Derivation and validation of the periodontal and tooth profile classification system for patient stratification

Thiago Morelli, Kevin L. Moss, James Beck, John S. Preisser, Di Wu, Kimon Divaris, Steven Offenbacher

Research output: Contribution to journalArticle

  • 7 Citations

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.

LanguageEnglish (US)
Pages153-165
Number of pages13
JournalJournal of periodontology
Volume88
Issue number2
DOIs
StatePublished - Feb 1 2017

Fingerprint

Tooth
Tooth Loss
Nutrition Surveys
Periodontitis
Periodontal Diseases
Computational Biology
Population
Mouth
Atherosclerosis
Health

Keywords

  • Classification
  • Diagnosis
  • Epidemiology
  • Gingivitis
  • Periodontitis
  • Prognosis

ASJC Scopus subject areas

  • Periodontics

Cite this

Derivation and validation of the periodontal and tooth profile classification system for patient stratification. / Morelli, Thiago; Moss, Kevin L.; Beck, James; Preisser, John S.; Wu, Di; Divaris, Kimon; Offenbacher, Steven.

In: Journal of periodontology, Vol. 88, No. 2, 01.02.2017, p. 153-165.

Research output: Contribution to journalArticle

Morelli, Thiago ; Moss, Kevin L. ; Beck, James ; Preisser, John S. ; Wu, Di ; Divaris, Kimon ; Offenbacher, Steven. / Derivation and validation of the periodontal and tooth profile classification system for patient stratification. In: Journal of periodontology. 2017 ; Vol. 88, No. 2. pp. 153-165
@article{99aba4d4af9c4c8ba47bd18fdc5d49ca,
title = "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",
author = "Thiago Morelli and Moss, {Kevin L.} and James Beck and Preisser, {John S.} and Di Wu and Kimon Divaris and Steven Offenbacher",
year = "2017",
month = "2",
day = "1",
doi = "10.1902/jop.2016.160379",
language = "English (US)",
volume = "88",
pages = "153--165",
journal = "Journal of Periodontology",
issn = "0022-3492",
publisher = "American Academy of Periodontology",
number = "2",

}

TY - JOUR

T1 - Derivation and validation of the periodontal and tooth profile classification system for patient stratification

AU - Morelli,Thiago

AU - Moss,Kevin L.

AU - Beck,James

AU - Preisser,John S.

AU - Wu,Di

AU - Divaris,Kimon

AU - Offenbacher,Steven

PY - 2017/2/1

Y1 - 2017/2/1

N2 - 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.

AB - 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.

KW - Classification

KW - Diagnosis

KW - Epidemiology

KW - Gingivitis

KW - Periodontitis

KW - Prognosis

UR - http://www.scopus.com/inward/record.url?scp=85011977216&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85011977216&partnerID=8YFLogxK

U2 - 10.1902/jop.2016.160379

DO - 10.1902/jop.2016.160379

M3 - Article

VL - 88

SP - 153

EP - 165

JO - Journal of Periodontology

T2 - Journal of Periodontology

JF - Journal of Periodontology

SN - 0022-3492

IS - 2

ER -