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Classification of Skeletal Phenotypes of Adult Patients With Cleft Skeletal Class III Malocclusion Using Principal Component Analysis and Cluster Analysis.

The purpose of this study was to classify the skeletal phenotypes of adult patients with skeletal class III (C-III) malocclusion and unilateral or bilateral cleft lip and palate using principal component analysis and cluster analysis. The samples consisted of 81 adult C-III patients with cleft lip and palate (CLP) who underwent orthognathic surgery (OGS) or distraction osteogenesis (59 males and 22 females; 50 unilateral cleft lip and palate and 31 bilateral cleft lip and palate; mean age when lateral cephalograms were taken, 22.2±4.6 y). Thirteen angular and one ratio cephalometric variables were measured. Using 4 representative variables obtained from principal component analysis (SNA, SNB, Gonial angle, and Bjork sum), K-means cluster analysis was performed to classify the phenotypes. Then, statistical analysis was conducted to characterize the differences in the variables among the clusters. Five clusters were obtained from 3 groups: severely retrusive maxilla and moderately retrusive mandible group: cluster-1 (23.5%, severely hyperdivergent pattern), cluster-4 (27.2%, moderately hyperdivergent pattern), and cluster-5 (11.1%, normodivergent pattern); moderately retrusive maxilla and normal mandible group: cluster-2 (30.9%, normodivergent pattern); normal maxilla and moderately protrusive mandible group: cluster-3 (7.4%, normodivergent pattern). Although skeletal phenotypes were diverse, distribution of sex and cleft type did not differ among 5 clusters (P>0.05). Sixty-two percent of cleft patients showed a severely retrusive maxilla and moderately retrusive mandible (cluster-1, cluster-4, and cluster-5), which indicated that these are the main cause of skeletal C-III malocclusion in CLP patients who were treated with OGS. Therefore, it is necessary to consider presurgical orthodontic treatment and surgical planning based on the skeletal phenotypes of CLP patients.

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