Multilevel analysis of bacterial counts from chronic periodontitis after root planing/scaling, surgery, and systemic and local antibiotics: 2-year results

Abstract

AimTo follow changes (over 2 years) in subgingival bacterial counts of five microbial complexes including health-related Actinomyces spp. in deeper pockets (≥5 mm) after periodontal treatments.MethodsEight different treatments were studied: (1) scaling+root planing (SRP); (2) periodontal surgery (SURG)+systemic amoxicillin (AMOX)+systemic metronidazole (MET); (3) SURG+locally delivered tetracycline (TET); (4) SURG; (5) AMOX+MET+TET; (6) AMOX+MET; (7) TET; and (8) SURG+AMOX+MET+TET. Antibiotics were given immediately following SRP. Subgingival plaque was collected mesiobuccally from each tooth, except third molars, from 176 subjects, completing the study, at baseline, 3, 6, 12, 18, and 24 months post-treatment and analysed for 40 different bacteria using checkerboard hybridization. A negative binomial (NB) generalized estimating equation (NB GEE) model was used to analyze count data and a logistic GEE was used for proportions.ResultsWe observed short-term beneficial changes in the composition of the red complex of up to 3 months by treating subjects with AMOX+MET+TET. Similar short-term improvements with the same treatment were observed for Tannerella forsythia and Treponema denticola of the red complex. SURG had also short-term beneficial effect on Porphyromonas gingivalis. No periodontal treatments applied to severely affected sites promoted the growth of Actinomyces. Smoking elevated counts of both the red and orange complex while bleeding on probing (BOP) and gingival redness were also predictors of more red complex counts. Comparatively similar findings were obtained by analyzing counts and by analyzing proportions.ConclusionsAlthough short-term reductions in the counts of the red complex were observed in sites that were treated with AMOX+MET+TET, long-term significant effects were not observed with any of the eight treatments. Poor oral hygiene in patients with severe chronic periodontitis diminished the beneficial effects of treatment.

Alterations of the Subgingival Microbiota in Pediatric Crohnʼs Disease Studied Longitudinally in Discovery and Validation Cohorts

“…4-8 The epithelial layer in the subgingival crevice is the initial interface between peridontopathic organisms and the host. 9, 10 The presence of bacteria in the subgingival environment can lead to the development of a biofilm, leading to an innate immune response associated with production of cytokines and anti-microbial peptides, reminiscent of responses at the mucosal interface in the gut.…”

Microbiological outcomes from different periodontal maintenance interventions: a systematic review

“…It is stated that this is a short period to observe great changes in periodontal status over time, and that the major challenges for a breakdown or recurrence of the disease occur later in the maintenance phase. 2,8,37,41,42 Herein, microbiological findings in studies with more than 12 months showed in general stability along subsequent years. 23,24,29,34,37 The methodology for microbiological identification performed by most studies were qualitative or semi-quantitative analyses, when it is accepted that molecular identification and absolute quantification are the preferred techniques.…”

“…1,2,3,4,5 However, during the past 20 years the subgingival scaling and root planing (SBG) has been associated to that control and this association (SPG+SBG) been recognized as the choice therapy during periodontal maintenance period (PMP). 6,7,8 Nevertheless, studies comparing this combination to other protocols were not available until recently when a clinical study 9 and two systematic reviews 10,11 suggested that the SPG biofilm control alone might be the choice during PMP.…”

Preconditions and multilevel models in studying post-surgical adverse outcomes

“…To identify the optimal periodontal treatment as measured by changes in pocket depth and clinical attachment level, Mdala et al built a multilevel model with preconditions, such as smoking habits, and other predictors, such as bleeding and gingival redness (Mdala et al 2013). In a study that modeled the 30-day mortality after heart surgery, Sanagou et al examined multilevel models that included an array of prior conditions, such as preoperative dialysis, previous cardiac surgery, vascular disease, and others in a large effort that attempted to account for correlation between patients within hospitals (Sanagou et al 2012).…”

“…Specifically, after observing the importance of both preconditions and measures of entropy and change point analysis in Terner et al (2014b), we employ multilevel models to understand the interactions between these predictors. Previous works in health and medicine have used multilevel models for similar reasons: Mdala et al include preconditions in an effort to identify the periodontal treatment that best maintained clinical results over time (Mdala et al 2013); Bouwmeester reviews multilevel models as a prognostic prediction tool for patients undergoing anesthetized surgery (Bouwmeester 2012). We are interested in examining preconditions in concert with characteristics of the time series to develop a layered understanding of patient performance in these surgeries.…”