Periodontal profile classes predict periodontal disease progression and tooth loss

Abstract

Background: Current periodontal disease taxonomies have limited utility for predicting disease progression and tooth loss; in fact, tooth loss itself can undermine precise person-level periodontal disease classifications. To overcome this limitation, the current group recently introduced a novel patient stratification system using latent class analyses of clinical parameters, including patterns of missing teeth. This investigation sought to determine the clinical utility of the Periodontal Profile Classes and Tooth Profile Classes (PPC/TPC) taxonomy for risk assessment, specifically for predicting periodontal disease progression and incident tooth loss.

Methods: The analytic sample comprised 4,682 adult participants of two prospective cohort studies (Dental Atherosclerosis Risk in Communities Study and Piedmont Dental Study) with information on periodontal disease progression and incident tooth loss. The PPC/TPC taxonomy includes seven distinct PPCs (person-level disease pattern and severity) and seven TPCs (tooth-level disease). Logistic regression modeling was used to estimate relative risks (RR) and 95% confidence intervals (CI) for the association of these latent classes with disease progression and incident tooth loss, adjusting for examination center, race, sex, age, diabetes, and smoking. To obtain personalized outcome propensities, risk estimates associated with each participant's PPC and TPC were combined into person-level composite risk scores (Index of Periodontal Risk [IPR]).

Results: Individuals in two PPCs (PPC-G: Severe Disease and PPC-D: Tooth Loss) had the highest tooth loss risk (RR = 3.6; 95% CI = 2.6 to 5.0 and RR = 3.8; 95% CI = 2.9 to 5.1, respectively). PPC-G also had the highest risk for periodontitis progression (RR = 5.7; 95% CI = 2.2 to 14.7). Personalized IPR scores were positively associated with both periodontitis progression and tooth loss.

Conclusions: These findings, upon additional validation, suggest that the periodontal/tooth profile classes and the derived personalized propensity scores provide clinical periodontal definitions that reflect disease patterns in the population and offer a useful system for patient stratification that is predictive for disease progression and tooth loss.

Keywords: Diagnosis; epidemiology; oral medicine; periodontal medicine; periodontitis; prognosis.

FIGURE 1

Computed probabilities presented as percentage for 10-year tooth loss (≥three teeth) using the DARIC dataset stratified by Periodontal Profile Classes (PPC) and Tooth Profile Classes (TPC). DARIC, Dental Atherosclerosis in Communities Study

FIGURE 2

Predicted probability for the Index of Periodontal Risk (IPR) score associated with tooth loss, attachment loss, and edentulism. A) Association of the IPR score and 10-year tooth loss (≥three teeth) in the DARIC dataset. B) Receiver Operator Curve (ROC) and C-statistic for 10-year tooth loss (≥three teeth) in the DARIC population. C) Association of the IPR score and 5-year tooth loss (≥three teeth) in the Piedmont Dental Study (PDS) dataset. D) ROC and C-statistic for 5-year tooth loss (≥three teeth) in the PDS dataset. E) Association of the IPR score and 3-year attachment loss in the PDS dataset. F) ROC and C-statistic for 3-year attachment loss in the PDS dataset. G) Association of the IPR score and edentulism in the PDS dataset. H) ROC and C-statistic for edentulism in the PDS dataset. DARIC, Dental Atherosclerosis in Communities Study