title: Multivariate prediction of skeletal Class II growth.
abstract: Prediction of craniofacial growth is one of the keys to successful orthodontic treatment and stability. Despite numerous attempts at growth forecasting, our ability to accurately predict growth is limited. The present study outlines a possible new approach to prediction of craniofacial growth that differs from any previous attempt because of both the methods used and type of patients studied. The purpose of this study is to create and test prediction equations for forecasting favorable or unfavorable patterns of growth in skeletal Class II preadolescents. The subjects for this study include 19 females and 12 males from the Bolton growth center in Cleveland, Ohio. The patients were all untreated orthodontically, had lateral cephalometric headfilms taken biannually from the ages of 6 through 18 and had a Class II skeletal relationship at age 8. Twenty-six skeletal and dental landmarks were identified and digitized, and 48 linear, angular, and proportional measurements were calculated. The subjects were divided into two groups based on anterior-posterior changes in the relationship between the maxilla and mandible. Eleven patients were in the favorable growth group, with an average improvement of 4.13 degrees in the ANB angle; 20 patients were in the unfavorable growth group with an average increase of 0.16 degrees in the ANB angle. The following prediction formula was created with Bayes theorem and assuming a multivariate Gaussian distribution: P(Good¿Fn) = ke (-(0.5)) ¿Fn - mu(ng)¿sigma(g)(-1)¿Fn - mu(ng)¿T. The equation's sensitivity and specificity was calculated from serial cephalometric data from ages 6, 8, 10, and 12. The results obtained with this equation indicate 82.2% sensitivity, 95% specificity with a overall positive predictive value of 91%. This corresponds to 17.8% of patients being incorrectly identified as Poor Growers and only 5% of our patients were incorrectly identified as Good Growers. We conclude that this prediction formula improves the ability to predict favorable or unfavorable patterns of growth in this sample of skeletal Class II preadolescents.
authors.affiliation: UCLA Department of Orthodontics, Los Angeles, Calif, USA.
authors.forename: D J
authors.initials: DJ
authors.lastname: Rudolph
authors.forename: S E
authors.initials: SE
authors.lastname: White
authors.forename: P M
authors.initials: PM
authors.lastname: Sinclair
mesh_terms: D000293:Adolescent; D000465:Algorithms; D001499:Bayes Theorem; D002508:Cephalometry / Q000706:statistics & numerical data; D002648:Child; D005260:Female; D005544:Forecasting; D006801:Humans; D008297:Male; D008312:Malocclusion, Angle Class II / Q000503:physiopathology*; D008445:Maxillofacial Development; D015999:Multivariate Analysis; D016011:Normal Distribution; D012016:Reference Values; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity