A prediction model for periodontal disease: Modelling and validation from a National Survey of 4061 Taiwanese adults

Hongmin Lai, Chiu Wen Su, Amy Ming Fang Yen, Sherry Yueh Hsia Chiu, Jean Ching Yuan Fann, Wendy Yi Ying Wu, Shu Lin Chuang, Hsing Chih Liu, Hsiu Hsi Chen, Li Sheng Chen*

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

28 Scopus citations

Abstract

Aims The aim of this study was to predict periodontal disease (PD) with demographical features, oral health behaviour, and clinical correlates based on a national survey of periodontal disease in Taiwan. Materials and Methods A total of 4061 subjects who were enrolled in a cross-sectional nationwide survey on periodontal conditions of residents aged 18 years or older in Taiwan between 2007 and 2008 were included. The community periodontal index (CPI) was used to measure the periodontal status at the subject and sextant levels. Information on demographical features and other relevant predictive factors for PD was collected using a questionnaire. Results In our study population, 56.2% of subjects had CPI grades ≥3. Periodontitis, as defined by CPI ≥3, was best predicted by a model including age, gender, education, brushing frequency, mobile teeth, gingival bleeding, smoking, and BMI. The area under the curve (AUC) for the final prediction model was 0.712 (0.690-0.734). The AUC was 0.702 (0.665-0.740) according to cross-validation. Conclusions A prediction model for PD using information obtained from questionnaires was developed. The feasibility of its application to risk stratification of PD should be considered with regard to community-based screening for asymptomatic PD.

Original languageEnglish
Pages (from-to)413-421
Number of pages9
JournalJournal of Clinical Periodontology
Volume42
Issue number5
DOIs
StatePublished - 01 05 2015

Bibliographical note

Publisher Copyright:
© 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Keywords

  • Community Periodontal Index
  • ROC curve
  • periodontal disease
  • prediction

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