IADR Abstract Archives
Class II and III Malocclusion Craniofacial Growth Predictors. A Systematic Review
Objectives: In order to evaluate the validity of craniofacial growth predictors in class II and III malocclusion.
Methods: An electronic search was conducted between 1960 and 2018 in PubMed, Cochrane Library, Embase, EBSCOhost, ScienceDirect, Bireme and Scielo including all languages. The articles were selected and analyzed by two authors independently and the methodology of selected studies was assessed using the 14-item Quality Assessment Tool for Diagnostic Accuracy Studies (QUADAS-2). The quality of evidence and strength of recommendation was assessed by The Grading of Recommendations Assessment Tool, Development and Evaluation (GRADE).
Results: In a process selection of two phases, 19 studies were identified. The studies were grouped according to malocclusion growth predictor in (a) class II (n=6); (b) class III (n=7); (c) class II and III (n=3) and (d) others (n=3). The predictors were mainly based on data extracted from cephalometries and characterized by: equations, techniques and computer programs, structural analysis among others. Only 3 studies included predictors based on DNA isolation, IGF-1 levels and P561T polymorphism. The analyzed studies were methodologically heterogeneous and had low quality. None of the studies fulfilled all of the QUADAS-2. Three studies were graded as moderate value of evidence; For class II malocclusion, the predictors were based on multivariate analysis by logistic regression and mathematical model (orthogonal polynomial based on 15 cephalometric measurements). For class II and III malocclusion, the Fishman system of maturation assessment would provide the best means of predicting both short- and long-term growth using Cranial Centroid and cranial measures (CC-A; CC-Gn; CCNA; CCNGn).
Conclusions:
The evidence available based on cephalometric, molecular and genetic methods were not conclusive as growth predictor for class II and III malocclusion since the studies were heterogeneous and presented high risk of bias. Develop methods to evaluate growth predictors requires further research with rigorous scientific methodologies.
Methods: An electronic search was conducted between 1960 and 2018 in PubMed, Cochrane Library, Embase, EBSCOhost, ScienceDirect, Bireme and Scielo including all languages. The articles were selected and analyzed by two authors independently and the methodology of selected studies was assessed using the 14-item Quality Assessment Tool for Diagnostic Accuracy Studies (QUADAS-2). The quality of evidence and strength of recommendation was assessed by The Grading of Recommendations Assessment Tool, Development and Evaluation (GRADE).
Results: In a process selection of two phases, 19 studies were identified. The studies were grouped according to malocclusion growth predictor in (a) class II (n=6); (b) class III (n=7); (c) class II and III (n=3) and (d) others (n=3). The predictors were mainly based on data extracted from cephalometries and characterized by: equations, techniques and computer programs, structural analysis among others. Only 3 studies included predictors based on DNA isolation, IGF-1 levels and P561T polymorphism. The analyzed studies were methodologically heterogeneous and had low quality. None of the studies fulfilled all of the QUADAS-2. Three studies were graded as moderate value of evidence; For class II malocclusion, the predictors were based on multivariate analysis by logistic regression and mathematical model (orthogonal polynomial based on 15 cephalometric measurements). For class II and III malocclusion, the Fishman system of maturation assessment would provide the best means of predicting both short- and long-term growth using Cranial Centroid and cranial measures (CC-A; CC-Gn; CCNA; CCNGn).
Conclusions:
The evidence available based on cephalometric, molecular and genetic methods were not conclusive as growth predictor for class II and III malocclusion since the studies were heterogeneous and presented high risk of bias. Develop methods to evaluate growth predictors requires further research with rigorous scientific methodologies.