlated to the presence of OSA. Goodness of fit of the logistic models was assessed using Hosmer and Lemeshow test. The correlation between P and AHI was studied by the Spearman rank test, and the positive and negative predictive values of various thresholds of P were calculated by ROC curve analysis.

Finally, individual data were analyzed to determine if some ranges or combinations of ranges of variables were highly predictive of either the presence or the absence of OSA.

Results are expressed as mean ± SD unless indicated. We considered a p value < 0.05 to be significant. Logistic regression analysis was repeated with an AHI threshold of 10 for definition of OSA. Statistical analysis was performed with BMDP (BMDP Statistical Software; Los Angeles, CA), SPSS (SPSS Inc; Chicago, IL) and SAS (SAS Institute; Cary, NC) statistical software. ROC curve analysis was performed with ROC Analyzer software (RM Centor and J Keightley; Richmond, VA).


   Results
TOP
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
The study population consisted of 34 women and 68 men, whose characteristics are summarized in Table 1 . BMI and FEV1/FVC were higher and tobacco smoking was less frequent in women than in men. OSA was more frequent in men (p < 0.01; Table 1 ). Airflow obstruction (as defined by the American Thoracic Society18 ) was present in 28% of our male population vs 12% of women, but this difference did not reach significance (p = 0.08). The proportion of patients with OSA did not differ between subjects with or without airflow obstruction, among both men and women (data not shown; p = not significant [NS] for all comparisons). AHI did not correlate with FEV1 and FEV1/FVC (r = 0 0.402 and r = -0.265, respectively; p = NS).


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  		Table 1.. Anthropometric, Polysomnographic, and Pulmonary Function Data*

 
Patients with OSA exhibited the following characteristics when compared with those who did not have OSA: higher CS, lower diurnal PaO2, lower nocturnal minSaO2, mSaO2, and higher CT80 (Table 2 ). These differences were found for continuous as well as for categorical variables. Thresholds used for transformation of continuous variables into categorical variables, as determined by ROC curve analysis, are shown in Table 3 . Table 3 also shows the sensitivity and specificity of these individual variables for the diagnosis of OSA.


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  		Table 2.. CS, Arterial Blood Gases, Indices of UAO, and Oximetric Data*

 

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  		Table 3.. Best Thresholds and Diagnostic Characteristics of Clinical, Oximetric, and Functional Variables

 
Multivariate logistic regression analysis showed that sex, CS, FEV1/FEV0.5, and mSaO2 were independent predictors of OSA when computed as categorical variables (Table 4 ). There was no interaction between sex and other predictive variables. The relationships between AHI and these predictive variables are shown in Figures 1 , 2 .


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  		Table 4.. Results of Multivariate Analysis: Independent Predictors of OSA, as Defined by an AHI of > 10 or > 15 Events/h*

 


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  		Figure 1.. AHI as a function of CS (left) and sex (right), which were both independent predictors of OSA according to logistic regression. Horizontal lines indicate the polysomnographic threshold for the diagnosis of OSA (AHI >= 15 events/h). In the left panel, the vertical line indicates the best threshold of clinical score for prediction of OSA, as determined by ROC curve analysis.

 
The parameter estimates calculated by logistic regression when all variables were used in the analysis are shown in Table 5 and were used to calculate P, as described in the methods section. The calculated probability of having OSA (P) correlated to AHI (Spearman’s, r = 0.66; p < 10-11), and a value of P > 0.75 (n = 21, 20.5% of patients) had a positive predictive value of 90% for the diagnosis of OSA, whereas a value of P < 0.35 (n = 53, 52.0% of patients) had a negative predictive value of 94% for the diagnosis of absence of OSA (Fig 3 , left). Restricting the variables used to those that independently correlated to the presence of OSA, the step-by-step Wald method yielded the following equation:


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  		Table 5.. Parameter Estimates Used to Assess the Probability of Having OSA (As Defined by an AHI of > 15 Events/h), as Calculated by Logistic Regression Analysis When All Variables Were Introduced*

 


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  		Figure 3.. Calculated probability of having OSA (ie, AHI >= 15/h) as a function of measured AHI. The probability was calculated as follows: p = ey/(1 + ey), where y = c (constant) + x1Variable1 + x2Variable 2 +... , with parameter estimates (x1, x2, etc) being calculated by logistic regression analysis. Left, P is the calculated probability of having OSA when all variables were introduced in the regression equation. Ninety percent of patients with P > 0.75 had OSA, while 94% of patients with P < 0.35 did not have OSA. Right, P' is the calculated probability of having OSA when variables introduced in the equation were restricted to those independently predicting OSA according to the ascendant Wald method. Eighty percent of patients with P' < 0.25 did not have OSA.

 
y' = -4.31 + (1.41 x sex) + (0.03 x CT80) + (0.78 x CS)

where y' is used to calculate P' as described above. P' correlated to AHI less strongly than P (r = 0.29; p < 0.01); the only useful feature that could be derived from the plot of P' against AHI was that a value of P' < 0.25 (n = 13, 12.7% of patients) had a negative predictive value of 80% for exclusion of OSA (Fig 3 , right). Results of multivariate analysis were not altered by using a different AHI threshold to define OSA, ie, 10 or 15 events/h (Table 4) .

Finally, individual data analysis found that all patients who had a CS of 4, FEV1/FEV0.5 ratio >= 1.3, and mSaO2 <= 85% (ie, 3% of the population) had OSA confirmed by PSG, whereas all patients who had a CS of < 2, FEV1/FEV0.5 ratio < 1.3, and mSaO2 > 85% (ie, 5% of the population) had the diagnosis of OSA eliminated by PSG. We were unable to define any other ranges or combinations of ranges of variables with intermediate as opposed to low predictive value.


   Discussion
TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
Our data show that in an overweight population, CS, PFTs, and nocturnal oximetry taken alone may not accurately predict the presence or absence of OSA. As shown in Figures 1 , 2 , there is a considerable overlap between patients with and without OSA, even for variables that independently predict OSA according to logistic regression, and even when ROC curve analysis is used to determine the best thresholds for each of these variables. In 72.5% of the population, a complex logistic regression model would predict the presence or absence of OSA with a positive predictive value of 94% and a negative predictive value of 90%.



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  		Figure 2.. AHI as a function of FEF50/FIF50 (left) and mSaO2 (right), which were both independent predictors of OSA according to logistic regression. Horizontal lines indicate the polysomnographic threshold for the diagnosis of OSA (AHI >= 15 events/h). Vertical lines indicate the best thresholds of these variables for prediction of OSA, as determined by ROC curve analysis.

 
As in most studies, patients referred to the sleep laboratory for suspected OSA were predominantly middle-aged, obese men. However, the proportion of obese patients and their mean BMI were higher than in other studies,9 19 20 which is likely due to the fact that several units in our facility are devoted to the management of obesity. Women represented one third of the population; however the male-to-female ratio was approximately 6:1 for patients with OSA, which is similar to values reported in other clinic-based samples (ie, 6:1 to 7:1),4 7 but higher than the ones reported in community-based samples (ie, 2:1 to 3:1).1 21

To study the clinical features of patients with suspected OSA, we used a CS derived from Williams et al,10 who showed that BMI, hypertension, snoring, and gasping or choking observed by a partner were significant predictors of sleep apnea severity. BMI had to be excluded in the score we used because all our patients were overweight (BMI >= 25 kg/m2). Other authors have developed similar scores based on neck circumference instead of BMI,7 22 but the predictive value of this index has been questioned.9

The relationships between OSA, abnormalities of resting diurnal gas exchanges, and pulmonary function are controversial. We observed a lower diurnal PaO2 in patients with OSA than in patients without OSA. Because only a small proportion of OSA patients had an associated bronchial obstruction (6/40, 15%), this resting hypoxemia may be in part explained by high BMI. Indeed, several studies conducted in predominantly obese populations found values of PaO2 similar to those of our patients.23 24 25 Among them, Gold et al23 also found a higher PaCO2 in sleep apnea patients than in control subjects, which was not the case in our study; this discrepancy is likely related to a higher proportion of overlap syndromes in their population, because their patients with OSA had lower FEV1 and FVC than patients without OSA, which we did not find. Some studies have even suggested that lower lung volumes and increased airway resistance contribute to the severity of OSA.26 However, AHI did not correlate to indices of bronchial obstruction (ie, FEV1 and FEV1/FVC) in our patients. Other studies also suggested that the development of hypercapnia in OSA patients requires the presence of an associated bronchial obstruction.24 27 However, this was not found in a study of 111 patients in which the only predictive factors of hypercapnia were PaO2 and female sex,25 although the sex-related difference in PaCO2 did not reach significance in a subsequent analysis of this population.28 In our study, there was no trend toward a higher PaCO2 in women than in men, despite a higher BMI.

