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你是否有过牙龈出血的情况?自我报告以确定牙龈炎症(SING 诊断准确性和诊断模型开发研究)。

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

机构信息

Health Services Research Unit, Centre for Healthcare Randomized Trials, University of Aberdeen, Aberdeen, UK.

出版信息

J Clin Periodontol. 2021 Jul;48(7):919-928. doi: 10.1111/jcpe.13455. Epub 2021 May 7.

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 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.

摘要

目的

评估自我报告的口腔健康问题的诊断性能,并结合其他危险因素开发一个诊断模型,以预测英国系统健康成年人的临床牙龈炎症。

方法

由经过培训的工作人员测量牙龈炎症,并将探诊出血(如果出血部位≥30%,则存在出血)定义为牙龈炎症。计算自我报告问题的敏感性和特异性;开发预测牙龈炎症的诊断模型,并评估其性能(校准和区分)。

结果

我们纳入了 2853 名参与者。关于牙龈出血的自我报告问题表现最佳:Likert 项目的最高敏感性为 0.73(95%CI 0.70,0.75),二项式问题的最高特异性为 0.89(95%CI 0.87,0.90)。最终的诊断模型包括自我报告的出血、口腔健康行为、吸烟状况、既往洁治和抛光。其曲线下面积为 0.65(95%CI 0.63-0.67)。

结论

这是对自我报告的口腔健康问题的诊断性能的最大评估,也是第一个开发的用于诊断牙龈炎症的诊断模型。自我报告的出血问题或我们的模型可用于推断牙龈炎症,因为它们具有良好的敏感性,但在识别健康个体方面存在局限性,应进行外部验证。

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