Resolution of peri-implant mucositis following standard treatment: A prospective split-mouth study.
algorithm
artificial intelligence
biomarkers
peri-implant mucositis
peri-implantitis
treatment
Journal
Journal of periodontology
ISSN: 1943-3670
Titre abrégé: J Periodontol
Pays: United States
ID NLM: 8000345
Informations de publication
Date de publication:
02 Dec 2023
02 Dec 2023
Historique:
revised:
25
10
2023
received:
25
08
2023
accepted:
27
10
2023
medline:
2
12
2023
pubmed:
2
12
2023
entrez:
2
12
2023
Statut:
aheadofprint
Résumé
Peri-implant mucositis (PIM) is a pathological precursor of peri-implantitis, but its pattern of conversion to peri-implantitis is unclear and complicated to diagnose clinically, while none of the available protocols yield complete disease resolution. The aim of this study was the evaluation of PIM responsiveness to standard anti-infective mechanical treatment (AIMT) at clinical and biomarker levels, and estimation of the diagnostic capacity of bone markers as surrogate endpoints and predictors. Systemically healthy outpatients presenting one implant exhibiting clinical signs of inflammation confined within the soft tissue (PIM) and one healthy control (HC) implant at a non-adjacent position were included. Clinical parameters and peri-implant crevicular fluid samples were collected baseline and 6 months following mechanical therapy, to assess the levels of RANKL, OPG, and IGFBP2. PIM clustering was performed using machine learning algorithms. Overall, 38 patients met the inclusion criteria. Therapy resulted in the reduction of all clinical and biological indicators, but respective values remained significantly higher compared to HC. Clinical examination noted 30% disease resolution at the 6-month follow-up, while 43% showed no active bone resorption. OPG showed positive prognostic value for treatment outcome, while the clustering based on active bone resorption did not differ in terms of therapeutic effectiveness. AIMT is effective in reducing the clinical and biological indicators of PIM, but complete clinical resolution was achieved in only 30% of the cases. Around one third of PIM patients exhibited active bone resorption bellow clinical detectability that was not associated with disease progression and poor treatment responsiveness.
Sections du résumé
BACKGROUND
BACKGROUND
Peri-implant mucositis (PIM) is a pathological precursor of peri-implantitis, but its pattern of conversion to peri-implantitis is unclear and complicated to diagnose clinically, while none of the available protocols yield complete disease resolution. The aim of this study was the evaluation of PIM responsiveness to standard anti-infective mechanical treatment (AIMT) at clinical and biomarker levels, and estimation of the diagnostic capacity of bone markers as surrogate endpoints and predictors.
METHODS
METHODS
Systemically healthy outpatients presenting one implant exhibiting clinical signs of inflammation confined within the soft tissue (PIM) and one healthy control (HC) implant at a non-adjacent position were included. Clinical parameters and peri-implant crevicular fluid samples were collected baseline and 6 months following mechanical therapy, to assess the levels of RANKL, OPG, and IGFBP2. PIM clustering was performed using machine learning algorithms.
RESULTS
RESULTS
Overall, 38 patients met the inclusion criteria. Therapy resulted in the reduction of all clinical and biological indicators, but respective values remained significantly higher compared to HC. Clinical examination noted 30% disease resolution at the 6-month follow-up, while 43% showed no active bone resorption. OPG showed positive prognostic value for treatment outcome, while the clustering based on active bone resorption did not differ in terms of therapeutic effectiveness.
CONCLUSION
CONCLUSIONS
AIMT is effective in reducing the clinical and biological indicators of PIM, but complete clinical resolution was achieved in only 30% of the cases. Around one third of PIM patients exhibited active bone resorption bellow clinical detectability that was not associated with disease progression and poor treatment responsiveness.
Identifiants
pubmed: 38041803
doi: 10.1002/JPER.23-0507
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2023 The Authors. Journal of Periodontology published by Wiley Periodicals LLC on behalf of American Academy of Periodontology.
Références
Rakic M, Galindo-Moreno P, Monje A, et al. How frequent does peri-implantitis occur? A systematic review and meta-analysis. Clin Oral Investig. 2018;22(4):1805-1816.
Kordbacheh Changi K, Finkelstein J, Papapanou PN. Peri-implantitis prevalence, incidence rate, and risk factors: a study of electronic health records at a U.S. dental school. Clin Oral Implants Res. 2019;30(4):306-314.
