Eduardo Montero1, David Herrera1, Mariano Sanz1, Sangeeta Dhir2, Thomas Van Dyke3,4, Corneliu Sima3,4. 1. ETEP (Etiology and Therapy of Periodontal Diseases) Research Group, University Complutense, Madrid, Spain. 2. Department of Dentistry. Consultant Periodontist, Max Super Specialty Hospital, Saket, New Delhi, India. 3. Center for Clinical and Translational Research, Forsyth Institute, Cambridge, Massachusetts. 4. Department of Oral Medicine, Infection and Immunity, Harvard School of Dental Medicine, Boston, Massachusetts.
Abstract
AIM: To develop and validate a predictive model for moderate-to-severe periodontitis in the adult USA population, with data from the 2011-2012 National Health and Nutrition Examination Survey (NHANES) cycle. MATERIAL AND METHODS: A subset of 3017 subjects aged >30 years, with >14 teeth present and having received a periodontal examination in addition to data collected on cardio-metabolic risk measures (smoking habit, body mass index [BMI], blood pressure, total cholesterol and glycated haemoglobin [HbA1c]) were used for model development by multivariable logistic regression. RESULTS: The prevalence of moderate and severe periodontitis using CDC/AAP classification was 37.1% and 13.2%, respectively. A multivariable logistic regression model revealed that HbA1c ≥5.7% was significantly associated with moderate-to-severe periodontitis (odds ratio, OR = 1.29; p < 0.01). A predictive model including age, gender, ethnicity, HbA1c and smoking habit as variables had 70.0% sensitivity and 67.6% specificity in detecting moderate-to-severe periodontitis in US adults. CONCLUSIONS: Periodontitis is a common disease in North American adults, and its prevalence is significantly higher in individuals with pre-diabetes or diabetes. The present study demonstrates that a model including age, gender, ethnicity, HbA1c and smoking habit could be used as a reliable screening tool for periodontitis in primary medical care settings to facilitate referral of patients at risk for periodontal examination and diagnosis.
AIM: To develop and validate a predictive model for moderate-to-severe periodontitis in the adult USA population, with data from the 2011-2012 National Health and Nutrition Examination Survey (NHANES) cycle. MATERIAL AND METHODS: A subset of 3017 subjects aged >30 years, with >14 teeth present and having received a periodontal examination in addition to data collected on cardio-metabolic risk measures (smoking habit, body mass index [BMI], blood pressure, total cholesterol and glycated haemoglobin [HbA1c]) were used for model development by multivariable logistic regression. RESULTS: The prevalence of moderate and severe periodontitis using CDC/AAP classification was 37.1% and 13.2%, respectively. A multivariable logistic regression model revealed that HbA1c ≥5.7% was significantly associated with moderate-to-severe periodontitis (odds ratio, OR = 1.29; p < 0.01). A predictive model including age, gender, ethnicity, HbA1c and smoking habit as variables had 70.0% sensitivity and 67.6% specificity in detecting moderate-to-severe periodontitis in US adults. CONCLUSIONS:Periodontitis is a common disease in North American adults, and its prevalence is significantly higher in individuals with pre-diabetes or diabetes. The present study demonstrates that a model including age, gender, ethnicity, HbA1c and smoking habit could be used as a reliable screening tool for periodontitis in primary medical care settings to facilitate referral of patients at risk for periodontal examination and diagnosis.
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