Margarida Sampaio Fernandes1, Paula Vaz2, Ana Cristina Braga3, João Carlos Sampaio Fernandes4, Maria Helena Figueiral5. 1. Department of Removable Prosthesis, Faculty of Dental Medicine of the University of Porto, Portugal. Electronic address: mfernandes@fmd.up.pt. 2. Department of Orofacial Genetics, Faculty of Dental Medicine of the University of Porto, Portugal. Electronic address: pvaz@fmd.up.pt. 3. Department of Production and Systems Engineering-Algoritmi Centre, University of Minho, Braga, Portugal. Electronic address: acb@dps.uminho.pt. 4. Department of Fixed Prosthesis, Faculty of Dental Medicine of the University of Porto, Portugal. Electronic address: jfernandes@fmd.up.pt. 5. Department of Removable Prosthesis, Faculty of Dental Medicine of the University of Porto, Portugal. Electronic address: mhsilva@fmd.up.pt.
Abstract
PURPOSE: Implant-supported overdentures are an alternative predictable rehabilitation method that has a high impact on improving the patient's quality of life. However, some biological complications may interfere with the maintenance and survival of these overdenture implants. The goal of this article was to assess the factors that affect peri-implant success, through a hypothetical prediction model for biological complications of implant overdentures. METHODS: A retrospective observational, prevalence study was conducted in 58 edentulous Caucasian patients rehabilitated with implant overdentures. A total of 229 implants were included in the study. Anamnestic, clinical, and implant-related parameters were collected and recorded in a single database. "Patient" was chosen as the unit of analysis, and a complete screening protocol was established. The data analytical study included assessing the odds ratio, concerning the presence or absence of a particular risk factor, by using binary logistic regression modeling. Probability values (p values) inferior to 0.05 were considered as representing statistically significant evidence. RESULTS: The performed prediction model included the following variables: mean probing depth, metal exposure, IL1B_allele2, maxillary edentulousness, and Fusobacterium nucleatum. The F. nucleatum showed significant association with the outcome. Introducing a negative coefficient appeared to prevent complications or even boost the biological defense when associated with other factors. CONCLUSIONS: The prediction model developed in this study could serve as a basis for further improved models that would assist clinicians in the daily diagnosis and treatment planning practice of oral rehabilitation with implant overdentures.
PURPOSE: Implant-supported overdentures are an alternative predictable rehabilitation method that has a high impact on improving the patient's quality of life. However, some biological complications may interfere with the maintenance and survival of these overdenture implants. The goal of this article was to assess the factors that affect peri-implant success, through a hypothetical prediction model for biological complications of implant overdentures. METHODS: A retrospective observational, prevalence study was conducted in 58 edentulous Caucasian patients rehabilitated with implant overdentures. A total of 229 implants were included in the study. Anamnestic, clinical, and implant-related parameters were collected and recorded in a single database. "Patient" was chosen as the unit of analysis, and a complete screening protocol was established. The data analytical study included assessing the odds ratio, concerning the presence or absence of a particular risk factor, by using binary logistic regression modeling. Probability values (p values) inferior to 0.05 were considered as representing statistically significant evidence. RESULTS: The performed prediction model included the following variables: mean probing depth, metal exposure, IL1B_allele2, maxillary edentulousness, and Fusobacterium nucleatum. The F. nucleatum showed significant association with the outcome. Introducing a negative coefficient appeared to prevent complications or even boost the biological defense when associated with other factors. CONCLUSIONS: The prediction model developed in this study could serve as a basis for further improved models that would assist clinicians in the daily diagnosis and treatment planning practice of oral rehabilitation with implant overdentures.