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Modelling changes in clinical attachment loss to classify periodontal disease progression
Ricardo Teles1,2, Habtamu K Benecha3, John S Preisser3
1Department of Periodontology, School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Journal of Clinical Periodontology
|March 4, 2016
Summary
Linear mixed models (LMM) improve the identification of progressing periodontal sites by analyzing longitudinal clinical attachment loss (CAL) measurements. This method offers a more accurate classification of disease progression and regression over time.
Area of Science:
- Periodontology
- Biostatistics
- Dental Research
Background:
- Periodontal disease is a significant cause of tooth loss.
- Accurate identification of disease progression is crucial for timely intervention.
- Traditional methods for assessing clinical attachment loss (CAL) may be subject to measurement error.
Purpose of the Study:
- To apply linear mixed models (LMM) to longitudinal clinical attachment loss (CAL) data.
- To identify and classify periodontal sites exhibiting disease progression or regression.
- To establish a reliable threshold for defining periodontal site progression.
Main Methods:
- Longitudinal CAL measurements were collected bi-monthly for 12 months from 93 healthy and 236 periodontitis subjects.
- Proportions of sites with CAL increase and reversal were calculated.
- LMM were fitted to predict CAL levels and categorize sites based on progression/regression criteria.
Main Results:
- Over 12 months, site progression rates were 21.2%, 2.8%, and 0.3% for 1, 2, and 3 mm CAL increase thresholds, respectively.
- A high percentage of sites initially classified as progressing showed reversal (42.0% to 77.7%).
- LMM classification showed high concordance with observed CAL increases, with lower reversal rates (10.6% to 53.0%).
Conclusions:
- Linear mixed models (LMM) effectively account for measurement errors in longitudinal CAL data.
- LMM provide an improved and more accurate method for classifying periodontal sites regarding disease progression.
- This statistical approach enhances the reliability of identifying active periodontal disease.
