种植修复术后食物嵌塞的危险因素分析及列线图预测模型构建

Risk factors for food impaction following implant restoration and development of a nomogram prediction model

摘要

目的 分析种植修复术后食物嵌塞的影响因素, 构建预测模型, 并提出相应的预防策略。方法 收集2023年1月至2024年12月温州医科大学附属口腔医院收治的因牙体缺损、牙列缺失等接受种植修复术患者310例作为研究对象, 采用留出法, 以7∶3比例将患者随机分为建模组(n=217)和验证组(n=93)。收集患者临床资料, 采用LASSO回归分析筛选变量纳入多因素分析, 以获得种植修复术后食物嵌塞的危险因素, 据此构建列线图模型, 采用受试者工作特征曲线(ROC)和校准曲线验证模型的预测性能, 采用决策曲线分析(DCA)和临床影响曲线(CIC)验证模型的临床实用性。结果 研究纳入患者食物嵌塞发生率为40.00%(124/310), LASSO回归分析基于λ_1se筛选出7个关键变量纳入多因素分析, 结果显示, 邻接触强度缺失[OR(95%CI)=2.973(1.364, 6.478)]、邻接区至牙槽嵴顶距离≥7 mm[OR(95%CI)=3.058(1.519, 6.158)]、种植区角化龈宽度≤1 mm[OR(95%CI)=2.062(1.042, 4.082)]、术区牙槽骨吸收[OR(95%CI)=4.172(1.926, 9.039)]、每日刷牙次数>2次[OR(95%CI)=3.369(1.538, 7.378)]、每日口腔清洁次数<1次[OR(95%CI)=3.012(1.152, 7.880)]、术后6个月菌斑指数升高[OR(95%CI)=2.297(1.106, 4.769)]均为种植修复术后食物嵌塞的独立危险因素(P<0.05)。ROC分析显示, 建模组和验证组ROC曲线下面积分别为0.802[95%CI(0.740, 0.864)]和0.813[95%CI(0.716, 0.910)]。Hosmer-Lemeshow检验建模组和验证组χ²分别为5.475(P=0.706)和5.332(P=0.720), 校准曲线显示, 建模组和验证组的校准斜率分别为0.88和0.97, Brier评分分别为0.11和0.06。DCA分析显示, 在高风险阈值0.10~0.83和0.20~0.92范围内, 采用列线图模型对患者进行预防性干预, 可获得净效益。CIC分析显示, 高风险阈值0.2~1.0范围内, 采用列线图模型对患者术后食物嵌塞风险进行筛查, 预测发生与实际发生食物嵌塞的患者例数差异在可接受范围内。结论 种植修复术后食物嵌塞与多因素有关, 基于相关危险因素构建模型可精准预测患者术后食物嵌塞风险, 可为临床制定个性化预防策略提供依据。

Abstract

Objective To analyze the risk factors for food impaction following implant restoration, construct a prediction model, and propose targeted preventive strategies. Methods Data were collected from 310 patients who underwent implant restoration for partial or complete edentulism in Hospital of Stomatology, Wenzhou Medical University between January 2023 and December 2024. A split-sample approach was employed to randomly divide patients into modeling (n=217) and validation (n=93) groups in a 7∶3 ratio. Clinical data were collected, and LASSO regression was used to screen candidate variables for inclusion in subsequent multivariable analysis to identify risk factors for food impaction following implant restoration. A nomogram was constructed based on these findings. Receiver operating characteristic (ROC) curves and calibration curves were used to assess predictive performance, while decision curve analysis (DCA) and clinical impact curve (CIC) were performed to evaluate clinical utility. Results The incidence of food impaction among enrolled patients was 40.00% (124/310). LASSO regression identified seven key variables based on λ _1se for inclusion in multivariable analysis. Multivariable analysis revealed that proximal contact loss [OR (95% CI) = 2.973 (1.364, 6.478)], distance from the proximal contact area to the alveolar ridge crest ≥7 mm [OR (95% CI) = 3.058 (1.519, 6.158)], width of keratinized gingiva ≤1 mm at the implant site [OR (95% CI)= 2.062 (1.042, 4.082)], alveolar bone resorption at the surgical site [OR (95% CI)= 4.172 (1.926, 9.039)], frequency of daily tooth brushing >2 times [OR (95% CI)= 3.369 (1.538, 7.378)], frequency of daily oral hygiene maintenance <1 time [OR (95% CI)= 3.012 (1.152, 7.880)], and elevated plaque index at 6 months postoperatively [OR (95% CI)= 2.297 (1.106, 4.769)] were independent risk factors for food impaction following implant restoration (all P<0.05). ROC analysis demonstrated areas under the curve of 0.802 [95% CI (0.740, 0.864)] in the modeling group and 0.813 [95% CI (0.716, 0.910)] in the validation group. Hosmer-Lemeshow goodness-of-fit tests yielded χ² values of 5.475 (P = 0.706) and 5.332 (P = 0.720) for the modeling and validation groups, respectively. Calibration slopes were 0.88 and 0.97, with Brier scores of 0.11 and 0.06. DCA demonstrated net benefit of applying the nomogram for preventive intervention across threshold probability ranges of 0.10-0.83 and 0.20-0.92, respectively. CIC analysis indicated that, within the threshold probability range of 0.2-1.0, the nomogram-based predicted probability showed good agreement with the actual observed risk of food impaction. Conclusion Food impaction after implant restoration is associated with multiple factors. This nomogram accurately predicts postoperative food impaction risk and provides a basis for developing personalized preventive strategies in clinical practice.

关键词

牙种植 / 修复术后 / 食物嵌塞 / 危险因素 / 牙槽嵴 / 角化龈 / 口腔卫生 / 预测模型

Key words

dental implantation / postoperative restoration / food impaction / risk factors / alveolar ridge / keratinized gingiva / oral hygiene / predictive models

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基金资助

浙江省医药卫生科技计划项目(2024KY988)