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Development and validation of a predictive model for periodontitis using NHANES 2011-2012 data.

Eduardo Montero¹, David Herrera¹, Mariano Sanz¹

¹ETEP (Etiology and Therapy of Periodontal Diseases) Research Group, University Complutense, Madrid, Spain.

Journal of Clinical Periodontology

|March 21, 2019

Summary

A new predictive model can identify moderate-to-severe periodontitis in US adults. This screening tool incorporates age, gender, ethnicity, glycated hemoglobin (HbA1c), and smoking habits for early detection.

Keywords:

HbA1c diabetes endocrinology glycated haemoglobin periodontitis predictive modelling

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Area of Science:

Oral health research
Public health surveillance
Cardiometabolic disease epidemiology

Background:

Periodontitis is a prevalent oral disease in North American adults.
Its prevalence is notably higher in individuals with pre-diabetes or diabetes.
Accurate screening tools are needed for early detection and intervention.

Purpose of the Study:

To develop and validate a predictive model for moderate-to-severe periodontitis in the adult US population.
To identify key risk factors associated with periodontitis.
To assess the model's utility as a screening tool in primary care.

Main Methods:

Utilized data from 3017 US adults aged >30 years from the 2011-2012 NHANES cycle.
Employed multivariable logistic regression for model development.
Included demographic, cardiometabolic risk (smoking, BMI, blood pressure, cholesterol, HbA1c), and periodontal examination data.

Main Results:

Prevalence of moderate and severe periodontitis was 37.1% and 13.2%, respectively.
Elevated glycated hemoglobin (HbA1c ≥5.7%) was significantly associated with moderate-to-severe periodontitis (OR=1.29, p<0.01).
The developed model achieved 70.0% sensitivity and 67.6% specificity using age, gender, ethnicity, HbA1c, and smoking habit.

Conclusions:

A predictive model incorporating age, gender, ethnicity, HbA1c, and smoking habit serves as a reliable screening tool for periodontitis.
This model can facilitate referrals for periodontal examination in primary medical care settings.
Early identification of at-risk patients can improve diagnosis and management of periodontitis.