Periodontal profile classes predict periodontal disease progression and tooth loss.
Periodontal profile classes predict periodontal disease progression and tooth loss.
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DOI：
10.1002/jper.17-0427
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发表时间：
2018-03
影响因子：
4.3
通讯作者：
Offenbacher S
中科院分区：
文献类型：
--
作者：
Morelli T;Moss KL;Preisser JS;Beck JD;Divaris K;Wu D;Offenbacher S
目前的牙周病分类对预测疾病进展和牙齿脱落的效用有限;事实上，牙齿脱落本身会破坏精确的个人水平的牙周病分类。为了克服这一局限性，目前的小组最近引入了一种新的患者分层系统，使用潜在的临床参数，包括缺牙模式的类分析。本研究旨在确定牙周组织轮廓分类和牙齿轮廓分类（PPC/TPC）分类法用于风险评估的临床实用性，特别是用于预测牙周病进展和偶发性牙齿脱落。分析样本包括两项前瞻性队列研究（社区牙齿动脉粥样硬化风险研究和皮埃蒙特牙科研究）的4，682名成年参与者，其中包括牙周病进展和牙齿脱落的信息。PPC/TPC分类法包括7种不同的PPC（人级疾病模式和严重程度）和7种TPC（牙齿级疾病）。Logistic回归模型用于估计这些潜在类别与疾病进展和牙齿脱落事件相关的相对风险（RR）和95%置信区间（CI），调整检查中心，种族，性别，年龄，糖尿病和吸烟。为了获得个性化的结果倾向，与每个参与者的PPC和TPC相关的风险估计被合并为个人水平的复合风险评分（周期性风险指数[IPR]）。两种PPC（PPC-G：严重疾病和PPC-D：牙齿脱落）中的个体具有最高的牙齿脱落风险（RR = 3.6; 95%CI = 2.6至5.0和RR = 3.8; 95%CI = 2.9至5.1）。PPC-G也有牙周炎进展的最高风险（RR = 5.7; 95%CI = 2.2至14.7）。个性化IPR评分与牙周炎进展和牙齿脱落呈正相关。这些研究结果，在额外的验证，表明牙周/牙齿轮廓类和衍生的个性化倾向评分提供临床牙周定义，反映疾病模式的人口，并提供了一个有用的系统，患者分层是预测疾病进展和牙齿脱落。
Current periodontal disease taxonomies have limited utility for predicting disease progression and tooth loss; in fact, tooth loss itself can undermine precise person-level periodontal disease classifications. To overcome this limitation, the current group recently introduced a novel patient stratification system using latent class analyses of clinical parameters, including patterns of missing teeth. This investigation sought to determine the clinical utility of the Periodontal Profile Classes and Tooth Profile Classes (PPC/TPC) taxonomy for risk assessment, specifically for predicting periodontal disease progression and incident tooth loss. The analytic sample comprised 4,682 adult participants of two prospective cohort studies (Dental Atherosclerosis Risk in Communities Study and Piedmont Dental Study) with information on periodontal disease progression and incident tooth loss. The PPC/TPC taxonomy includes seven distinct PPCs (person-level disease pattern and severity) and seven TPCs (tooth-level disease). Logistic regression modeling was used to estimate relative risks (RR) and 95% confidence intervals (CI) for the association of these latent classes with disease progression and incident tooth loss, adjusting for examination center, race, sex, age, diabetes, and smoking. To obtain personalized outcome propensities, risk estimates associated with each participant’s PPC and TPC were combined into person-level composite risk scores (Index of Periodontal Risk [IPR]). Individuals in two PPCs (PPC-G: Severe Disease and PPC-D: Tooth Loss) had the highest tooth loss risk (RR = 3.6; 95% CI = 2.6 to 5.0 and RR = 3.8; 95% CI = 2.9 to 5.1, respectively). PPC-G also had the highest risk for periodontitis progression (RR = 5.7; 95% CI = 2.2 to 14.7). Personalized IPR scores were positively associated with both periodontitis progression and tooth loss. These findings, upon additional validation, suggest that the periodontal/tooth profile classes and the derived personalized propensity scores provide clinical periodontal definitions that reflect disease patterns in the population and offer a useful system for patient stratification that is predictive for disease progression and tooth loss.