Functional imaging using computational fluid dynamics to predict treatment success of mandibular advancement devices in sleep-disordered breathing

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Abstract

Mandibular advancement devices (MADs) have emerged as a popular alternative for the treatment of sleep-disordered breathing. These devices bring the mandibula forward in order to increase upper airway (UA) volume and prevent total UA collapse during sleep. However, the precise mechanism of action appears to be quite complex and is not yet completely understood; this might explain interindividual variation in treatment success. We examined whether an UA model, that combines imaging techniques and computational fluid dynamics (CFD), allows for a prediction of the treatment outcome with MADs. Ten patients that were treated with a custom-made mandibular advancement device (MAD), underwent split-night polysomnography. The morning after the sleep study, a low radiation dose CT scan was scheduled with and without the MAD. The CT examinations allowed for a comparison between the change in UA volume and the anatomical characteristics through the conversion to three-dimensional computer models. Furthermore, the change in UA resistance could be calculated through flow simulations with CFD. Boundary conditions for the model such as mass flow rate and pressure distributions were obtained during the split-night polysomnography. Therefore, the flow modeling was based on a patient specific geometry and patient specific boundary conditions. The results indicated that a decrease in UA resistance and an increase in UA volume correlate with both a clinical and an objective improvement. The results of this pilot study suggest that the outcome of MAD treatment can be predicted using the described UA model.

Introduction

The awareness of the potentially serious consequences of sleep related breathing disorders (SBD) such as obstructive sleep apnea hypopnea syndrome (OSAHS) is increasing. OSAHS is characterized by recurrent episodes of partial or complete collapse of the upper airway. This may lead to a fragmentation of the sleep pattern, a decrease in oxygen saturation and a partial pressure rise of CO2 in the blood. The arousals and the nocturnal hypoxemia can cause excessive daytime sleepiness, loss of concentration, hypertension and atherosclerosis. In some cases, the pronounced presence of OSAHS may lead to stroke and even heart failure, resulting in an increased prevalence of cardiovascular morbidity and mortality (De Backer, 2006; American Academy of Sleep Medicine Task Force, 1999). Nowadays, the golden standard to assess the severity of sleep apnea is the apnea–hypopnea index (AHI). This index presents the number of UA narrowings and closures per hour. An AHI above 5 events h−1 in combination with complaints is regarded as an OSAHS.

Mandibular advancement devices (MADs), which are worn intra-orally at night to advance the lower jaw, have emerged as a non-invasive treatment for SBD (Marklund et al., 2004; Lim et al., 2006; Vanderveken et al., 2004). There is evidence that MADs can significantly reduce the collapsibility of the upper airway (UA) during sleep (Kato et al., 2000; Ng et al., 2003; Huang et al., 2005). The widening of the pharyngeal cross-sectional area (CSA) as induced by MADs can occur at the level of velo-, oro- and/or hypopharynx (Ryan et al., 1999; Kato et al., 2000; Tsuiki et al., 2004, Tsuiki et al., 2005). Both the reduction in pharyngeal collapsibility and the widening of CSA, caused by MADs, have been reported to be dose-dependent (Kato et al., 2000; Tsuiki et al., 2005). However, in recent studies that compare different degrees of mandibular advancement, a more pronounced mandibular advancement did not lead to a greater improvement compared to less advancement for patients with mild to moderate OSAHS (Tegelberg et al., 2003; Walker-Engstrom et al., 2003).

MAD therapy may be a first line treatment in snorers without (primary) or with (non-apneic) excessive daytime sleepiness and in patients with mild to moderate OSAHS (Kushida et al., 2006; Ferguson et al., 2006; Hoekema et al., 2004). Treatment with MADs may also be considered in subjects with OSAHS who do not tolerate or comply with continuous positive airway pressure (CPAP) or as a temporary alternative (Lim et al., 2006; Smith and Stradling, 2002). Although promising, success rate with MADs is limited, and a high interindividual variability has been noted (Marklund, 2006a). Therefore, it would be worthwhile to have a tool to distinguish responders from non-responders. However, the data in the literature on predictability of treatment outcome with MADs are not conclusive and more research is needed to develop a adequate prediction method (Cistulli and Gotsopoulos, 2004; Cistulli et al., 2004). Recently, we have developed an UA model based on the principles of computational fluid dynamics (CFD) to develop an adequate prediction tool for interventions in the UA. (Vos et al., 2007). Computed tomography (CT) data are transformed into computational models and analyzed through CFD. In this paper, results of a pilot study using this UA model for the assessment of treatment success with MAD in SBD are discussed.

Section snippets

Patient data and mandibular advancement device

For this study 10 adult patients with heavy snoring and an apnea–hypopnea index (AHI) <40 h−1 on standard polysomnography (PSG) were selected. All of these patients were treated with a custom-made MAD. The custom-made MAD used in this study (Fig. 1) was fabricated by a dental technician based on individually made plaster casts of the teeth and a bite registration taken by our dentist; the MAD was constructed at the dental laboratory of the Umeå University, Sweden (Marklund et al., 1998, Marklund

Results

An overview of the patient data and the results of PSG and UA modeling are given in Table 1.

The PSG showed for all 10 patients an average AHI of 17.2±10.3. The MAD treatment was unsuccessful in 3 out of 10 patients (33.3%). The successfully treated patients had an average AHI change of 16.9±8.4. The non-responders had an average AHI change of −1.3±2.1.

A positive change or enlargement in UA volume correlates (r=−0.70, p=0.02) with clinical improvement (Fig. 3). The responders had an average

Discussion

The precise mechanism of action of MADs is quite complex and not yet completely understood. Interindividual variations in treatment success are obvious and important, while the treatment success is difficult to predict.

Many papers assess the issue of possible predictors of treatment success with MAD. Mehta et al. (2001) were able to develop a predictive model based on a positive correlation between success with MAD and both neck circumference and baseline AHI. Marklund et al. (1998) reported

Conflict of interest

This manuscript has been prepared according to all ethical and scientific guidelines. No conflict of interest existed during the course of this study and the preparation of the manuscript.

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