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Published ahead of print on January 11, 2007, doi:10.1164/rccm.200608-1205OC
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American Journal of Respiratory and Critical Care Medicine Vol 175. pp. 726-730, (2007)
© 2007 American Thoracic Society
doi: 10.1164/rccm.200608-1205OC

Original Article

Use of Flow–Volume Curves to Predict Oral Appliance Treatment Outcome in Obstructive Sleep Apnea

Biao Zeng1,2, Andrew T. Ng2, M. Ali Darendeliler3, Peter Petocz4 and Peter A. Cistulli1,2,5

1 Centre for Sleep Health and Research, Department of Respiratory Medicine, Royal North Shore Hospital, University of Sydney, Sydney, Australia; 2 Department of Respiratory and Sleep Medicine, St. George Hospital, University of New South Wales, Sydney, Australia; 3 Discipline of Orthodontics, Sydney Dental Hospital, University of Sydney, Sydney, Australia; 4 Department of Statistics, Macquarie University, Sydney, Australia; and 5 Woolcock Institute of Medical Research, Sydney, Australia

Correspondence and requests for reprints should be addressed to Peter Cistulli, M.D., Ph.D., Centre for Sleep Health and Research, Royal North Shore Hospital, St. Leonards, NSW 2065, Australia. E-mail: cistullip{at}med.usyd.edu.au


   ABSTRACT
TOP
 ABSTRACT
AT A GLANCE COMMENTARY
 METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Background: It has been recognized that mandibular advancement splint (MAS) treatment is effective in some, but not all, patients with obstructive sleep apnea (OSA). Hence there is a need for a simple and reliable clinical tool to assist in the differentiation of treatment responses. We hypothesized that abnormalities of flow–volume curves, together with other clinical variables, may have clinical utility in the prediction of MAS treatment outcome.

Methods: Fifty-four patients with known OSA underwent MAS treatment. Expiratory and inspiratory flow–volume curves were measured in the erect and supine positions to derive midinspiratory flow (MIF50) and the ratio of expiratory to inspiratory flow at 50% of vital capacity (MEF50:MIF50). Multivariable logistic regression was performed to identify additional significant clinical variables in the prediction of treatment outcome.

Results: The mean (± SD) apnea–hypopnea index (AHI) in 35 responders was significantly reduced from 28.9 ± 13.7 to 6.7 ± 5.8/hour (p < 0.001). In 19 nonresponders there was no significant change in AHI. MIF50 was lower (6.04 ± 1.80 vs. 6.88 ± 1.08 L/second; p = 0.035) and the MEF50:MIF50 ratio was higher (0.82 ± 0.23 vs. 0.61 ± 0.15; p = 0.001) in responders than nonresponders. Logistic regression analysis revealed that the MEF50:MIF50 ratio was the most important predictive factor for MAS treatment outcome, but that body mass index, age, and baseline AHI were also contributory.

Conclusions: These data suggest that flow–volume curves, in combination with other factors such as body mass index, age, and baseline AHI, may have a useful clinical role in the prediction of treatment outcome with MAS.

Key Words: obstructive sleep apnea, spirometry • mandibular advancement splint



   AT A GLANCE COMMENTARY
TOP
 ABSTRACT
 AT A GLANCE COMMENTARY
METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Scientific Knowledge on the Subject
Oral appliance therapy is being used increasingly in obstructive sleep apnea; however, reliable predictors of treatment outcome remain to be determined.

What This Study Adds to the Field
Flow–volume curves may be useful in the prediction of treatment outcome associated with mandibular advancement splinting for obstructive sleep apnea.

 
Obstructive sleep apnea (OSA) is a common disorder, estimated to occur in 1.4 to 6.5% of men and in 1.1 to 2.5% of women (1–3), and is considered to be a serious public health problem. In addition to causing debilitating symptoms such as daytime sleepiness, neurocognitive impairment, and mood disturbance, there is now evidence linking OSA to hypertension, myocardial infarction, stroke (4–6), and increased risk of motor vehicle accidents (7). Continuous positive airway pressure has been widely established as the treatment of choice for OSA, but its cumbersome nature makes tolerance and compliance less than optimal, with a large proportion of patients declining or discontinuing treatment (8–10).

A growing evidence base indicates that oral appliances are an acceptable alternative to continuous positive airway pressure for some patients with OSA. Recently updated practice parameters endorse the use of oral appliances in patients with mild to moderate OSA and in those who fail other treatments (11). Mandibular advancement splints (MAS) are the commonest type of oral appliance and impact positively on the anatomy and physiology of the upper airway. Although these appliances can improve sleep-disordered breathing and daytime consequences in a substantial number of patients with OSA (12, 13), a suboptimal clinical outcome can occur in 30–50% of patients (14). The identification of reliable clinical predictors of treatment outcome remains elusive.

