Survival rate and potential risk indicators of implant loss in non‐smokers and systemically healthy periodontitis patients: An up to 9‐year retrospective study

Abstract

Background An increasing number of patients with chronic periodontitis (CP) have received implant restoration. However, very few studies have evaluated the probable risk indicators of implant loss in patients with CP. Objective The aim of this study is to evaluate implant long‐term survival rates in patients with CP. The results are analyzed to discern potential risk indicators of implant loss. Methods A total of 1549 implants were inserted in 827 non‐smokers and systemically healthy CP patients between March 2011 and March 2019. Clinical variables (age; sex; implant location; implant diameter; implant length; implant type; bone quality; bone graft, periodontal disease status, and insertion torque) were recorded. Kaplan‐Meier survival curves illustrated the cumulative survival rate. The relationship between variables and implant loss was discerned by univariate analysis. Further multivariate Cox proportional hazard regression analysis was carried out for the variables with P < 0.2. Results The cumulative survival rates were 98.8% after 3 months, 97.9% after 6 months, 97.7% after 1 year, and 97.4% after 2 to 9 years. After adjusting possible confounders, the multivariable Cox regression model revealed statistically significant influences of implant location, history of bone graft, and insertion torque on implant loss. Implants with history of bone graft were more likely to loss. Implants inserted in the anterior area had a higher implant loss risk; insertion torque of <15 Newton‐centimeter (Ncm) showed a relatively high risk of being lost. Conclusions The study represented public hospital insight into long‐term implant results of patients with CP. Under the premise of strict periodontal control, patients with the history of CP exhibited relatively high implant survival rate. Anterior implant location, history of bone graft, and insertion torque <15 Ncm are associated with a lower implant survival rate and could be considered at a higher risk of implant failure in patients with CP.

Clinical outcomes and risk factor analysis of dental implants inserted with lateral maxillary sinus floor augmentation: A 3‐ to 8‐year retrospective study

“…These findings are in accordance with numerous studies demonstrating the key roles of periodontitis (Adler et al, 2020;Theodoridis et al, 2017), together with the still limited evidence supporting the negative effects of other patient-, implant-and surgery-related factors on implant survival (Atarchi et al, 2020;Grisar et al, 2017;Kim et al, 2020;Starch-Jensen et al, 2018;Wang et al, 2021).…”

“…When adjusted for the co‐variations in the multivariable cure model, the predictor of a history of periodontitis without regular SPT was additionally confirmed, whereas the impact of gender, smoking habit and RBH weakened. These findings are in accordance with numerous studies demonstrating the key roles of periodontitis (Adler et al, 2020; Theodoridis et al, 2017), together with the still limited evidence supporting the negative effects of other patient‐, implant‐ and surgery‐related factors on implant survival (Atarchi et al, 2020; Grisar et al, 2017; Kim et al, 2020; Starch‐Jensen et al, 2018; Wang et al, 2021).…”

Dental implant failure and bone augmentation: A retrospective study

“…A total of 553 records of dental implants were included in the analysis and the incidence of implant failure in the present retrospective investigation was 3.1% which is similar to other studies’ reported findings ( 17 - 19 ). In addition, similar to other studies, tobacco users exhibited an 8.69 higher risk of implant failure than non-users ( p <0.001).…”

Identification of the Transcription Co-Factor–Related Gene Signature and Risk Score Model for Osteosarcoma

“…TcoF-related genes associated with prognosis were identified by the univariate Cox regression with a p value <0.2 to prevent omissions based on the TARGET database ( Gokcal et al, 2021 ; Wang et al, 2021 ). After that, in order to screen candidate genes with an optimal number for this risk score model, the least absolute shrinkage and selection operator (LASSO) Cox regression with the “glmnet” R package was utilized for variable filters, and 10-fold cross-validation analysis was used for validation analysis.…”