Dataset Information

Modelling changes in clinical attachment loss to classify periodontal disease progression.

ABSTRACT:

Aim

The goal of this study was to identify progressing periodontal sites by applying linear mixed models (LMM) to longitudinal measurements of clinical attachment loss (CAL).

Methods

Ninety-three periodontally healthy and 236 periodontitis subjects had their CAL measured bi-monthly for 12 months. The proportions of sites demonstrating increases in CAL from baseline above specified thresholds were calculated for each visit. The proportions of sites reversing from the progressing state were also computed. LMM were fitted for each tooth site and the predicted CAL levels used to categorize sites regarding progression or regression. The threshold for progression was established based on the model-estimated error in predictions.

Results

Over 12 months, 21.2%, 2.8% and 0.3% of sites progressed, according to thresholds of 1, 2 and 3 mm of CAL increase. However, on average, 42.0%, 64.4% and 77.7% of progressing sites for the different thresholds reversed in subsequent visits. Conversely, 97.1%, 76.9% and 23.1% of sites classified as progressing using LMM had observed CAL increases above 1, 2 and 3 mm after 12 months, whereas mean rates of reversal were 10.6%, 30.2% and 53.0% respectively.

Conclusion

LMM accounted for several sources of error in longitudinal CAL measurement, providing an improved method for classifying progressing sites.

SUBMITTER: Teles R

PROVIDER: S-EPMC5021116 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Publications

Modelling changes in clinical attachment loss to classify periodontal disease progression.

Teles Ricardo R Benecha Habtamu K HK Preisser John S JS Moss Kevin K Starr Jacqueline R JR Corby Patricia P Genco Robert R Garcia Nathalia N Giannobile William V WV Jared Heather H Torresyap Gay G Salazar Elida E Moya Julie J Howard Cynthia C Schifferle Robert R Falkner Karen L KL Gillespie Jane J Dixon Debra D Cugini MaryAnn M

Journal of clinical periodontology 20160406 5

<h4>Aim</h4>The goal of this study was to identify progressing periodontal sites by applying linear mixed models (LMM) to longitudinal measurements of clinical attachment loss (CAL).<h4>Methods</h4>Ninety-three periodontally healthy and 236 periodontitis subjects had their CAL measured bi-monthly for 12 months. The proportions of sites demonstrating increases in CAL from baseline above specified thresholds were calculated for each visit. The proportions of sites reversing from the progressing st ...[more]

PMID: 26935472

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Project description: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.

Project description:Metabolic syndrome, a cluster of 3 or more risk factors for cardiovascular disease, is associated with periodontal disease, but few studies have been prospective in design. This study's aim was to determine whether metabolic syndrome predicts tooth loss and worsening of periodontal disease in a cohort of 760 men in the Department of Veterans Affairs Dental Longitudinal Study and Normative Aging Study who were followed up to 33 y from 1981 to 2013. Systolic and diastolic blood pressures were measured with a standard mercury sphygmomanometer. Waist circumference was measured in units of 0.1 cm following a normal expiration. Fasting blood samples were measured in duplicate for glucose, triglyceride, and high-density lipoprotein. Calibrated periodontists served as dental examiners. Periodontal outcome events on each tooth were defined as progression to predefined threshold levels of probing pocket depth (?5 mm), clinical attachment loss (?5 mm), mobility (?0.5 mm), and alveolar bone loss (?40% of the distance from the cementoenamel junction to the root apex, on radiographs). Hazards ratios (95% confidence intervals) of tooth loss or a periodontitis event were estimated from tooth-level extended Cox proportional hazards regression models that accounted for clustering of teeth within individuals and used time-dependent status of metabolic syndrome. Covariates included age, education, smoking status, plaque level, and initial level of the appropriate periodontal disease measure. Metabolic syndrome as defined by the International Diabetes Federation increased the hazards of tooth loss (1.39; 1.08 to 1.79), pocket depth ?5 mm (1.37; 1.14 to 1.65), clinical attachment loss ?5 mm (1.19; 1.00 to 1.41), alveolar bone loss ?40% (1.25; 1.00 to 1.56), and tooth mobility ?0.5 mm (1.43; 1.07 to 1.89). The number of positive metabolic syndrome conditions was also associated with each of these outcomes. These findings suggest that the metabolic disturbances that comprise the metabolic syndrome may play a role in the development or worsening of periodontitis.

Dataset Information

Modelling changes in clinical attachment loss to classify periodontal disease progression.

Aim

Methods

Results

Conclusion

Publications

Modelling changes in clinical attachment loss to classify periodontal disease progression.

Similar Datasets

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