A number of studies have attempted to determine predictors of treatment outcome, using a variety of anthropomorphic, physiologic, and radiographic measurements, taken while the patient is either awake or asleep. While such studies have suggested that aspects of craniofacial structure, age, sex, and OSA severity are relevant to prediction, none of the models has been prospectively evaluated to confirm clinical utility (15). More recently, there have been reports of single-night titration of mandibular advancement, showing good positive and negative predictive value (16, 17). Although interesting, such an approach may be too complex and costly for routine clinical use. Hence, there is a need to develop simple, clinically useful tools to predict treatment outcome. We investigated the site of upper airway collapse during sleep and found that primary oropharyngeal collapse was highly predictive of treatment success (18). We questioned whether previously reported flow–volume curve abnormalities found in patients with OSA (19–22) could be used as a surrogate for the sleep-related site of upper airway collapse, and therefore assist in the prediction of treatment outcome. In a pilot study, we found significant differences in awake flow–volume curve parameters between patients with oropharyngeal versus velopharyngeal collapse during sleep (23). Hence, the aim of this study was to examine the potential clinical role of flow–volume curves, either alone or in combination with other clinical variables, in the prediction of MAS treatment outcome. Some of the results of this study have been previously reported in the form of an abstract (24).


   METHODS
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 ABSTRACT
 AT A GLANCE COMMENTARY
 METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Subjects
Patients were recruited from a multidisciplinary sleep disorders clinic in a university teaching hospital. Inclusion criteria included the presence of at least two symptoms of OSA, and evidence of OSA on polysomnography. Patients were excluded if there was periodontal disease, insufficient number of teeth, an exaggerated gag reflex, or a known history of lung disease. The study was approved by the institutional ethics committee, and written informed consent was obtained from all patients.

Flow–Volume Curve Measurements
A spirometer (SpiroPro; SensorMedics, Viasys Healthcare, Inc., Yorba Linda, CA) was used to record flow–volume curves before initiation of treatment. These were obtained with the patient in both the erect and supine positions according to American Thoracic Society guidelines (25). The subjects inspired maximally, then exhaled forcibly and continuously into the spirometer until residual volume was achieved, followed again by maximal inhalation with maximal effort (26). The following variables were derived: expiratory flow rate at 50% of vital capacity (MEF50), inspiratory flow rate at 50% of vital capacity (MIF50) and the ratio between the expiratory and inspiratory flow rates at 50% of vital capacity (MEF50:MIF50). The highest values of three technically satisfactory performances were used for this study.

Oral Appliance
A custom-made two-piece oral appliance was used (SomnoMed MAS; SomnoMed Ltd, Crows Nest, Australia), the design features and effectiveness of which have been previously published (12). Acclimatization occurred over a period of approximately 6 weeks, during which the appliance was incrementally advanced until the maximum comfortable limit of mandibular advancement was reached.

Nocturnal Polysomnography
Standard nocturnal polysomnography, as previously described (18, 27), was performed to determine treatment outcome, and was scored according to standard criteria (28, 29). In brief, apnea was defined as cessation of airflow for at least 10 seconds. Hypopnea was defined as a reduction in amplitude of airflow, measured as pressure change at the nares, or thoracoabdominal wall movement of greater than 50% of the baseline measurement for more than 10 seconds with an accompanying oxygen desaturation of at least 3% and/or associated with an arousal.

Treatment Outcome
We defined "responders" as patients who had a greater than 50% reduction in AHI and "nonresponders" as patients with a less than 50% reduction in AHI (criterion 1). Reflecting differences in the clinical definition of treatment success, we used additional definitions for comparative purposes, as follows: greater than 50% reduction and residual AHI less than 20/hour (criterion 2); and greater than 50% reduction in AHI and residual AHI less than 10/hour (criterion 3).

Statistical Analysis
Statistical analyses were performed with a statistical package (SPSS version 12; SPSS, Inc., Chicago, IL). All descriptive statistics are presented as means ± SD. Differences between groups were tested by independent samples Student t tests. Receiver operating characteristic (ROC) curves were constructed to derive cutoff predictive values of spirometric parameters. The {chi}2 test was used to compare the distribution of predictive values of spirometric parameters between responders and nonresponders. A p value less than 0.05 was considered significant. Multivariable logistic regression analysis was performed, with MAS treatment outcome as the dependent variable and independent variables including MIF50, MEF50:MIF50 ratio, age, sex, body mass index (BMI), baseline AHI, and neck circumference. A stepwise forward selection procedure was also performed to examine the effects of different variables and to identify the important explanatory variables. An ROC curve for the multivariate logistic regression model was constructed.


