TY  - JOUR
AU  - Canullo, Luigi
AU  - Tallarico, Marco
AU  - Radovanovic, Sandro
AU  - Delibasic, Boris
AU  - Covani, Ugo
AU  - Rakić, Mia
PY  - 2016
UR  - http://doi.wiley.com/10.1111/clr.12738
UR  - https://radar.ibiss.bg.ac.rs/handle/123456789/2527
AB  - 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.
PB  - Blackwell Munksgaard
T2  - Clinical Oral Implants Research
T1  - Distinguishing predictive profiles for patient-based risk assessment and diagnostics of plaque induced, surgically and prosthetically triggered peri-implantitis
IS  - 10
VL  - 27
DO  - 10.1111/clr.12738
SP  - 1243
EP  - 1250
ER  - 

@article{
author = "Canullo, Luigi and Tallarico, Marco and Radovanovic, Sandro and Delibasic, Boris and Covani, Ugo and Rakić, Mia",
year = "2016",
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.",
publisher = "Blackwell Munksgaard",
journal = "Clinical Oral Implants Research",
title = "Distinguishing predictive profiles for patient-based risk assessment and diagnostics of plaque induced, surgically and prosthetically triggered peri-implantitis",
number = "10",
volume = "27",
doi = "10.1111/clr.12738",
pages = "1243-1250"
}

Canullo, L., Tallarico, M., Radovanovic, S., Delibasic, B., Covani, U.,& Rakić, M.. (2016). Distinguishing predictive profiles for patient-based risk assessment and diagnostics of plaque induced, surgically and prosthetically triggered peri-implantitis. in Clinical Oral Implants Research
Blackwell Munksgaard., 27(10), 1243-1250.
https://doi.org/10.1111/clr.12738

Canullo L, Tallarico M, Radovanovic S, Delibasic B, Covani U, Rakić M. Distinguishing predictive profiles for patient-based risk assessment and diagnostics of plaque induced, surgically and prosthetically triggered peri-implantitis. in Clinical Oral Implants Research. 2016;27(10):1243-1250.
doi:10.1111/clr.12738 .

Canullo, Luigi, Tallarico, Marco, Radovanovic, Sandro, Delibasic, Boris, Covani, Ugo, Rakić, Mia, "Distinguishing predictive profiles for patient-based risk assessment and diagnostics of plaque induced, surgically and prosthetically triggered peri-implantitis" in Clinical Oral Implants Research, 27, no. 10 (2016):1243-1250,
https://doi.org/10.1111/clr.12738 . .