数字化导板引导种植修复的美学效果及疗效影响因素分析

doi:10.19439/j.sjos.2025.01.009

摘要/Abstract

摘要： 目的: 分析数字化导板引导种植修复术治疗牙列缺损的美学效果、种植精度,探讨影响疗效的相关因素。方法: 选择2020年5月—2022年6月行数字化导板引导种植修复术治疗的牙列缺损患者168例为试验组,选取同期84例行常规修复的牙列缺损患者为对照组,比较种植精度（颈部距离偏差、根尖部距离偏差、深度偏差、角度偏差）、红色美学评分（pink esthetic score,PES）和白色美学评分（white esthetic score,WES）。随访1年,根据修复效果将试验组分为良好组（n=139）和不良组（n=29）。采用logistic回归分析影响牙列缺损患者经数字化导板引导种植修复术疗效的危险因素,依据危险因素构建疗效不良风险综合指数。绘制受试者工作特征（receiver operating characteristics,ROC）曲线,分析综合指数,预测牙列缺损患者疗效不良的AUC值、敏感度、特异度。采用SPSS 22.0软件包对数据进行统计学分析。结果: 试验组颈部距离偏差、根尖部距离偏差、深度偏差、角度偏差显著低于对照组（P<0.05）。试验组术后6个月的PES评分、WES评分显著高于对照组（P<0.05）。不良组和良好组性别、年龄、种植体、缺牙原因、高血压史、饮酒史、种植体直径、种植体长度和骨增量手术等指标相比,差异无统计学意义（P>0.05）;而缺牙骨密度、糖尿病、吸烟史、探诊深度（probe depth,PD）和龈沟出血指数（sulcus bleeding index,SBI）等相比,差异有统计学意义（P<0.05）。Logistic回归分析显示,缺牙骨密度Ⅲ~Ⅳ级、糖尿病史、吸烟史和SBI≥2是影响牙列缺损患者经数字化导板引导种植修复术疗效的危险因素（P<0.05）。ROC曲线分析显示,综合指数、缺牙骨密度、糖尿病、吸烟史和SBI预测牙列缺损患者疗效不良的AUC分别为0.846、0.725、0.487、0.731和0.702,敏感度分别为44.70%、53.20%、59.60%和66.00%,特异度分别为90.20%、92.20%、85.60%和76.50%。结论: 数字化导板引导种植修复术治疗牙列缺损效果理想,有利于提高种植精度和美观度,但缺牙骨密度、糖尿病史、吸烟史和SBI会影响疗效,依据这些危险因素构建综合指数,对评估疗效具有更高的预测效能。

关键词: 数字化导板, 口腔种植修复, 牙列缺损, 美学效果, 影响因素

Abstract: PURPOSE: To explore the aesthetic effects and influencing factors of digital guided oral implant restoration in the treatment of dentition defect. METHODS: A total of 168 patients with dentition defect who underwent digital guided dental implant restoration from May 2020 to June 2022 were selected as the experimental group, while 84 patients with dentition defect who underwent routine restoration were selected as the control group. The planting accuracy(neck distance deviation, apical distance deviation, depth deviation and angle deviation), PES score and WES score were compared. The experimental group was divided into good group (n=139) and poor group (n=29) according to the repair effect. Logistic regression analysis was used to analyze the risk factors affecting the therapeutic effect of digital-guided oral implant prosthesis in patients with denture defects, and based on the risk factors, a risk nomogram prediction model for adverse therapeutic effect in patients with denture defects was constructed. Receiver operating characteristic (ROC) curve was drawn to analyze the nomogram to predict the AUC value, sensitivity and specificity of patients with dentition defect. SPSS 22.0 software package was used for statistical analysis. RESULTS: The neck distance deviation, root tip distance deviation, depth deviation and angle deviation in the experimental group were significantly lower than those in the control group(P<0.05). The PES scores and WES scores of the experimental group were significantly higher than those of the control group 6 months after treatment (P<0.05). There was no significant difference in gender, age, implant, cause of tooth loss, history of hypertension, drinking history, implant diameter, implant length and bone increment surgery between good and poor groups(P>0.05). There were significant differences in bone mineral density(BMD), diabetes mellitus, smoking history, probe depth and gingival sulcus bleeding index(P<0.05). Logistic regression analysis showed that grade Ⅲ-Ⅳ bone mineral density(BMD), diabetes history, smoking history, gingival crevicular bleeding index ≥2 were the risk factors affecting the treatment effect of digital guided oral implant repair in patients with dental defect(P<0.05). ROC curve analysis showed that AUC of nomogram prediction model, bone mineral density of missing teeth, diabetes mellitus, smoking history and gingival sulcus bleeding index in predicting adverse treatment effect of patients with dentition defect were 0.846, 0.725, 0.487, 0.731 and 0.702; the sensitivity was 44.70%, 53.20%, 59.60% and 66.00%,the specificity was 90.20%, 92.20%, 85.60% and 76.50%, respectively. CONCLUSIONS: Digital-guided oral implant restoration has a relatively ideal effect in the treatment of dentition defects, which is conducive to improving implant accuracy and aesthetics. However, bone mineral density(BMD), diabetes history, smoking history and gingival crevicular bleeding index will affect the therapeutic effect. The prediction model based on these risk factors has a higher predictive efficacy in evaluating the therapeutic effect.

Key words: Digital guide plate, Oral implant prosthesis, Dentition defect, Aesthetic effect, Influencing factor

中图分类号:

R782.1

林勇, 刘卿, 张军华, 常晓荣, 侯丹. 数字化导板引导种植修复的美学效果及疗效影响因素分析[J]. 上海口腔医学, 2025, 34(1): 48-53.

LIN Yong, LIU Qing, ZHANG Jun-hua, CHANG Xiao-rong, HOU Dan. Analysis of the aesthetic effect and influencing factors of digital guided implant prosthesis in the treatment of dentition defect[J]. Shanghai Journal of Stomatology, 2025, 34(1): 48-53.

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