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

• Autores: Thiago Morelli, Kevin L. Moss, James Beck, John S. Preisser, Di Wu, Kimon Divaris, Steven Offenbacher
• Localización: Journal of periodontology, ISSN 0022-3492, Vol. 88, Nº. 2, 2017, págs. 153-165
• Idioma: inglés
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• Resumen

  • 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.

    Precise stratification is an important and highly desirable goal, from both clinical and public health standpoints. In the oral health domain, accurate stratification has the promise of optimizing diagnoses, treatment decisions, and overall care. For example, estimating tooth loss propensity at the individual and tooth levels can be highly informative for planning personalized, risk-based care.

    Clustering methods based on principal component analyses have been widely used to identify microbial community structures and a combination of clinical signs that describe characteristics of the population.1-3 However, most traditional clustering techniques neither categorize individuals to enable person-specific predictions, nor are they sensitive to change in status with time. Most existing models use person-level summary variables of clinical parameters, such as mean or extent scores for various signs of disease including plaque scores, gingival indices, probing depths (PDs), and clinical attachment levels (CALs), that reflect person-level disease and are not always linked to tooth type or tooth loss patterns. Other classifications are minimalist in nature, seeking the fewest number of sites or probing measures to place individuals into mutually exclusive categories of disease status.4,5 Latent class analysis (LCA) is a statistical method used to identify a set of discrete, mutually exclusive latent classes of individuals based on their responses to a set of observed categorical variables.6 It is a data-driven, person-centered approach that considers heterogeneity among individuals that can be grouped into relatively homogeneous subclasses with similar clinical patterns or trait endorsements.7,8 LCA can also be used to explore association between a set of observed categorical variables through assumed, unobserved latent classes. Researchers in numerous areas have been increasingly using LCA to discover hidden (latent) classes of individuals, including the behavioral sciences,9,10 autism,11 HIV infection,12 and asthma.13 To the best knowledge of the current authors, LCA has not been used before to derive periodontal or tooth profile classes.

    In this study, analytic procedures were implemented to identify discrete classes of individuals that are discriminated by tooth-level clinical parameters. Tooth-level LCA was also applied to discriminate different classes of teeth using tooth-/site-level clinical parameters. Finally, the resulting estimates were applied as model parameters to systematically examine other large, randomly sampled populations to ascertain whether tooth-based clinical parameters could effectively segregate different clinical periodontal classes, even in the presence of incomplete data. This study reports the derivation and validation of the LCA classes. The clinical application of this new stratification system will be presented in future reports.