Derivation and Validation of the Periodontal and Tooth Profile Classification System for Patient Stratification

Abstract

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.

Keywords: Classification; diagnosis; epidemiology; gingivitis; periodontitis; prognosis.

Figure 1

Item response probabilities conditional on class membership for A. Tooth Status (presence or absence), B. Prosthetic Crowns (presence or absence), C. Interproximal Attachment Loss ≥3mm, D. Pocket Depth ≥4mm, E. Gingival Index (GI, dichotomized as ≥1 sites with GI≥1 vs none), F. Plaque Index (PI, dichotomized at ≥1 sites with Pl≥1), G. Bleeding on Probing (BOP, dichotomized at 50% or ≥3 sites per tooth). Probabilities are illustrated for each tooth type (1-32) representing both arches graphically in a heatmap for each clinical parameter in the DARIC sample. The upper and lower arch are represented for each Periodontal Profile Class (PPC) for each tooth, with green indicating high probability of tooth presence, crown absence, and healthy clinical signs shifting to yellow and red indicating more disease-associated signs.

Figure 2

Distribution of the seven Tooth Profile Classes (TPC) for each of the seven Periodontal Profile Classes (PPC). TPC-A (Health), TPC-B (Recession), TPC-C (Crown), TPC-D (GI), TPC-E (Interproximal Disease), TPC-F (Reduced Periodontium), and TPC-G (Severe Disease).