|Title||Derivation and Validation of the Periodontal and Tooth Profile Classification System for Patient Stratification.|
|Publication Type||Journal Article|
|Year of Publication||2017|
|Authors||Morelli T, Moss KL, Beck J, Preisser JS, Wu D, Divaris K|
|Secondary Authors||Offenbacher S|
|Date Published||2017 02|
|Keywords||Aged, Demography, Female, Humans, Male, Middle Aged, Nutrition Surveys, Periodontal Diseases, Phenotype, Severity of Illness Index, Tooth Loss, United States|
BACKGROUND: The goal of this study is to use bioinformatics tools to explore identification and definition of distinct periodontal and tooth profile classes (PPCs/TPCs) among a cohort of individuals by using detailed clinical measures at the tooth level, including both periodontal measurements and tooth loss.
METHODS: Full-mouth clinical periodontal measurements (seven clinical parameters) from 6,793 individuals from the Dental Atherosclerosis Risk in Communities Study (DARIC) were used to identify PPC. A custom latent class analysis (LCA) procedure was developed to identify clinically distinct PPCs and TPCs. Three validation cohorts were used: NHANES (2009 to 2010 and 2011 to 2012) and the Piedmont Study population (7,785 individuals).
RESULTS: The LCA method identified seven distinct periodontal profile classes (PPCs A to G) and seven distinct tooth profile classes (TPCs A to G) ranging from health to severe periodontal disease status. The method enabled identification of classes with common clinical manifestations that are hidden under the current periodontal classification schemas. Class assignment was robust with small misclassification error in the presence of missing data. The PPC algorithm was applied and confirmed in three distinct cohorts.
CONCLUSIONS: The findings suggest PPC and TPC using LCA can provide robust periodontal clinical definitions that reflect disease patterns in the population at an individual and tooth level. These classifications can potentially be used for patient stratification and thus provide tools for integrating multiple datasets to assess risk for periodontitis progression and tooth loss in dental patients.
|Alternate Journal||J Periodontol|
|PubMed Central ID||PMC5288277|
|Grant List||K23 DE025093 / DE / NIDCR NIH HHS / United States |
UL1 TR001111 / TR / NCATS NIH HHS / United States
T90 DE021986 / DE / NIDCR NIH HHS / United States
R01 DE023836 / DE / NIDCR NIH HHS / United States
R90 DE022527 / DE / NIDCR NIH HHS / United States
R01 DE021418 / DE / NIDCR NIH HHS / United States