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3D Planning and Printing of Patient Specific Implants for Reconstruction of Bony Defects
Published on: August 4, 2020
Residual Jaw Bone Volume and Five-Year Outcomes of Custom-Made Subperiosteal Implants: A Retrospective Study
Jan Łoginoff1, Zuzanna Popińska2, Agata Majos1
1II Department of Radiology and Diagnostic Imaging, Central Clinical Hospital, Medical University of Lodz, 251 Pomorska Street, 92-213 Lodz, Poland.
Background:
Custom subperiosteal implants represent an alternative for severe jaw atrophy, yet long-term quantitative bone predictors remain limited. This retrospective study aimed to evaluate 5-year implant survival and determine preoperative volumetric bone thresholds using 3D CT segmentation.
Methods:
The unit of analysis was individual implant sites in 40 patients (25 maxillae and 15 mandibles). Primary outcomes were 5-year cumulative implant survival (Kaplan-Meier method) and preoperative total jaw bone volume (cm3) measured via standardized 3D segmentation using BrainLab software (version 1.7.0.116). Group comparisons between surviving and failed sites were performed with 95% confidence intervals (CIs).
Results:
Overall 5-year Kaplan-Meier implant survival was 77.5% (31/40; 95% CI: 64.6-90.4%). Maxillary sites achieved a survival rate of 80.0% (20/25; 95% CI: 64.3-95.7%), whereas mandibular sites achieved 73.3% (11/15; 95% CI: 51.0-95.6%). Preoperative jaw bone volume was significantly lower in failure cases across both maxillary and mandibular cohorts (p < 0.05). Nine implants failed due to biological (n = 4), mechanical (n = 2), or combined (n = 3) complications.
Conclusions:
Residual bone volume exhibits a statistically significant association with framework survival, whereas the categorical Cawood and Howell classification showed no statistically significant association in this cohort. These findings suggest that continuous volumetric assessment may serve as a useful exploratory parameter for surgical risk stratification, though predictive superiority requires validation in larger, adjusted multivariable models.
