A more accurate soft-tissue prediction model for Class III 2-jaw surgeries.

INTRODUCTION: The use of bimaxillary surgeries to treat Class III malocclusions makes the results of the surgeries more complicated to estimate accurately. Therefore, our objective was to develop an accurate soft-tissue prediction model that can be universally applied to Class III surgical-orthodontic patients regardless of the type of surgical correction: maxillary or mandibular surgery with or without genioplasty. METHODS: The subjects of this study consisted of 204 mandibular setback patients who had undergone the combined surgical-orthodontic correction of severe skeletal Class III malocclusions. Among them, 133 patients had maxillary surgeries, and 81 patients received genioplasties. The prediction model included 226 independent and 64 dependent variables. Two prediction methods, the conventional ordinary least squares method and the partial least squares (PLS) method, were compared. When evaluating the prediction methods, the actual surgical outcome was the gold standard. After fitting the equations, test errors were calculated in absolute values and root mean square values through the leave-1-out cross-validation method. RESULTS: The validation result demonstrated that the multivariate PLS prediction model with 30 orthogonal components showed the best prediction quality among others. With the PLS method, the pattern of prediction errors between 1-jaw and 2-jaw surgeries did not show a significantly difference. CONCLUSIONS: The multivariate PLS prediction model based on about 30 latent variables might provide an improved algorithm in predicting surgical outcomes after 1-jaw and 2-jaw surgical corrections for Class III patients.

A more accurate method of predicting soft tissue changes after mandibular setback surgery.

PURPOSE: To propose a more accurate method to predict the soft tissue changes after orthognathic surgery. PATIENTS AND METHODS: The subjects included 69 patients who had undergone surgical correction of Class III mandibular prognathism by mandibular setback. Two multivariate methods of forming prediction equations were examined using 134 predictor and 36 soft tissue response variables: the ordinary least-squares (OLS) and the partial least-squares (PLS) methods. After fitting the equation, the bias and a mean absolute prediction error were calculated. To evaluate the predictive performance of the prediction equations, a 10-fold cross-validation method was used. RESULTS: The multivariate PLS method showed significantly better predictive performance than the conventional OLS method. The bias pattern was more favorable and the absolute prediction accuracy was significantly better with the PLS method than with the OLS method. CONCLUSIONS: The multivariate PLS method was more satisfactory than the conventional OLS method in accurately predicting the soft tissue profile change after Class III mandibular setback surgery.

Do Class III patients have a different growth spurt than the general population?

INTRODUCTION: Understanding the timing and length of the growth spurt of Class III prognathic patients is fundamental to the strategy of interceptive orthopedic orthodontics as well as to the timing of orthognathic surgery. Consequently, this study was undertaken to determine whether there are any significant differences in the stature growth pattern of Class III subjects compared with non-Class III subjects and the general population. METHODS: Twelve-year longitudinal stature growth data were collected for 402 randomly selected adolescents in the general population, 55 Class III mandibular prognathic patients, and 37 non-Class III patients. The growth data were analyzed by using the traditional linear interpolation method and nonlinear growth functions. The 6 stature growth parameters were measured: age at takeoff, stature at takeoff, velocity at takeoff, age at peak height velocity, stature at peak height velocity, and velocity at peak height velocity. Comparisons in the stature growth parameters and 15 cephalometric variables among the general population, Class III subjects, and non-Class III subjects were made with multivariate analysis. RESULTS: Patients with Class III prognathism did not have different growth parameters compared with Class II subjects or the general population. CONCLUSIONS: This study does not allow meaningful conclusions with regard to the relationship of mandibular size and stature growth pattern. The application of nonlinear growth curves vs the traditional linear interpolation method was also discussed.

A better statistical method of predicting postsurgery soft tissue response in Class II patients.

OBJECTIVE: To propose a better statistical method of predicting postsurgery soft tissue response in Class II patients. MATERIALS AND METHODS: The subjects comprise 80 patients who had undergone surgical correction of severe Class II malocclusions. Using 228 predictor and 64 soft tissue response variables, we applied two multivariate methods of forming prediction equations, the conventional ordinary least squares (OLS) method and the partial least squares (PLS) method. After fitting the equation, the bias and a mean absolute prediction error were calculated. To evaluate the predictive performance of the prediction equations, a leave-one-out cross-validation method was used. RESULTS: The multivariate PLS method provided a significantly more accurate prediction than the conventional OLS method. CONCLUSION: The multivariate PLS method was more satisfactory than the OLS method in accurately predicting the soft tissue profile change after surgical correction of severe Class II malocclusions.