AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE VOL 175 2007
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
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
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
Complete article http://ajrccm.atsjournals.org/cgi/reprint/175/7/726.pdf