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The International Journal of Adult Orthodontics & Orthogathic Surgery
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Publication:
International Journal of Adult Orthodontics and Orthognathic Surgery

Year 2000
Volume 15 , Issue 3

Back
Pages: 179 - 191

Obstructive sleep apnea (OSA): A cephalometric analysis of severe and non-severe OSA patients. Part II: A predictive discriminant function analysis

Vivat Tangugsorn, DDS, MScD, Dip Orth, Cert OMFS/Olaf Krogstad, DDS, PhD/Lisen Espeland, DDS, PhD/Torstein Lyberg, DDS, MD, PhD

One hundred male obstructive sleep apnea (OSA) patients were classified into 2 groups on the basis of apnea-hypopnea index (AHI) as severe (AHI >= 50) and non-severe (AHI < 50). A comprehensive cephalometric analysis of cervicocraniofacial skeletal and upper airway soft tissue morphology was performed in 51 non-severe and 49 severe OSA patients. In addition, a multivariate statistical method (principal component analysis and predictive discriminant analysis) was performed to identify the components that could correctly differentiate the severe from the non-severe OSA patients. Eight principal components (PCs) of cervicocraniofacial skeletal morphology, 4 PCs of hyoid bone position and head posture, and 7 PCs of upper airway soft tissue morphology, together with the selected demographic variables, were deduced to formulate a linear canonical discriminant function. The equation of a 9-variable model was generated as follows: PDF score = 4.127 0.144 (Body Mass Index) 0.376 (PC1.1) + 0.311 (PC1.4) + 0.214 (PC1.5) + 0.075 (PC2.1) 1.309 (PC2.3) + 0.708 (PC3.2) 0.059 (PC3.3) + 0.609 (PC3.6). The cutoff score was 0.03899. The overall rate of correct classification was 83%. The results showed that Body Mass Index and 8 other PCs contributed significantly to the OSA severity. These analyses are proven to be a useful adjunctive diagnostic tool to select optimal treatment regimens for OSA patients with varying degrees of severity.

 

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