AIM: To develop and validate a prediction model to forecast long-term stability of early treatment with rapid maxillary expansion (RME) and facemask (FM) in a large sample of Class III growing patients. METHODS: The Brazilian Group (BG) consisted of 73 consecutively treated Caucasian Class III patients (41 females and 32 males). Mean age at T0 (before treatment) was 7.1 ± 1.6 years, while mean age at T1 (long-term follow-up) was 21.8 ± 3.2 years. The Italian Group (IG, validation cohort) comprised 28 consecutively treated Caucasian Class III patients (14 females and 14 males, mean age at T0 9.0 ± 1.3 years and mean age at T1 18.2 ± 1.4 years). Cephalometric analysis was performed on lateral cephalograms at T0. Gender and cephalometric variables, chronologic age, and dentition phase at T0 were used as predictors for long-term unsuccessful treatment at T1. All predictors for unsuccessful treatment in the BG were subjected to bivariate logistic regression. Only those statistically significant predictors in the bivariate logistic regression entered mixed stepwise logistic regression with P = 0.05 to enter and to leave. The validity of the prediction model derived from the BG was then tested on the IG. RESULTS: The prediction model consisted of only one cephalometric variable: the angle between the Condylar Axis and the Mandibular Plane (CondAx-MP) (odds ratio: 1.52, 95% confidence interval: 1.25-1.85, P < 0.0001). Unsuccessful treatment at T1 was predicted for values of CondAx-MP at T0 greater than the cut-off value of 147.8 degrees. BG patients predicted incorrectly were 3 out of 22 for the unsuccessful cases and 1 out of 51 for the successful cases. Therefore, accuracy was 0.95, sensitivity 0.86, specificity 0.98, and positive and negative predictive values were 0.95 and 0.94. When the predictive model was applied on IG, all five unsuccessful cases were predicted correctly, while only 1 out of 23 successful patients was predicted incorrectly. CONCLUSION: CondAx-ML was identified as a reliable predictor for long-term stability of early Class III treatment with RME and FM.
AIM: To develop and validate a prediction model to forecast long-term stability of early treatment with rapid maxillary expansion (RME) and facemask (FM) in a large sample of Class III growing patients. METHODS: The Brazilian Group (BG) consisted of 73 consecutively treated Caucasian Class III patients (41 females and 32 males). Mean age at T0 (before treatment) was 7.1 ± 1.6 years, while mean age at T1 (long-term follow-up) was 21.8 ± 3.2 years. The Italian Group (IG, validation cohort) comprised 28 consecutively treated Caucasian Class III patients (14 females and 14 males, mean age at T0 9.0 ± 1.3 years and mean age at T1 18.2 ± 1.4 years). Cephalometric analysis was performed on lateral cephalograms at T0. Gender and cephalometric variables, chronologic age, and dentition phase at T0 were used as predictors for long-term unsuccessful treatment at T1. All predictors for unsuccessful treatment in the BG were subjected to bivariate logistic regression. Only those statistically significant predictors in the bivariate logistic regression entered mixed stepwise logistic regression with P = 0.05 to enter and to leave. The validity of the prediction model derived from the BG was then tested on the IG. RESULTS: The prediction model consisted of only one cephalometric variable: the angle between the Condylar Axis and the Mandibular Plane (CondAx-MP) (odds ratio: 1.52, 95% confidence interval: 1.25-1.85, P < 0.0001). Unsuccessful treatment at T1 was predicted for values of CondAx-MP at T0 greater than the cut-off value of 147.8 degrees. BG patients predicted incorrectly were 3 out of 22 for the unsuccessful cases and 1 out of 51 for the successful cases. Therefore, accuracy was 0.95, sensitivity 0.86, specificity 0.98, and positive and negative predictive values were 0.95 and 0.94. When the predictive model was applied on IG, all five unsuccessful cases were predicted correctly, while only 1 out of 23 successful patients was predicted incorrectly. CONCLUSION: CondAx-ML was identified as a reliable predictor for long-term stability of early Class III treatment with RME and FM.
Authors: Marianna Pellegrino; Silvia Caruso; Tiziana Cantile; Gioacchino Pellegrino; Gianmaria Fabrizio Ferrazzano Journal: Int J Environ Res Public Health Date: 2020-05-20 Impact factor: 3.390
Authors: L de Frutos-Valle; C Martin; J A Alarcón; J C Palma-Fernández; R Ortega; A Iglesias-Linares Journal: Sci Rep Date: 2020-10-21 Impact factor: 4.379