Australian Research Centre for Population Oral Health, Adelaide Dental School, University of Adelaide, South Australia, Australia.
Australian Research Centre for Population Oral Health, Adelaide Dental School, University of Adelaide, South Australia, Australia; Robinson Research Institute, University of Adelaide, South Australia, Australia.
Int Dent J. 2021 Oct;71(5):407-413. doi: 10.1016/j.identj.2020.12.013. Epub 2021 Feb 18.
Periodontal examinations are time-consuming and potentially uncomfortable for recipients. We modelled if self-reported questions alone, or combined with objective evidence of periodontal bone loss observable from radiographs, are accurate predictors of periodontitis.
Self-reported data from the Australian National Survey of Adult Oral Heath 2004-06 were compared with clinical periodontal examinations to assess the validity of 8 periodontitis screening questions in predicting moderate/severe periodontitis. To model alveolar bone loss, a proxy variable simulating radiographic clinical attachment level (rCAL) was created. Three multivariable binary logistic regression models were constructed: responses to 8 screening questions alone (Model 1), screening questions combined with 5 classic periodontitis risk indicators (age, sex, smoking status, country of birth, and diabetes status) (Model 2), and the addition of rCAL (Model 3). Predictive validity was determined via sensitivity (Se) and specificity (Sp) scores and graphically represented using area under the receiver operator characteristic curves (AUROC).
Data from 3630 participants periodontally examined determined that 32.4% exhibited periodontitis. Periodontitis risk indicators were all significantly associated with periodontitis case status. Six of 8 screening questions (Model 1) were weak periodontitis predictors (Se = 0.28; Sp = 0.89; AUROC = 0.61). Combining 13 variables for (Model 2) improved prediction (Se = 0.55; Sp = 0.81; AUROC = 0.77). The addition of rCAL (Model 3) improved diagnostic capacity considerably (AUROC = 0.86).
Self-reported questions combined with classic risk indicators are "useful" for periodontitis screening. Addition of radiographs markedly improved diagnostic validity. Based on modelling, nondental health care professionals may provisionally screen for periodontitis with minimal training.
牙周检查既耗时又可能令受检者感到不适。我们构建模型，探讨仅通过自我报告问题，或结合放射影像中可见的牙周骨丧失的客观证据，是否能准确预测牙周炎。
2004-06 年澳大利亚全国成人口腔健康调查的自我报告数据与临床牙周检查进行了比较，以评估 8 项牙周炎筛查问题预测中重度牙周炎的准确性。为了对牙槽骨丧失进行建模，创建了一个模拟放射临床附着水平（rCAL）的代理变量。构建了三个多变量二项逻辑回归模型：仅回答 8 项筛查问题（模型 1）、筛查问题与 5 项经典牙周炎风险指标（年龄、性别、吸烟状况、出生地和糖尿病状况）结合（模型 2），以及增加 rCAL（模型 3）。通过敏感性（Se）和特异性（Sp）评分来确定预测有效性，并通过受试者工作特征曲线下面积（AUROC）图形表示。
对 3630 名接受牙周检查的参与者进行数据分析，发现 32.4%的参与者患有牙周炎。牙周炎风险指标均与牙周炎病例状态显著相关。8 项筛查问题中的 6 项（模型 1）对牙周炎的预测能力较弱（Se=0.28；Sp=0.89；AUROC=0.61）。结合 13 项变量（模型 2）可提高预测效果（Se=0.55；Sp=0.81；AUROC=0.77）。增加 rCAL（模型 3）可显著提高诊断能力（AUROC=0.86）。
自我报告问题结合经典风险指标对牙周炎筛查“有用”。添加放射影像可显著提高诊断的准确性。基于模型，非牙科保健专业人员可能可以通过最少的培训来初步筛查牙周炎。
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