Is the personalized approach the key to improve clinical diagnosis of peri-implant conditions? The role of bone markers

Abstract

Background - Study objectives were 1) to estimate diagnostic capacity of clinical parameters, receptor activator of nuclear factor kappa-B (RANKL) and osteoprotegerin (OPG) to diagnose healthy peri-implant condition (HI), peri-implant mucositis (PIM) and peri-implantitis (PIMP) by assessing respective diagnostic accuracy, sensitivity, specificity and diagnostic ranges 2) to develop personalized diagnostic model (PDM) for implant monitoring. Methods - Split-mouth study included 126 patients and 252 implants (HI = 126, PIM = 57, and PIMP = 69). RANKL and OPG concentrations were estimated in peri-implant crevicular fluid using enzyme-linked immunosorbent assay method and assessed with clinical parameters using routine statistics, while the diagnostic capacity of individual parameters and overall clinical diagnosis were estimated using classifying algorithms. PDM was developed using decision trees. Results - Bleeding on probing (BOP), plaque index, and probing depth (PD) were confirmed reliable discriminants between peri-implant health and disease, while increase in PD (PD > 4 mm) and suppuration were good discriminants amongst PIM/PIMP. Bone turnover markers (BTMs) demonstrated presence of bone resorption in PIM; between comparable diagnostic ranges PIM/PIMP, PIMP was clinically distinguished from PIM in about 60% of patients while 40% remained diagnosed as false negatives. PDM demonstrated highest diagnostic capacity (accuracy 96.27%, sensitivity 95.00%, specificity 100%) and defined HI BOP ≤0.25%; PIM BOP >0.25%, PD ≤4.5 mm; PIMP BOP >0.25%, PD >4.5 mm and RANKL ≤19.9 pg/site; PIM BOP >0.25%, PD >4.5 mm, and RANKL >19.9 pg/site. Conclusions - BTMs demonstrated capacity to substantially improve clinical diagnosis of peri-implant conditions. Considering lack of difference in BTMs between PIM/PIMP and cluster of PIM with exceeding BTMs, a more refined definition of peri-implant conditions according to biological characteristics is required for further BTMs validation and appropriate PIMP management.

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.