收稿日期: 2026-05-09
修回日期: 2026-05-31
录用日期: 2026-06-02
网络出版日期: 2026-08-13
Predictive modeling of unfavorable outcomes after vital pulp therapy in children's carious first permanent molars
Received date: 2026-05-09
Revised date: 2026-05-31
Accepted date: 2026-06-02
Online published: 2026-08-13
目的 分析儿童第一恒磨牙龋源性活髓保存治疗(VPT)预后不良的危险因素,并构建列线图(Nomogram)预测模型。 方法 回顾性分析2020年1月至2024年12月在苏州口腔医院因龋病行VPT的儿童第一恒磨牙患者(862例,862颗患牙)的临床资料,按7∶3比例随机分为建模集(n = 603)和验证集(n = 259)。采用LASSO回归选择最优因素,结合多因素logistic回归分析构建临床预测模型,并进行内部验证。通过受试者工作特征(ROC)曲线、校准曲线及临床决策曲线分析(DCA)评估模型的区分度、校准度及临床实用价值。 结果 建模集预后不良发生率为12.60%(76/603),验证集为12.36%(32/259)。LASSO回归共筛选出8个非零系数变量,包括牙根发育、自发疼痛史、牙髓活力测试、盖髓材料、窝洞分类、患牙修复时机、牙髓状态-不可复性牙髓炎(哑变量)和止血时间 > 5 min(哑变量)。多因素logistic回归分析显示,牙根发育已完成(OR = 6.077)、自发疼痛史(OR = 5.331)、牙髓活力测试异常(OR = 4.926)、窝洞分类Ⅱ类(OR = 4.579)、患牙延期修复(OR = 3.013)、止血时间 > 5 min(OR = 2.431)、不可复性牙髓炎(OR = 2.392)为儿童第一恒磨牙龋源性VPT预后不良的独立危险因素;盖髓材料iRoot BP Plus(OR = 0.440)为其保护因素。ROC曲线分析显示,预测模型在建模集中曲线下面积(AUC)为0.899(95%CI:0.872 ~ 0.926,P < 0.001),验证集AUC为0.936(95%CI:0.903 ~ 0.963,P < 0.001)。采用Bootstrap法重复抽样1 000次行内部验证,校准曲线显示模型预测值与实际值具有良好的一致性;DCA结果进一步证实该模型具有良好的临床应用价值。 结论 牙根发育、自发疼痛史、牙髓活力测试、不可复性牙髓炎、止血时间> 5 min、窝洞分类、患牙修复时机和盖髓材料对儿童第一恒磨牙龋源性VPT术后预后不良的早期预测有较好的价值。
杨欢 , 熊正慧 , 王智亨 . 儿童第一恒磨牙龋源性VPT预后不良的预测模型构建[J]. 实用医学杂志, 2026 , 42(15) : 2853 -2862 . DOI: 10.3969/j.issn.1006-5725.2026.15.023
Objective To analyze the risk factors for unfavorable outcomes following vital pulp therapy (VPT) in carious first permanent molars in children and to develop a nomogram-based predictive model. Methods Clinical data from 862 pediatric patients (862 teeth) who underwent VPT for caries-induced pulp exposure in first permanent molars at Suzhou Stomatological Hospital between January 2020 and December 2024 were retrospectively analyzed. Patients were randomly divided into a training set (n = 603) and a validation set (n = 259) in a 7∶3 ratio. The least absolute shrinkage and selection operator (LASSO) regression was applied to identify optimal predictive variables, which were subsequently incorporated into a multivariable logistic regression analysis to construct a nomogram-based prediction model. Model discrimination, calibration, and clinical utility were evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA), respectively. Results The incidence of unfavorable outcomes was 12.60% (76/603) in the training set and 12.36% (32/259) in the validation set. LASSO regression identified eight variables with non-zero coefficients: root development, history of spontaneous pain, pulp vitality status, pulp-capping material, cavity classification, timing of restoration, irreversible pulpitis, and hemostasis time > 5 minutes. Multivariable logistic regression analysis revealed that root development (OR = 6.077), history of spontaneous pain (OR = 5.331), pulp vitality status (OR = 4.926), cavity classification (OR = 4.579), timing of restoration (OR = 3.013), hemostasis time > 5 minutes (OR = 2.431), and irreversible pulpitis (OR = 2.392) were independent risk factors for unfavorable outcomes following VPT in carious first permanent molars. Conversely, the use of iRoot BP Plus as a pulp-capping material (OR = 0.440) was identified as a protective factor. ROC curve analysis demonstrated an area under the curve (AUC) of 0.899 (95% CI: 0.872 - 0.926, P < 0.001) in the training set and 0.936 (95% CI: 0.903 - 0.963, P < 0.001) in the validation set. Internal validation using the bootstrap method with 1 000 resampling iterations showed excellent agreement between predicted and observed probabilities on the calibration curves, and DCA confirmed the favorable clinical utility of the model. Conclusion Root development, history of spontaneous pain, pulp vitality status, cavity classification, timing of restoration, prolonged hemostasis time (> 5 minutes), irreversible pulpitis, and the choice of pulp-capping material are strong predictors of unfavorable outcomes following VPT in carious first permanent molars in children. The developed nomogram provides a valuable tool for the early identification of high-risk patients and guiding clinical decision-making.
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