Distinguishing predictive profiles for patient‐based risk assessment and diagnostics of plaque induced, surgically and prosthetically triggered peri‐implantitis. Issue 10 (20th November 2015)

• Record Type: Journal Article
• Authors: Canullo, Luigi; Tallarico, Marco; Radovanovic, Sandro; Delibasic, Boris; Covani, Ugo; Rakic, Mia
• Is Part Of: Clinical oral implants research. Volume 27:Issue 10(2016)
• Page Start: 1243
• Page End: 1250
• Publication Date: 2015-11-20
• Subjects: data mining -- peri‐implantitis -- predictive profile -- predictors -- risk factor assessment; Dental implants -- Research -- Periodicals; 617.69
• DOI: 10.1111/clr.12738
• Languages: English
• ISSNs: 0905-7161

Abstract

Abstract: Objective: To investigate whether specific predictive profiles for patient‐based risk assessment/diagnostics can be applied in different subtypes of peri‐implantitis. Materials and methods: This study included patients with at least two implants (one or more presenting signs of peri‐implantitis). Anamnestic, clinical, and implant‐related parameters were collected and scored into a single database. Dental implant was chosen as the unit of analysis, and a complete screening protocol was established. The implants affected by peri‐implantitis were then clustered into three subtypes in relation to the identified triggering factor: purely plaque‐induced or prosthetically or surgically triggered peri‐implantitis. Statistical analyses were performed to compare the characteristics and risk factors between peri‐implantitis and healthy implants, as well as to compare clinical parameters and distribution of risk factors between plaque, prosthetically and surgically triggered peri‐implantitis. The predictive profiles for subtypes of peri‐implantitis were estimated using data mining tools including regression methods and C4.5 decision trees. Results: A total of 926 patients previously treated with 2812 dental implants were screened for eligibility. Fifty‐six patients (6.04%) with 332 implants (4.44%) met the study criteria. Data from 125 peri‐implantitis and 207 healthy implants were therefore analyzed and included in the statistical analysis. Within peri‐implantitis group, 51 were classified as surgically triggered (40.8%), 38 as prosthetically triggered (30.4%), and 36 as plaque‐induced (28.8%) peri‐implantitis. For peri‐implantitis, 51 were associated with surgical risk factor (40.8%), 38 with prosthetic risk factor (30.4%), 36 with purely plaque‐induced risk factor (28.8%). The variables identified as predictors of peri‐implantitis were female sex (OR = 1.60), malpositioning (OR = 48.2), overloading (OR = 18.70), and bone reconstruction (OR = 2.35). The predictive model showed 82.35% of accuracy and identified distinguishing predictive profiles for plaque, prosthetically and surgically triggered peri‐implantitis. The model was in accordance with the results of risk analysis being the external validation for model accuracy. Conclusions: It can be concluded that plaque induced and prosthetically and surgically triggered peri‐implantitis are different entities associated with distinguishing predictive profiles; hence, the appropriate causal treatment approach remains necessary. The advanced data mining model developed in this study seems to be a promising tool for diagnostics of peri‐implantitis subtypes.