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骨性II类错颌生长的多变量预测

Multivariate prediction of skeletal Class II growth.

作者信息

Rudolph D J, White S E, Sinclair P M

机构信息

UCLA Department of Orthodontics, Los Angeles, Calif, USA.

出版信息

Am J Orthod Dentofacial Orthop. 1998 Sep;114(3):283-91. doi: 10.1016/s0889-5406(98)70210-0.

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.

摘要

颅面生长预测是正畸治疗成功与稳定的关键之一。尽管人们多次尝试进行生长预测,但我们准确预测生长的能力仍然有限。本研究概述了一种颅面生长预测的新方法,该方法因所使用的方法和所研究患者的类型而与以往的任何尝试都有所不同。本研究的目的是创建并测试预测方程,以预测骨骼II类青春期前患者的有利或不利生长模式。本研究的受试者包括来自俄亥俄州克利夫兰市博尔顿生长中心的19名女性和12名男性。这些患者均未接受过正畸治疗,从6岁到18岁每两年拍摄一次头颅侧位X线片,8岁时为II类骨骼关系。确定并数字化了26个骨骼和牙齿标志点,并计算了48项线性、角度和比例测量值。根据上颌骨与下颌骨关系的前后变化,将受试者分为两组。11名患者属于有利生长组,ANB角平均改善4.13度;20名患者属于不利生长组,ANB角平均增加0.16度。利用贝叶斯定理并假设多元高斯分布创建了以下预测公式:P(Good¿Fn) = ke (-(0.5)) ¿Fn - mu(ng)¿sigma(g)(-1)¿Fn - mu(ng)¿T。该方程的敏感性和特异性根据6岁、8岁、10岁和12岁的系列头颅侧位片数据计算得出。用该方程得到的结果显示敏感性为82.2%,特异性为95%,总体阳性预测值为91%。这意味着17.8%的患者被错误地认定为生长不良者,而只有5%的患者被错误地认定为生长良好者。我们得出结论,该预测公式提高了预测该骨骼II类青春期前患者样本有利或不利生长模式的能力。

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