As in other studies of flow-volume curves and UAO indices, we found that the saw-tooth pattern12 and the FEF50/FIF50 ratio29 are not useful for OSA case finding. Conversely, we found that the FEV1/FEV0.5 ratio, which has been shown to detect UAO when > 1.5, was a predictor of OSA in the logistic regression analysis when > 1.3.15 However, there was a great overlap between patients with OSA and patients without OSA (Fig 2) .

Various oximetric indices have been studied for case finding of OSA, with sensitivities ranging from 40 to 100% and specificities ranging from 39 to 100%.6 In patients with OSA, we found that minSaO2 and mSaO2 were lower, and CT80 higher, than in patients without OSA. Indeed, AHI was negatively correlated with minSaO2 and mSaO2, and positively correlated with CT80. Finally, mSaO2 was a predictor of OSA according to logistic regression analysis. After determination of optimal thresholds by ROC curves, the oximetric criteria were the variables that had the best diagnostic values, as expressed by the area under the ROC curve30 (Table 3) ; however, this diagnostic value was not good enough to be useful as a screening technique (Fig 2) . This limitation was also pointed out by Gyulay and coworkers,31 who analyzed home nocturnal oximetry. In fact, a combination of independent clinical, functional, and oximetric features allowed prediction of the presence or absence of OSA with an accuracy of 100% in only a small number of patients (8%), and we could not find any other clinically useful combination of variables. Despite a trend toward a higher CT80 for men with OSA, logistic regression did not show any interaction between sex and oximetric data.

Logistic regression analysis provided a model that would have allowed to diagnose or exclude OSA confidently in 72.5% of our population. However, this model is rather complex because it includes 19 variables, which makes it unlikely to be used in clinical practice. A more simple equation, on the other hand, would not be accurate enough to be useful. In any case, such a model must be validated by prospective testing in other series of patients before being used in practice.9

Finally, the choice of PSG as a reference test for measurement of respiratory events, and of AHI for expression of results and discrimination between OSA and non-OSA subjects, may be controversial, because some studies found a poor correlation between AHI and some important clinical features of OSA such as daytime sleepiness.32 However, PSG remains the "gold standard" for the diagnosis of OSA despite extensive research on new diagnostic methods, and it seemed important to use the same reference test as in most studies in this field.9 16 17 For the same reason, an AHI threshold of 15 events/h was chosen for the diagnosis of OSA.33 We confirmed, however, that changing this cut-off value to 10 events/h did not modify our results.

We conclude that individual clinical, functional, and oximetric features do not adequately predict OSA in an overweight population (one third of which was female), and do not provide significant sex-related discrepancies. A predictive model developed by logistic regression analysis may be useful in 72.5% of patients, but this model is complex and its validity needs to be further tested in other series of patients.


   Acknowledgements
 
The authors wish to thank Philippe-François Bernard and Alain Beauchet for their help.


   Footnotes
 
Abbreviations: AHI = apnea-hypopnea index; BMI = body mass index; CS = clinical score; CT80 = cumulative time spent with arterial oxygen saturation below 80%; CT90 = cumulative time spent with arterial oxygen saturation below 90%; FEF50/FIF50 = ratio of the forced expiratory flow after 50% of the FVC over the forced inspiratory flow after 50% of the FVC; FEV1/FEV0.5 = ratio of FEV1 over forced expiratory volume in 0.5 s; minSaO2 = minimal nocturnal arterial oxygen saturation; mSaO2 = mean nocturnal arterial oxygen saturation; NS = not significant; OSA = obstructive sleep apnea; P = probability of having obstructive sleep apnea; PEF/FEF50 = ratio of peak expiratory flow over forced expiratory flow after 50% of the FVC; PFT = pulmonary function test; PSG = polysomnography; ROC = receiver operating characteristics; SaO2 = arterial oxygen saturation; UAO = upper airway obstruction

Received for publication February 3, 1999. Accepted for publication July 6, 1999.


   References
TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

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Chest, March 1, 2002; 121(3): 747 - 752.
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N. Netzer, A. H. Eliasson, C. Netzer, and D. A. Kristo
Overnight Pulse Oximetry for Sleep-Disordered Breathing in Adults : A Review
Chest, August 1, 2001; 120(2): 625 - 633.
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