Abstract
Aim
To assess the diagnostic performance of self-reported oral health questions and develop a
diagnostic model with additional risk factors to predict clinical gingival inflammation in
systemically healthy adults in the United Kingdom.
Methods
Gingival inflammation was measured by trained staff and defined as bleeding on probing (present if bleeding sites >=30%). Sensitivity and specificity of self-reported questions were calculated; a diagnostic model to predict gingival inflammation was developed and its performance (calibration and discrimination) assessed.
Results
We included 2,853 participants. Self-reported questions about bleeding gums had the best
performance: the highest sensitivity was 0.73 (95% CI 0.70, 0.75) for a Likert item and the highest
specificity 0.89 (95% CI 0.87, 0.90) for a binary question. The final diagnostic model included selfreported bleeding, oral health behaviour, smoking status, previous scale and polish received. Its
area under the curve was 0.65 (95% CI 0.63 to 0.67).
Accepted Article
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Conclusion
This is the largest assessment of diagnostic performance of self-reported oral health questions and the first diagnostic model developed to diagnose gingival inflammation. A self-reported bleeding question or our model could be used to rule in gingival inflammation since they showed good sensitivity, but are limited in identifying healthy individuals and should be externally
validated.
Original language | English |
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Pages (from-to) | 919-928 |
Number of pages | 10 |
Journal | Journal of Journal of Clinical Periodontology |
Volume | 48 |
Issue number | 7 |
Early online date | 7 May 2021 |
DOIs | |
Publication status | Published - 1 Jul 2021 |
Keywords
- Diagnosis
- epidemiology
- gingival inflammation
- self-report
- prediction modelling