Song J, Zhao H, Pan C, Li C, Liu J, Pan Y
Department of Periodontics and Oral Biology, School of Stomatology, China Medical University, Shenyang, 110002, China.
BMC Med Inform Decis Mak. 2017 Sep 15;17(1):135. doi: 10.1186/s12911-017-0533-2.
Chronic periodontitis is a multifactorial polygenetic disease with an increasing number of associated factors that have been identified over recent decades. Longitudinal epidemiologic studies have demonstrated that the risk factors were related to the progression of the disease. A traditional multivariate regression model was used to find risk factors associated with chronic periodontitis. However, the approach requirement of standard statistical procedures demands individual independence. Multilevel modelling (MLM) data analysis has widely been used in recent years, regarding thorough hierarchical structuring of the data, decomposing the error terms into different levels, and providing a new analytic method and framework for solving this problem. The purpose of our study is to investigate the relationship of clinical periodontal index and the risk factors in chronic periodontitis through MLM analysis and to identify high-risk individuals in the clinical setting.
Fifty-four patients with moderate to severe periodontitis were included. They were treated by means of non-surgical periodontal therapy, and then made follow-up visits regularly at 3, 6, and 12 months after therapy. Each patient answered a questionnaire survey and underwent measurement of clinical periodontal parameters.
Compared with baseline, probing depth (PD) and clinical attachment loss (CAL) improved significantly after non-surgical periodontal therapy with regular follow-up visits at 3, 6, and 12 months after therapy. The null model and variance component models with no independent variables included were initially obtained to investigate the variance of the PD and CAL reductions across all three levels, and they showed a statistically significant difference (P < 0.001), thus establishing that MLM data analysis was necessary. Site-level had effects on PD and CAL reduction; those variables could explain 77-78% of PD reduction and 70-80% of CAL reduction at 3, 6, and 12 months. Other levels only explain 20-30% of PD and CAL reductions. Site-level had the greatest effect on PD and CAL reduction.
Non-surgical periodontal therapy with regular follow-up visits had a remarkable curative effect. All three levels had a substantial influence on the reduction of PD and CAL. Site-level had the largest effect on PD and CAL reductions.
慢性牙周炎是一种多因素的多基因疾病,近几十年来已发现越来越多与之相关的因素。纵向流行病学研究表明,这些危险因素与疾病的进展有关。传统的多元回归模型用于寻找与慢性牙周炎相关的危险因素。然而,标准统计程序的方法要求个体独立性。近年来,多水平建模(MLM)数据分析被广泛应用,它对数据进行全面的分层结构分析,将误差项分解到不同层次,并为解决这一问题提供了一种新的分析方法和框架。我们研究的目的是通过MLM分析探讨慢性牙周炎临床牙周指标与危险因素之间的关系,并在临床环境中识别高危个体。
纳入54例中重度牙周炎患者。对他们进行非手术牙周治疗,然后在治疗后3、6和12个月定期进行随访。每位患者回答问卷调查并接受临床牙周参数测量。
与基线相比,非手术牙周治疗后,在治疗后3、6和12个月定期随访时,探诊深度(PD)和临床附着丧失(CAL)有显著改善。最初获得了不包含自变量的空模型和方差成分模型,以研究所有三个层次上PD和CAL降低的方差,结果显示有统计学显著差异(P<0.001),从而确定MLM数据分析是必要的。位点水平对PD和CAL降低有影响;这些变量可以解释在3、6和12个月时PD降低的77 - 78%和CAL降低的70 - 80%。其他层次仅解释PD和CAL降低的20 - 30%。位点水平对PD和CAL降低的影响最大。
定期随访的非手术牙周治疗有显著疗效。所有三个层次对PD和CAL的降低都有实质性影响。位点水平对PD和CAL降低的影响最大。