Romandini M, Lima C, Pedrinaci I, Araoz A, Soldini MC, Sanz M. Prevalence and risk/protective indicators of peri-implant diseases: a university-representative cross-sectional study. Clin Oral Implants Res. 2021;32(1):112-122.
Esposito M, Grusovin MG, Worthington HV. Treatment of peri-implantitis: what interventions are effective? A Cochrane systematic review. Eur J Oral Implantol. 2012;5(l):S21-41.
Heitz-Mayfield LJA, Salvi GE, Mombelli A, et al. Supportive peri-implant therapy following anti-infective surgical peri-implantitis treatment: 5-year survival and success. Clin Oral Implants Res. 2018;29(1):1-6.
Koldsland OC, Wohlfahrt JC, Aass AM. Surgical treatment of peri-implantitis: prognostic indicators of short-term results. J Clin Periodontol. 2018;45(1):100-113.
Schwarz F, Derks J, Monje A, Wang HL. Peri-implantitis. J Clin Periodontol. 2018;45(Suppl 20):S246-266.
Monje A, Caballé-Serrano J, Nart J, Peñarrocha D, Wang HL, Rakic M. Diagnostic accuracy of clinical parameters to monitor peri-implant conditions: a matched case-control study. J Periodontol. 2018;89(4):407-417.
Brägger U. Use of radiographs in evaluating success, stability and failure in implant dentistry. Periodontol 2000. 1998;17:77-88.
Lang NP, Wetzel AC, Stich H, Caffesse RG. Histologic probe penetration in healthy and inflamed peri-implant tissues. Clin Oral Implants Res. 1994;5(4):191-201.
Tonetti MS, Greenwell H, Kornman KS. Staging and grading of periodontitis: framework and proposal of a new classification and case definition. J Periodontol. 2018;89(Suppl 1):S159-172.
Heitz-Mayfield LJA, Salvi GE. Peri-implant mucositis. J Clin Periodontol. 2018;45(S20):S237-245.
Berglundh T, Armitage G, Araujo MG, et al. Peri-implant diseases and conditions: consensus report of workgroup 4 of the 2017 World Workshop on the Classification of Periodontal and Peri-Implant Diseases and Conditions. J Clin Periodontol. 2018;45(Suppl 20):S286-291.
Fretwurst T, Nelson K, Tarnow DP, Wang HL, Giannobile WV. Is metal particle release associated with peri-implant bone destruction? An emerging concept. J Dent Res. 2018;97(3):259-265.
Blasi A, Iorio-Siciliano V, Pacenza C, Pomingi F, Matarasso S, Rasperini G. Biofilm removal from implants supported restoration using different instruments: a 6-month comparative multicenter clinical study. Clin Oral Implants Res. 2016;27(2):e68-73.
Máximo MB, de Mendonça AC, Renata Santos V, Figueiredo LC, Feres M, Duarte PM. Short-term clinical and microbiological evaluations of peri-implant diseases before and after mechanical anti-infective therapies. Clin Oral Implants Res. 2009;20(1):99-108.
Schwarz F, Becker K, Sager M. Efficacy of professionally administered plaque removal with or without adjunctive measures for the treatment of peri-implant mucositis. A systematic review and meta-analysis. J Clin Periodontol. 2015;42:S202-213.
Gennai S, Bollain J, Ambrosio N, Marruganti C, Graziani F, Figuero E. Efficacy of adjunctive measures in peri-implant mucositis. A systematic review and meta-analysis. J Clin Periodontol. 2023;50(Suppl 26):161-187.
Iorio-Siciliano V, Blasi A, Isola G, Sculean A, Salvi GE, Ramaglia L. Resolution of peri-implant mucositis at tissue- and bone-level implants: a 6-month prospective controlled clinical trial. Clin Oral Implants Res. 2023;34(5):450-462.
Iorio-Siciliano V, Blasi A, Stratul SI, et al. Anti-infective therapy of peri-implant mucositis with adjunctive delivery of a sodium hypochlorite gel: a 6-month randomized triple-blind controlled clinical trial. Clin Oral Investig. 2020;24(6):1971-1979.
Rakic M, Struillou X, Petkovic-Curcin A, et al. Estimation of bone loss biomarkers as a diagnostic tool for peri-implantitis. J Periodontol. 2014;85(11):1566-1574.
