Have you had bleeding from your gums? Self-report to Identify giNGival inflammation (The SING diagnostic accuracy and diagnostic model development study)

Beatriz Goulao* (Corresponding Author), Graeme MacLennan, Craig Ramsay

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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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 This article is protected by copyright. All rights reserved 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 languageEnglish
Pages (from-to)919-928
Number of pages10
JournalJournal of Journal of Clinical Periodontology
Volume48
Issue number7
Early online date7 May 2021
DOIs
Publication statusPublished - 1 Jul 2021

Keywords

  • Diagnosis
  • epidemiology
  • gingival inflammation
  • self-report
  • prediction modelling

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