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 2853 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 self-reported bleeding, oral health behaviour, smoking status, previous scale and polish received. Its area under the curve was 0.65 (95% CI 0.63-0.67). 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.
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 2853 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 self-reported bleeding, oral health behaviour, smoking status, previous scale and polish received. Its area under the curve was 0.65 (95% CI 0.63-0.67). 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.