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Updated: Aug 28, 2026

Finite Element Analysis Model for Assessing Expansion Patterns from Surgically Assisted Rapid Palatal Expansion
Published on: October 20, 2023
Factors influencing complete midpalatal suture separation in adult patients undergoing 3D-guided
Svitlana Koval1, Daria Chepanova1, Nika Stepanoff1
1DrKoval Orthodontics, Private Practice, Boca Raton, FL, 33341, USA.
Objectives:
To evaluate demographic, cephalometric, and craniometric biomarker (frontozygomatic separation) predictors of full versus partial midpalatal suture (MPS) separation following 3D-guided piezocorticotomy-assisted MARPE, and characterize separation morphologies.
Methods:
A retrospective case-control study analyzed 108 adults (70 full separation controls; 38 partial separation cases). Separation patterns were categorized (Types 1-5). Multivariable logistic regression assessed biological sex, age, cephalometrics ( , ), PNS-PPW width, and frontozygomatic separation. Model discrimination was evaluated via Area Under the ROC Curve ( ). Linear combinations tested sex-specific age interactions ( , , years).
Results:
Five distinct MPS separation morphologies were identified. Baseline demographic, cephalometric, and post-expansion parameters showed no group differences ( ). Multivariable regression ( , , , Pseudo ) demonstrated that baseline ( ) and ( ) did not predict separation. Frontozygomatic separation was a significant positive predictor ( , , ). Conversely, male sex ( , ) and advancing age ( , ) significantly reduced separation likelihood. Adding the biomarker increased classification accuracy by ( ; , ). Stratified analysis revealed age-related declines operated predominantly in males ( ), with males aged demonstrating an odds reduction ( , ), whereas females ( ) maintained stable odds ( ).
Conclusions:
MPS separation manifests in five morphological types. Full separation is uninfluenced by baseline cephalometrics but constrained by male sex and advancing age (precipitously after age 40 in males). Frontozygomatic separation serves as a strong structural predictor.
