Children with class III malocclusion: development of multivariate statistical models to predict future need for orthognathic surgery.
Until now, the literature does not provide an accurate model to predict the future need for orthognathic surgery in prepubertal patients with class III malocclusion. Because not all of these patients are candidates for later surgical correction, patient assessment and selection remain arbitrary with respect to diagnosis and treatment planning. The purpose of the present investigation was to analyze the value of classifying class III children before puberty into patients who can be effectively treated by orthopedic/orthodontic therapy alone and those who require orthognathic surgery. To obtain a robust model, the study design was multicentric (University Orthodontic Departments of Frankfurt, Heidelberg, and Würzburg). A total of 88 patients with class III malocclusion were grouped into orthopedic/orthodontic (n = 65) and surgery patients (n = 23), according to their records after puberty (mean age, 17 years three months). Discriminant analysis (DA) and logistic regression (LogR) were applied to 20 landmarks of the patients' cephalograms before puberty (mean age, nine years eight months) to identify the dentoskeletal variables that provide the best group separation and the best predictability of group membership, respectively. Both models were highly significant (P < .001), classifying 93.3% (DA) and 94.3% (LogR) of the patients correctly. The extracted variables were identical for both procedures: Wits appraisal, palatal plane angle, and individualized inclination of the lower incisors. The resulting equation of LogR was individual score = -7.968 - 1.323Wits - 0.363NL-NSL + 0.153[180 - (LI-ML) - (L1-ML(ind))]. We concluded that by means of multivariate statistics, prepubertal children with class III malocclusions may be classified into nonsurgery and surgery patients with high accuracy.
Department of Orthodontics, University of Frankfurt, Frankfurt, Germany. g.schuster@em.uni-frankfurt.de
Gabriele
G
Schuster
Christopher J
CJ
Lux
Angelika
A
Stellzig-Eisenhauer
D000293:Adolescent; D000704:Analysis of Variance; D002508:Cephalometry / Q000706:statistics & numerical data; D002648:Child; D016002:Discriminant Analysis; D005260:Female; D005544:Forecasting; D006801:Humans; D007180:Incisor / Q000473:pathology; D016015:Logistic Models; D008297:Male; D008313:Malocclusion, Angle Class III / Q000145:classification / Q000601:surgery* / Q000628:therapy; D008334:Mandible / Q000473:pathology; D008437:Maxilla / Q000473:pathology; D015233:Models, Statistical; D015999:Multivariate Analysis; D020380:Needs Assessment / Q000706:statistics & numerical data; D009971:Orthodontics, Corrective / Q000706:statistics & numerical data; D010159:Palate / Q000473:pathology; D010347:Patient Care Planning / Q000706:statistics & numerical data*; D011627:Puberty