A Predictive Model for Root Caries Incidence.
A Predictive Model for Root Caries Incidence.
复制标题
根龋发生率的预测模型。
DOI：
10.1159/000445445
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发表时间：
2016
期刊：
影响因子：
4.2
通讯作者：
Vollmer,WilliamM
中科院分区：
文献类型：
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作者：
Ritter,AndréV;Preisser,JohnS;Puranik,ChaitanyaP;Chung,Yunro;Bader,JamesD;Shugars,DanielA;Makhija,Sonia;Vollmer,WilliamM
本研究旨在利用木糖醇治疗成人龋齿试验（X-ACT）的数据，寻找一组最能预测龋齿活跃成年人牙根龋齿（RC）发病率的风险指标。使用基线时根面暴露的安慰剂对照参与者和两个研究中心收集的辅助数据（n = 155），比较了五种逻辑回归模型对事件RC的预测性能。从基线变量和纳入辅助变量[吸烟、饮食、使用可摘局部假牙（RPD）、牙刷使用、收入、教育程度和牙科保险]后评估预测效果。敏感性分析为对照组和治疗组（n = 301）的模型添加了治疗，以预测对照组的RC。49%的控制组参与者有RC事件。该模型包括随访危险年数、危险根面数、RC指数、性别、种族、年龄和吸烟等因素，预测效果最佳，AUC最高，Brier评分最低。敏感性分析支持初步分析，并给出稍好一些的性能总结度量。最能预测RC发病率的一组风险指标包括危险根面数量增加和基线时RC指数增加，其次是白人和不吸烟，它们是强的无显著性预测因子。性别、年龄和风险随访年数的增加，虽然包括在模型中，但也没有统计学意义。纳入健康、饮食、RPD使用、牙刷使用、收入、教育和牙科保险等变量并没有提高预测效果。
This study aimed to find the set of risk indicators best able to predict root caries (RC) incidence in caries-active adults utilizing data from the Xylitol for Adult Caries Trial (X-ACT). Five logistic regression models were compared with respect to their predictive performance for incident RC using data from placebo-control participants with exposed root surfaces at baseline and from two study centers with ancillary data collection (n = 155). Prediction performance was assessed from baseline variables and after including ancillary variables [smoking, diet, use of removable partial dentures (RPD), toothbrush use, income, education, and dental insurance]. A sensitivity analysis added treatment to the models for both the control and treatment participants (n = 301) to predict RC for the control participants. Forty-nine percent of the control participants had incident RC. The model including the number of follow-up years at risk, the number of root surfaces at risk, RC index, gender, race, age, and smoking resulted in the best prediction performance, having the highest AUC and lowest Brier score. The sensitivity analysis supported the primary analysis and gave slightly better performance summary measures. The set of risk indicators best able to predict RC incidence included an increased number of root surfaces at risk and increased RC index at baseline, followed by white race and nonsmoking, which were strong nonsignificant predictors. Gender, age, and increased number of follow-up years at risk, while included in the model, were also not statistically significant. The inclusion of health, diet, RPD use, toothbrush use, income, education, and dental insurance variables did not improve the prediction performance.