   RESULTS
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 RESULTS
DISCUSSION
 REFERENCES
 
Sixty-eight patients were screened for the study, and 14 were excluded on the basis of periodontal disease (n = 4), insufficient teeth (n = 3), other dental problems requiring intervention (n = 3), or a known history of lung disease (n = 4). Table 1 shows the clinical characteristics of the 54 eligible patients recruited into the study (40 men, 14 women), at baseline and after MAS treatment, as well as the results of the flow–volume curve parameters. MAS treatment was well tolerated by all patients, with only mild and transient side effects, including temporomandibular joint pain (10%), salivation (50%), tooth tenderness (40%), and gum irritation (8%). The mean (± SD) mandibular advancement with MAS was 6.6 ± 2.4 mm (range, 2.5–14 mm), representing a mean 70% of maximal mandibular protrusion. The MIF50 in responders was significantly lower than that of the nonresponders in both erect and supine positions. There was a significant difference in MEF50:MIF50 ratio between the two groups, with responders having a higher ratio. Postural comparisons (erect vs. supine) of both MIF50 and MEF50:MIF50 showed no significant difference between treatment outcome groups.


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  		TABLE 1. CLINICAL CHARACTERISTICS OF PATIENTS AND FLOW–VOLUME CURVE PARAMETERS ACCORDING TO TREATMENT OUTCOME

 
ROC curves were used to determine the cutoff value of MIF50 and the MEF50:MIF50 ratio, which could be used as a single or combined predictor for MAS treatment outcome. An MIF50 value of 6.0 L/second and an MEF50:MIF50 ratio of 0.7 in the erect position were found to be the best cutoff values for predicting MAS treatment outcome (Table 2). This combined cutoff criterion endpoint applied to 36 of the 54 (67%) patients (sensitivity, 81%; specificity, 87%; positive predictive value, 89%; negative predictive value, 76%; respectively). Table 3 compares the predictive capacity of flow–volume indices according to the three predefined treatment outcome criteria.


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  		TABLE 2. USE OF ERECT MIF50 OR MEF50:MIF50 RATIO CUTOFFS, SINGLY OR IN COMBINATION, IN PREDICTION OF TREATMENT SUCCESS

 

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  		TABLE 3. EFFECT OF TREATMENT OUTCOME CRITERIA ON PREDICTIVE UTILITY OF ERECT MIF50 AND MEF50:MIF50 RATIO

 
Multivariable logistic regression was used to develop a predictive equation. Flow–volume curve parameters (MIF50 and the MEF50:MIF50 ratio), BMI, age, sex, baseline AHI, and neck circumference were included in the logistic regression analysis. The MEF50:MIF50 ratio, BMI, baseline AHI, and age were found to be independent predictors of MAS treatment outcome. This analysis resulted in the following equation: log(OR) = 5.49 + (8.12 x MEF50:MIF50 ratio) – (0.251 x BMI) – (0.110 x age) + (0.101 x baseline AHI). OR is the odds ratio for treatment success as per the primary definition we used. This can be converted to a probability of MAS treatment success by the formula P = elog(OR)/[1 + elog(OR)]. An ROC curve of the multivariate logistic regression model, based on criterion 1, is shown in Figure 1. The area under the curve was 0.91, indicating excellent model discrimination. The effect of the other two treatment outcome definitions (criteria 2 and 3) on the model was assessed, and comparisons are summarized in Table 4. The MEF50:MIF50 ratio and BMI remained significant predictors regardless of the outcome definition used.


Figure 1
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  		Figure 1. Receiver operator characteristic (ROC) curve for the logistic regression model based on the criterion 1 definition of treatment outcome. Area under the ROC curve, 0.91.

 

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  		TABLE 4. MULTIVARIABLE PREDICTION OF MANDIBULAR ADVANCEMENT SPLINT TREATMENT OUTCOME ACCORDING TO DIFFERENT TREATMENT OUTCOME CRITERIA

 

   DISCUSSION
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 ABSTRACT
 AT A GLANCE COMMENTARY
 METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
Although oral appliances are now considered an acceptable treatment option in about two-thirds of patients with OSA, particularly for mild to moderate OSA, a key barrier to their use has been the inability to reliably predict treatment outcome. A number of different predictive factors have been suggested, but none has been prospectively evaluated for its clinical utility, and others are impractical, expensive, and labor intensive (17, 18, 30–33). We have attempted to overcome this barrier by developing a simple and clinically useful tool to assist in identifying patients who are suitable for this treatment modality. Our data suggest that flow–volume curve parameters, in combination with other clinical variables, offer substantial assistance in predicting treatment outcome. This predictive model is simple and inexpensive, and has good sensitivity and specificity. Hence, we believe it has the potential to become a useful prediction tool in the clinical practice setting.

Flow–volume curve abnormalities have been noted to occur commonly in patients with OSA (19–22). They include a "saw tooth" pattern, which consists of regular oscillations on the forced expiratory or forced inspiratory curve, or an increased MEF50:MIF50 ratio greater than 1. These patterns are thought to reflect dynamic upper airway collapse (19–22). Although the findings are neither sensitive nor specific for OSA, they probably indicate anatomic narrowing or neuromuscular alterations of the upper airway (19, 21, 34–37). The actual site of upper airway abnormality is unknown but potentially may reside within the oropharynx, as suggested by improvement of flow–volume curves on removal of severe tonsillar hypertrophy (38). Hence, our current findings support our previous work showing that upper airway collapse at the level of the oropharynx during sleep is a key predictor of MAS treatment outcome (18).

The key finding of this study is the observed significant differences in MIF50 and MEF50:MIF50 between MAS responders and nonresponders. Although there was overlap between the groups, we were able to derive cutoff values with acceptable positive and negative predictive values. The positive predictive value of an MEF50:MIF50 ratio greater than 0.7 was 83%, suggesting that this might be useful parameter to predict MAS treatment. Using a combined cutoff of MIF50 < 6.0 L/second and a MEF50:MIF50 ratio greater than 0.7 results in positive and negative predictive values of 89 and 76%, respectively. Importantly, 67% of patients who satisfied both these criteria were able to be classified in this way, suggesting a potential clinical role for this approach. Notably, different definitions for treatment success significantly changed the sensitivity, specificity, positive predictive value, and negative predictive value.

Multivariable logistic regression revealed that the MEF50:MIF50 ratio, baseline AHI, age, and BMI were significant independent predictors of MAS treatment outcome. MAS treatment outcome was strongly associated with the MEF50:MIF50 ratio, followed by BMI, age, and baseline AHI. When we applied more rigorous definitions of treatment success, baseline AHI was eliminated from the significant predictors in the logistic regression equation, followed by age. It is well recognized that obesity is an important risk factor for OSA, and so it is not surprising to find that lower BMI is associated with a better treatment outcome. As in other studies, we found that younger age was a predictor of a better response to MAS treatment (13). We speculate that this relates to the duration of OSA and the long-term impact on upper airway soft tissues and neuromuscular pathways, which may make the airway less amenable to improvement. Although previous studies have shown that lower baseline AHI is associated with a better response to MAS (14), it appears to be a minor contributor. Our finding of a positive association between baseline AHI and treatment response is counter to previous studies. Notably, this was found only for the least rigorous definition of treatment outcome, and was only a weak predictor as per our previous work (27).

This study has a number of limitations. There is the potential for selection and performance bias because patients were recruited from a sleep clinic with an established interest and expertise in oral appliance therapy, and this may have positively influenced treatment outcomes. The prediction cutoff values of flow–volume curve indices derived from our study population may not be applicable to different OSA populations. We specifically excluded patients with known lung disease, as this would affect the flow–volume curve, and hence the results of this study are unlikely to apply to patients with OSA with coexistent pulmonary disorders. The primary definition of treatment outcome used for this study can be criticized for being liberal. We deliberately chose this definition because it reflects clinical practice, whereby patients will often continue using MAS despite incomplete resolution of OSA. Comparison with more rigorous definitions demonstrated persistent predictive capacity of the flow–volume indices. All patients were treated with a single oral appliance design, and it is unknown whether the results are applicable to all types of appliances. Finally, although our study has prospectively derived a potentially useful prediction equation, validation is required in a further prospective cohort to verify the clinical utility of these findings.

In conclusion, these data suggest that flow–volume curves may have a useful clinical role in identifying patients with a higher likelihood of successful treatment with MAS. Combination with other known predictive factors provided good predictive power in our study population. Given the relative simplicity of this approach, further work is warranted to prospectively evaluate the clinical utility of this model.


   Acknowledgments
 
The authors thank Drs. Belinda Liu and Helen Gotsopoulos for the dental management of patients, Dr. Richard Lee for assistance with aspects of data analysis, and Dr. Jin Qian for assistance with sleep study data collection.


   FOOTNOTES
 
Supported by a project grant (no. 300525) from the National Health and Medical Research Council of Australia.

Originally Published in Press as DOI: 10.1164/rccm.200608-1205OC on January 11, 2007

Conflict of Interest Statement: B.Z. does not have a financial relationship with a commercial entity that has an interest in the subject of this manuscript. A.T.N. does not have a financial relationship with a commercial entity that has an interest in the subject of this manuscript. M.A.D. does not have a financial relationship with a commercial entity that has an interest in the subject of this manuscript. P.P. does not have a financial relationship with a commercial entity that has an interest in the subject of this manuscript. P.A.C. has previously served as a consultant/advisory board member to SomnoMed, the manufacturer of the oral appliance used in this study, and holds stock in the company.

Received in original form September 7, 2006; accepted in final form January 10, 2007


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 AT A GLANCE COMMENTARY
 METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

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