Model for Predicting Temporomandibular Dysfunction: Use of Classification Tree Analysis

Abstract The aim of this study was to construct a predictive model that uses classification tree statistical analysis to predict the occurrence of temporomandibular disorder, by dividing the sample into groups of high and low risk for the development of the disease. The use of predictive statistical approaches that facilitate the process of recognizing and/or predicting the occurrence of temporomandibular disorder is of interest to the scientific community, for the purpose of providing patients with more adequate solutions in each case. This was a cross-sectional analytical population-based study that involved a sample of 776 individuals who had sought medical or dental attendance at the Family Health Units in Recife, PE, Brazil. The sample was submitted to anamnesis using the instrument Research Diagnostic Criteria for Temporomandibular Disorders. The data were inserted into the software Statistical Package for the Social Sciences 20.0 and analyzed by the Pearson Chi-square test for bivariate analysis, and by the classification tree method for the multivariate analysis. Temporomandibular disorder could be predicted by orofacial pain, age and depression. The high-risk group was composed of individuals with orofacial pain, those between the ages of 25 and 59 years and those who presented depression. The low risk group was composed of individuals without orofacial pain. The authors were able to conclude that the best predictor for temporomandibular disorder was orofacial pain, and that the predictive model proposed by the classification tree could be applied as a tool for simplifying decision making relative to the occurrence of temporomandibular disorder.

摘要 本研究旨在构建一种基于分类树统计分析的预测模型，通过将研究样本划分为颞下颌关节紊乱病（temporomandibular disorder）发病高风险组与低风险组，实现对该病发生情况的预测。借助预测性统计方法识别或预判颞下颌关节紊乱病的发生，可为临床针对不同患者制定更适宜的诊疗方案提供支撑，这一方向已受到科学界的广泛关注。本研究为基于人群的横断面分析性研究，纳入巴西伯南布哥州累西腓市家庭卫生所就诊的776名个体作为研究样本。研究采用《颞下颌关节紊乱病研究诊断标准》相关工具对所有研究对象进行病史采集。将采集得到的数据录入社会科学统计软件包（Statistical Package for the Social Sciences）20.0版本，采用Pearson卡方检验开展双变量分析，并通过分类树法进行多变量分析。结果显示，口面部疼痛、年龄与抑郁可用于预测颞下颌关节紊乱病的发生：高风险组由存在口面部疼痛、年龄介于25~59岁以及伴有抑郁症状的个体构成，而低风险组仅包含无口面部疼痛的个体。本研究最终证实，口面部疼痛是颞下颌关节紊乱病的最佳预测因子，且本研究提出的分类树预测模型可作为简化颞下颌关节紊乱病发生相关临床决策的工具加以应用。