Resolution of peri-implant mucositis following standard treatment: A prospective split-mouth study

Abstract

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.

Sandro Radovanović

Assistant Professor at University of Belgrade

My research interests include machine learning, development and design of decision support systems, decision theory, and fairness and justice concepts in algorithmic decision making.