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Predicting primary stability of orthodontic mini-implants, according to position, screw-size, and bone quality, in the maxilla of aged patients: a cadaveric study.

Research paper by Stephan C SC Möhlhenrich, Nicole N Heussen, Philipp P Winterhalder, Andreas A Prescher, Frank F Hölzle, Ali A Modabber, Michael M Wolf, Kristian K Kniha

Indexed on: 21 Dec '20Published on: 26 Sep '19Published in: European Journal of Oral Sciences



Abstract

This study aimed to evaluate the effect of implant size and bone condition on primary stability of orthodontic mini-implants with a view to predict the primary stability before insertion. Four-hundred and forty mini-implants of two different diameters (2.0 and 2.3 mm) and lengths (7 and 12 mm) were inserted at 11 different positions in human cadaver maxillae. Before placement of mini-implants, cone beam computed tomography (CBCT) scans were performed to measure bone density and cortical thickness and, after mini-implant placement, implant stability quotient (ISQ) values were determined by resonance frequency analysis and cofactors were analyzed to determine their influence on the primary stability. Additionally, an equation was developed to predict the expected stability based on implant size and bone quality. Bone density varied between 433 (SD 122) and 587 (SD 249) Hounsfield units (HU), and cortical thickness varied between 0.49 (SD 0.42) and 0.98 (SD 0.60) mm. The lowest ISQ value, of 15.50 (SD 7.29) (bone density: 531 (SD 219) HU; cortical thickness: 0.65 (SD 0.58) mm), was found for a mini-implant of 2.0 × 7 mm and the highest ISQ value, of 46.30 (SD 8.69) (bone density: 587 (SD 249) HU; cortical thickness: 0.98 (SD 0.60) mm), was found for a mini-implant of 2.3 × 11 mm. Statistically significant influences of the cofactors on primary stability were demonstrated. To visualize the predictive power of the model, the observed values versus the predicted values of primary stability were compared and the model fit was represented by residual plots. The expected primary stability can be estimated by a linear regression model comprising the radiologically determined bone density, cortical thickness, implant length and diameter, and placement position. © 2019 Eur J Oral Sci.