Luigi Canullo1, Sandro Radovanović2, Boris Delibasic2, Juan Antonio Blaya3, David Penarrocha3, Mia Rakic4,5. 1. Private Practice, Rome, Italy. 2. Centre for Business Decision-making, Faculty of Organizational Sciences, University of Belgrade, Belgrade, Serbia. 3. Department of Oral Surgery, University of Valencia, Valencia, Spain. 4. Institute for Biological Research "Sinisa Stankovic", University of Belgrade, Belgrade, Serbia. 5. Centre for Osteoarticular and Dental Tissue Engineering, INSERM U791, Faculty of Dental Surgery, University of Nantes, Nantes, France.
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.
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-implantitispatients 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.
Authors: Philipp Sahrmann; Fabienne Gilli; Daniel B Wiedemeier; Thomas Attin; Patrick R Schmidlin; Lamprini Karygianni Journal: Microorganisms Date: 2020-05-01
Authors: João G S Souza; Raphael C Costa; Aline A Sampaio; Victória L Abdo; Bruna E Nagay; Nidia Castro; Belén Retamal-Valdes; Jamil A Shibli; Magda Feres; Valentim A R Barão; Martinna Bertolini Journal: iScience Date: 2022-03-01