Chronic periodontitis is a common oral disease that is a leading cause of tooth loss. This study develops a risk prediction system for severe chronic periodontitis using a mixed effects logistic regression model trained on longitudinal data from the EGAT2 cohort in Thailand. The model incorporates patient demographics, behaviors, dental features, and plaque score to predict risk. When validated, the model demonstrated high accuracy, sensitivity, and specificity over 90%, indicating it could help reduce the workload of dental examinations by identifying high-risk patients for targeted screening. However, further external validation and improved algorithms may enhance the model's performance and utility.

1. Clinical Prediction of
Chronic Periodontitis
Htun Teza
Master of Science Program in Data Science for Healthcare
Department of Clinical Epidemiology and Biostatistics
Faculty of Medicine Ramathibodi Hospital
Mahidol University

2. Chronic Periodontitis
• One of the most common oral disease
• Leading cause of tooth loss in adults
• 743 million people affected worldwide
• Prevalence –
• 11.2% globally
• 15.0 – 20.0% in Asians
• 26% in Thai Adults
• 36% in Thai Elderly

3. Diagnostic criteria
• Characterized by resorption of the alveolar bone
• Without radiographs; increase in clinical attachment level is examined

4. Severe Chronic Periodontitis
• proposed by Centers for Disease Control and Prevention/American
Academy of Periodontology (CDC/AAP) working group
• subject with
• ≥2 interproximal sites with Clinical attachment level (CAL) ≥6 mm in different teeth and
• 1 site with Periodontal pocket depth (PD) ≥5 mm

5. Examination
• The distance is manually measured using periodontal probes
• At six sites per tooth and every present teeth
• Excluding third molars: 28 teeth; 168 sites per person

6. Reducing workload
• requires dentist / dental hygienist / trained personnel to examine
• time consuming
• risk prediction system can help; especially in cases with high number of
patients such as public health screenings

7. Risk prediction system
• Commonly statistical models
• Logistic Regression for binary classification (Positive / Negative)
• Trained from cross-sectional data
• This study applies Mixed effects logistic regression
• Trained from longitudinal data
• Learning both
• Fixed effects (population average) and
• Random effects (subject specific)

8. Mixed effects models
𝑦 = 𝑋𝛽 + 𝑍𝑢 + 𝜖
– where;
𝑦 = known vector of observations (dependent variable)
𝑋, 𝑍 = known design matrices relating the observations 𝑦 to 𝛽 and 𝑢, respectively
(independent variables)
𝛽 = unknown vector of fixed effects
𝑢 = unknown vector of random effects.

9. Materials and Methods
• Applying EGAT2 cohort;
• both the 3rd survey (2/3: 2008) and
• the 4th survey (2/4: 2013)
• STATA version 16.0
• Pareto principle; training (80%) and validation (20%) datasets

10. Feature selection
• Stepwise method; Forward selection
• Univariate analysis: p-value less than 0.1 are included in multivariate analysis
• Multivariate analysis: p-value less than 0.05 are included in the final model
• Output of the classification model is dichotomized (severe / non-severe)
• using 0.35 as threshold (prevalence of severe periodontitis in our data)

11. Scoring system
• Risk of developing severe
periodontitis = !!"!#$ %&'( ')"%*
"# ! !"!#$ %&'( ')"%*
• Value more than 0.35 is predicted
as positive

12. Odd ratios
Variables Covariates Odd ratios (95% CI) P-value
Gender Male 2.63 (1.68 to 4.10) < 0.001
Female ref
Education < Highschool 7.68 (3.62 to 16.30) < 0.001
Vocational School 3.86 (1.93 to 7.72) < 0.001
Bachelor’s degree 1.34 (0.68 to2.64) < 0.001
> Bachelor’s degree ref 0.393
Smoking Non-smoker ref
Ex-smoker 2.09 (1.38 to 3.17) 0.001
Current smoker 5.38 (3.28 to 8.83) < 0.001
Diabetes Mellitus Positive 1.66 (1.07 to 2.57) 0.024
Negative ref
Number of teeth 0.94 (0.91 to 0.97) < 0.001
Plaque score 1.03 (1.02 to 1.03) < 0.001

13. Classification performance
Metrics On training data On validation data
Sensitivity 91.4% (89.5% to 93.0%) 89.5% (85.1% to 92.9%)
Specificity 90% (88.7% to 91.3%) 92.5% (89.9% to 94.6%)
Accuracy 90.5% (89.4% to 91.5%) 91.5% (89.3% to 93.3%)
Area under Receiver Operating Curve (AUC) 0.91 (0.90 to 0.92) 0.91 (0.89 to 0.93)
Positive likelihood ratio 9.18 (8.05 to 10.50) 11.9 (8.77 to 16.30)
Positive predictive value 82.9% (80.7% to 85.0%) 86.2% (81.6% to 90.1%)
Negative predictive value 95.2% (94.1% to 96.1%) 94.4% (92.0% to 96.2%)
• AUC ranges from 0 to 1:
• value of 0.5 : no more discriminative power than a coin flip
• 0.7 to 0.8 : acceptable
• 0.8 to 0.9 : excellent
• more than 0.9 : outstanding

14. Limitations
• Both training and validation data are from the same cohort; external
validation would be better for model evaluation
• Not all oral examination is eliminated; plaque score requires trained
personnel to evaluate albeit easier than comprehensive probing
• Higher performing algorithms such as machine-learning and neural
networks to further enhance the model performance
• Further studies and investigations until the model is completely self-
reportable

15. Conclusion
• Screening tool removing exhaustive periodontal probing and
• reduce the workload of dentists and dental hygienists
• reduce time and resource requirements.
• Mixed effects model accounts for subject-specific effects of latent variables
resulting in better estimation of population average effects
• Using longitudinal data to develop classification model results in high
performance.

16. Thank You
Htun Teza
Master of Science Program in Data Science for Healthcare
Department of Clinical Epidemiology and Biostatistics
Faculty of Medicine Ramathibodi Hospital
Mahidol University