Kayaoglu Guven, Gürel Mügem, Saricam Esma, Ilhan Mustafa Necmi, Ilk Ozlem
Department of Endodontics, Faculty of Medicine, Gazi University, Ankara, Turkey.
Department of Endodontics, Faculty of Medicine, Gazi University, Ankara, Turkey.
J Endod. 2016 Jan;42(1):36-41. doi: 10.1016/j.joen.2015.09.021. Epub 2015 Nov 12.
This observational study sought to assess the incidence of intraoperative pain (IOP) among patients receiving endodontic treatment and to construct a model for predicting the probability of IOP.
All patients attending the endodontic training clinic at Gazi University, Ankara, Turkey, during the spring term of 2014 were examined (N = 2785 patients; observation completed in 1435 patients; male: 628, female: 807; mean age: 39 years; 1655 teeth total). Demographic and clinical variables were recorded for patients requiring primary endodontic treatment. Local anesthesia was administered and routine endodontic treatment commenced. After the working length was established, each patient was asked to report any pain according to a visual analog scale. Supplementary local infiltration anesthesia was administered if necessary. If pain continued despite supplementary anesthesia, then the pain score was immediately assessed. A visual analog scale score corresponding to more than mild pain indicated IOP. A predictive model was constructed with multiple logistic regression analysis from the data of 85% of cases, with the remaining 15% of cases being used to test the external validity of the model.
The incidence of IOP was 6.1% (101/1655 cases). One tooth from each patient was randomly selected, with 1435 teeth being retained for further analysis. A multiple logistic regression model was constructed with the variables age, tooth type, arc, pulpal diagnosis, pain present within the previous 24 hours, and anesthetic solution (P < .05). Good fits were obtained for the final model and external control, with a correct classification rate (efficiency) of 0.78, sensitivity (true positive rate) of 0.63, and specificity (true negative rate) of 0.79 for the external control.
A successful predictive model of IOP was constructed with demographic and clinical variables.
本观察性研究旨在评估接受牙髓治疗患者的术中疼痛（IOP）发生率，并构建一个预测IOP发生概率的模型。
对2014年春季学期在土耳其安卡拉加齐大学牙髓治疗培训诊所就诊的所有患者进行检查（N = 2785例患者；1435例患者完成观察；男性：628例，女性：807例；平均年龄：39岁；共1655颗牙齿）。记录需要进行初次牙髓治疗患者的人口统计学和临床变量。给予局部麻醉并开始常规牙髓治疗。确定工作长度后，要求每位患者根据视觉模拟量表报告任何疼痛情况。必要时给予补充局部浸润麻醉。如果尽管进行了补充麻醉仍有疼痛，则立即评估疼痛评分。视觉模拟量表评分对应超过轻度疼痛表明存在IOP。使用85%病例的数据通过多元逻辑回归分析构建预测模型，其余15%的病例用于测试模型的外部有效性。
IOP发生率为6.1%（101/1655例）。从每位患者中随机选择一颗牙齿，保留1435颗牙齿进行进一步分析。使用年龄、牙齿类型、牙弓、牙髓诊断、前24小时内是否存在疼痛以及麻醉溶液等变量构建多元逻辑回归模型（P <.05）。最终模型和外部对照拟合良好，外部对照的正确分类率（效率）为0.78，敏感性（真阳性率）为0.63，特异性（真阴性率）为0.79。
利用人口统计学和临床变量构建了一个成功的IOP预测模型。