Rakic M, Monje A, Radovanovic S, Petkovic-Curcin A, Vojvodic D, Tatic Z. Is the personalized approach the key to improve clinical diagnosis of peri-implant conditions? The role of bone markers. J Periodontol. 2020;91(7):859-869.
Belibasakis GN, Bostanci N. The RANKL-OPG system in clinical periodontology. J Clin Periodontol. 2012;39(3):239-248.
Rakic M, Lekovic V, Nikolic-Jakoba N, Vojvodic D, Petkovic-Curcin A, Sanz M. Bone loss biomarkers associated with peri-implantitis. A cross-sectional study. Clin Oral Implants Res. 2013;24(10):1110-1116.
Chaparro A, Beltrán V, Betancur D, et al. Molecular biomarkers in peri-implant health and disease: a cross-sectional pilot study. Int J Mol Sci. 2022;23(17):9802.
Amin S, Riggs BL, Melton LJ, Achenbach SJ, Atkinson EJ, Khosla S. High serum IGFBP-2 is predictive of increased bone turnover in aging men and women. J Bone Miner Res. 2007;22(6):799-807.
DeMambro VE, Maile L, Wai C, et al. Insulin-like growth factor-binding protein-2 is required for osteoclast differentiation. J Bone Miner Res. 2012;27(2):390-400.
Koliaraki V, Prados A, Armaka M, Kollias G. The mesenchymal context in inflammation, immunity and cancer. Nat Immunol. 2020;21(9):974-982.
Moon IS, Berglundh T, Abrahamsson I, Linder E, Lindhe J. The barrier between the keratinized mucosa and the dental implant. J Clin Periodontol. 1999;26(10):658-663.
Ramanauskaite A, Fretwurst T, Schwarz F. Efficacy of alternative or adjunctive measures to conventional non-surgical and surgical treatment of peri-implant mucositis and peri-implantitis: a systematic review and meta-analysis. Int J Implant Dent. 2021;7(1):112.
World Medical Association. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA. 2013;310(20):2191-2194.
Sanz M, Chapple IL, on behalf of Working Group 4 of the VIII European Workshop on Periodontology*. Clinical research on peri-implant diseases: consensus report of Working Group 4. J Clin Periodontol. 2012;39:202-206.
Van Aken J. Optimum conditions for intraoral roentgenograms. Oral Surg Oral Med Oral Pathol. 1969;27(4):475-491.
Nakashima K, Demeurisse C, Cimasoni G. The recovery efficiency of various materials for sampling enzymes and polymorphonuclear leukocytes from gingival crevices. J Clin Periodontol. 1994;21(7):479-483.
Fleming TR, DeMets DL. Surrogate end points in clinical trials: are we being misled? Ann Intern Med. 1996;125(7):605-613.
Fleming TR, Powers JH. Biomarkers and surrogate endpoints in clinical trials. Stat Med. 2012;31(25):2973-2984.
Djuran B, Tatic Z, Perunovic N, et al. Underdiagnosis in background of emerging public health challenges related to peri-implant diseases: an interventional split-mouth study. Int J Environ Res Public Health. 2022;20(1):477.
Quinlan JR. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc.; 1993.
Quinlan JR. C4.5: Programs for Machine Learning. Elsevier; 2014. 313 p.
Bostanci N, İlgenli T, Emingil G, et al. Gingival crevicular fluid levels of RANKL and OPG in periodontal diseases: implications of their relative ratio. J Clin Periodontol. 2007;34(5):370-376.
Liu YCG, Lerner UH, Teng YTA. Cytokine responses against periodontal infection: protective and destructive roles. Periodontol 2000. 2010;52(1):163-206.
Shah P, Kendall F, Khozin S, et al. Artificial intelligence and machine learning in clinical development: a translational perspective. NPJ Digital Med. 2019;2(1):1-5.
Weis C, Cuénod A, Rieck B, et al. Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning. Nat Med. 2022;28(1):164-174.
Canullo L, Tallarico M, Radovanovic S, Delibasic B, Covani U, Rakic M. Distinguishing predictive profiles for patient-based risk assessment and diagnostics of plaque induced, surgically and prosthetically triggered peri-implantitis. Clin Oral Implants Res. 2016;27(10):1243-1250.
Giannobile WV, Lang NP, Tonetti MS. Osteology Guidelines for Oral and Maxillofacial Regeneration: Clinical Research. 1st ed. Quintessence Publishing Co., Ltd; 2014:318.