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 . .

TY  - GEN
AU  - Canullo, Luigi
AU  - Radovanović, Sandro
AU  - Delibasic, Boris
AU  - Blaya, Juan Antonio
AU  - Penarrocha, David
AU  - Rakić, Mia
PY  - 2016
UR  - http://www.scopus.com/inward/record.url?eid=2-s2.0-84963502806&partnerID=tZOtx3y1
UR  - https://radar.ibiss.bg.ac.rs/handle/123456789/2526
AB  - AIM: The primary aim of this study was to evaluate 23 pathogens associated with peri-implantitis at inner part of implant connections, in peri-implant and periodontal pockets between patients suffering peri-implantitis and participants with healthy peri-implant tissues; the secondary aim was to estimate the predictive value of microbiological profile in patients wearing dental implants using data mining methods. MATERIAL AND METHODS: Fifty participants included in the present case─control study were scheduled for collection of plaque samples from the peri-implant pockets, internal connection, and periodontal pocket. Real-time polymerase chain reaction was performed to quantify 23 pathogens. Three predictive models were developed using C4.5 decision trees to estimate the predictive value of microbiological profile between three experimental sites. RESULTS: The final sample included 47 patients (22 healthy controls and 25 diseased cases), 90 implants (43 with healthy peri-implant tissues and 47 affected by peri-implantitis). Total and mean pathogen counts at inner portions of the implant connection, in peri-implant and periodontal pockets were generally increased in peri-implantitis patients when compared to healthy controls. The inner portion of the implant connection, the periodontal pocket and peri-implant pocket, respectively, presented a predictive value of microbiologic profile of 82.78%, 94.31%, and 97.5% of accuracy. CONCLUSION: This study showed that microbiological profile at all three experimental sites is differently characterized between patients suffering peri-implantitis and healthy controls. Data mining analysis identified Parvimonas micra as a highly accurate predictor of peri-implantitis when present in peri-implant pocket while this method generally seems to be promising for diagnosis of such complex infections.
PB  - Blackwell Munksgaard
T2  - Clinical oral implants research
T1  - The predictive value of microbiological findings on teeth, internal and external implant portions in clinical decision making.
DO  - 10.1111/clr.12828
ER  - 

@misc{
author = "Canullo, Luigi and Radovanović, Sandro and Delibasic, Boris and Blaya, Juan Antonio and Penarrocha, David and Rakić, Mia",
year = "2016",
abstract = "AIM: The primary aim of this study was to evaluate 23 pathogens associated with peri-implantitis at inner part of implant connections, in peri-implant and periodontal pockets between patients suffering peri-implantitis and participants with healthy peri-implant tissues; the secondary aim was to estimate the predictive value of microbiological profile in patients wearing dental implants using data mining methods. MATERIAL AND METHODS: Fifty participants included in the present case─control study were scheduled for collection of plaque samples from the peri-implant pockets, internal connection, and periodontal pocket. Real-time polymerase chain reaction was performed to quantify 23 pathogens. Three predictive models were developed using C4.5 decision trees to estimate the predictive value of microbiological profile between three experimental sites. RESULTS: The final sample included 47 patients (22 healthy controls and 25 diseased cases), 90 implants (43 with healthy peri-implant tissues and 47 affected by peri-implantitis). Total and mean pathogen counts at inner portions of the implant connection, in peri-implant and periodontal pockets were generally increased in peri-implantitis patients when compared to healthy controls. The inner portion of the implant connection, the periodontal pocket and peri-implant pocket, respectively, presented a predictive value of microbiologic profile of 82.78%, 94.31%, and 97.5% of accuracy. CONCLUSION: This study showed that microbiological profile at all three experimental sites is differently characterized between patients suffering peri-implantitis and healthy controls. Data mining analysis identified Parvimonas micra as a highly accurate predictor of peri-implantitis when present in peri-implant pocket while this method generally seems to be promising for diagnosis of such complex infections.",
publisher = "Blackwell Munksgaard",
journal = "Clinical oral implants research",
title = "The predictive value of microbiological findings on teeth, internal and external implant portions in clinical decision making.",
doi = "10.1111/clr.12828"
}

Canullo, L., Radovanović, S., Delibasic, B., Blaya, J. A., Penarrocha, D.,& Rakić, M.. (2016). The predictive value of microbiological findings on teeth, internal and external implant portions in clinical decision making.. in Clinical oral implants research
Blackwell Munksgaard..
https://doi.org/10.1111/clr.12828

Canullo L, Radovanović S, Delibasic B, Blaya JA, Penarrocha D, Rakić M. The predictive value of microbiological findings on teeth, internal and external implant portions in clinical decision making.. in Clinical oral implants research. 2016;.
doi:10.1111/clr.12828 .

Canullo, Luigi, Radovanović, Sandro, Delibasic, Boris, Blaya, Juan Antonio, Penarrocha, David, Rakić, Mia, "The predictive value of microbiological findings on teeth, internal and external implant portions in clinical decision making." in Clinical oral implants research (2016),
https://doi.org/10.1111/clr.12828 . .