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

Abstract

One hundred male obstructive sleep apnea (OSA) patients were classified into 2 groups on the basis of apnea-hypopnea index (AHI) as severe (AHI > or = 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 (PC 2.1) - 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.