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结合自我报告信息与影像学骨丧失情况筛查牙周炎:一项效能研究。

Combining Self-Reported Information with Radiographic Bone Loss to Screen Periodontitis: A Performance Study.

作者信息

Mendes José João, Neves Margarida, Supiot Clara, Pinto Leonor, Tenda Diogo, Silva Nuno, Proença Luís, Leira Yago, Machado Vanessa, Botelho João

机构信息

Egas Moniz Center for Interdisciplinary Research (CiiEM), Egas Moniz School of Health & Science, 2829-511 Almada, Portugal.

Periodontology Unit, Faculty of Medicine and Odontology, University of Santiago de Compostela, 15705 Santiago de Compostela, Spain.

出版信息

J Clin Med. 2025 Jun 26;14(13):4531. doi: 10.3390/jcm14134531.

Abstract

: The objective of this study is to evaluate the diagnostic performance of a combined screening approach using self-reported periodontal information and radiographic periodontal bone loss (R-PBL) in identifying individuals with periodontitis. : An exploratory cross-sectional study was conducted including adult participants with available panoramic radiographs and responses to a validated self-reported periodontal screening questionnaire. R-PBL was assessed on interproximal sites and classified according to established thresholds. Self-reported information followed a validated strategy based on the Center for Diseases Control tool. The performance of individual and combined indicators was analyzed against the 2018 case definition for periodontitis, calculating sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). : A total of 150 participants were included, equally divided between periodontitis cases and controls, with a mean age of 46.5 years. The R-PBL model demonstrated the best predictive performance for both periodontitis (AUC: 0.833) and severe periodontitis (AUC: 0.796), with the highest precision and net benefit across thresholds. The Either model showed similar performance, particularly in sensitivity, while SR and Both models underperformed. Decision curve analysis confirmed the superior clinical utility of 'R-PBL' and 'Either' models in guiding decision-making. : Combining self-reported information with radiographic bone loss showed adequate screening performance for periodontitis. This dual approach may provide a feasible strategy for identifying high-risk individuals in settings where full clinical examination is not possible.

摘要

本研究的目的是评估一种联合筛查方法的诊断性能,该方法使用自我报告的牙周信息和放射学牙周骨丧失(R-PBL)来识别牙周炎患者。

进行了一项探索性横断面研究,纳入了有可用全景X线片且对经过验证的自我报告牙周筛查问卷有回应的成年参与者。在邻面部位评估R-PBL,并根据既定阈值进行分类。自我报告信息遵循基于疾病控制中心工具的经过验证的策略。根据2018年牙周炎病例定义分析个体和联合指标的性能,计算敏感性、特异性、阳性预测值(PPV)、阴性预测值(NPV)以及受试者操作特征曲线下面积(AUC)。

总共纳入了150名参与者,牙周炎病例和对照各占一半,平均年龄为46.5岁。R-PBL模型在牙周炎(AUC:0.833)和重度牙周炎(AUC:0.796)的预测性能方面均表现最佳,在各个阈值下具有最高的精度和净效益。“Either”模型表现出相似的性能,尤其是在敏感性方面,而“SR”模型和“Both”模型表现较差。决策曲线分析证实了“R-PBL”模型和“Either”模型在指导决策方面具有更高的临床实用性。

将自我报告信息与放射学骨丧失相结合,对牙周炎显示出足够的筛查性能。这种双重方法可能为在无法进行全面临床检查的情况下识别高危个体提供一种可行的策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33da/12249554/0a41bbee44a2/jcm-14-04531-g001.jpg

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