Human platelet-derived growth factor-BB (rhPDGF-BB) with collagen matrix for sinus elevation without using a bone graft.

BACKGROUND: This case report involves a 38-year-old male who presented to the clinic after experiencing complications from a tooth extraction, including a dislodged root segment in the sinus, a sinus wall fenestration on the palate, a residual bone height (RBH) of 3 mm, and inadequate healing of the soft tissue. He presented for implant placement. METHODS: Recombinant human platelet-derived growth factor-BB (rh-PDGF-BB) was applied to a wound dressing material and placed in the sinus cavity alongside a 4.8 × 10 mm dental implant (Straumann SP, Straumann, Andover, MA, USA.). As documented in the literature, a graftless sinus lift via a lateral window was performed using a split-thickness flap technique to elevate the sinus membrane, re-establish its integrity, and restore its barrier function. RESULTS: An 8-month cone beam computed tomography assessment showed a 6.2 mm vertical bone gain and complete implant coverage. CONCLUSION: This approach provided a successful alternative to shorten treatment duration and achieve favorable radiographic outcomes during early healing. KEY POINTS: The use of rhPDGF-BB and a collagen matrix in a sinus lift procedure emerges as a practical therapeutic option when grafting might lack predictability and notably consume more treatment time, while also achieving the desired bone height when used with a simultaneously placed implant.

Clustering by periodontitis-associated factors: A novel application to NHANES data.

BACKGROUND: Unsupervised clustering is a method used to identify heterogeneity among groups and homogeneity within a group of patients. Without a prespecified outcome entry, the resulting model deciphers patterns that may not be disclosed using traditional methods. This is the first time such clustering analysis is applied in identifying unique subgroups at high risk for periodontitis in National Health and Nutrition Examination Surveys (NHANES 2009 to 2014 data sets using >500 variables. METHODS: Questionnaire, examination, and laboratory data (33 tables) for >1,000 variables were merged from 14,072 respondents who underwent clinical periodontal examination. Participants with ≥6 teeth and available data for all selected categories were included (N = 1,222). Data wrangling produced 519 variables. k-means/modes clustering (k = 2:14) was deployed. The optimal k-value was determined through the elbow method, formula = ∑ (xi2 ) - ((∑ xi )2 /n). The 5-cluster model showing the highest variability (63.08%) was selected. The 2012 Centers for Disease Control and Prevention/American Academy of Periodontology (AAP) and 2018 European Federation of Periodontology/AAP periodontitis case definitions were applied. RESULTS: Cluster 1 (n = 249) showed the highest prevalence of severe periodontitis (43%); 39% self-reported "fair" general health; 55% had household income <$35,000/year; and 48% were current smokers. Cluster 2 (n = 154) had one participant with periodontitis. Cluster 3 (n = 242) represented the greatest prevalence of moderate periodontitis (53%). In Cluster 4 (n = 35) only one participant had no periodontitis. Cluster 5 (n = 542) was the systemically healthiest with 77% having no/mild periodontitis. CONCLUSION: Clustering of NHANES demographic, systemic health, and socioeconomic data effectively identifies characteristics that are statistically significantly related to periodontitis status and hence detects subpopulations at high risk for periodontitis without costly clinical examinations.