Novel and Accurate Predictive Model of Peri-implantitis for Implant Prosthesis in Managing Patients With Chronic Periodontitis

Abstract

The aim of this study was to identify the influencing factors of peri-implantitis for implant prosthesis and to establish a predictive model of occurrence in managing patients with chronic periodontitis. 430 patients (a total of 686 implants) with 5 years follow-up were included in the study. The demographic and clinical characteristics independent variables were including patient related factors (age, gender, duration of symptoms, smoking, diabetes, severity of periodontitis, bone mineral density, oral hygiene) and implants related factors (position, implant diameter, implant length, additional operations, probing depth, gingival index, plaque index, load-bearing time). Univariate analysis identified the influencing factors, the statistically significant factors were further analyzed by multivariate logistic regression, and the predictive model was established. After 5 years of follow-up, peri-implantitis occurred in 95 of 686 implants (13.8%). Multiple linear regression indicated that smoking (p = 0.011), diabetes (p < 0.001), severe periodontitis (p = 0.023), lager implant diameter (p < 0.001) and poor oral hygiene (p < 0.001) increased the odds of occurrence of peri-implantitis. There were no significant differences in other factors. (p > 0.05) Logistic regression drew the equation: occurrence of peri-implantitis = [1 + e (16.172+5.395*smoking+4.807*diabetes +8.109*severe periodontitis+5.569*lager implant diameter+11.138*poor oral hygiene)]−1. The sensitivity of predictive factors was 91.77%, 88.43%, 82.64%, 85.21% and 92.02%, respectively. The specificity 45.65%, 49.17%, 66.23%, 42.39% and 65.41%, respectively. The AUC was 0.721, 0.620, 0.453, 0.651 and 0.826, respectively. Hosmer-Lemeshow test showed a good fitting of the predictive model with an overall accuracy of 87.2%. Smoking, diabetes, severe periodontitis, lager implant diameter and poor oral hygiene are important risk factors of peri-implantitis. We also established a novel and accurate predictive model for the occurrence of peri-implantitis in managing patients with chronic periodontitis. The use of these parameters, in the form of a predictive model, has the potential to improve decision-making in the application of the treatment plan.