Department of Oral and Maxillofacial Surgery, School and Hospital of Stomatology, Tianjin Medical University, 12 Qixiangtai Road, Heping District, Tianjin, 300070, China.
Graduate Student of School and Hospital of Stomatology, Tianjin Medical University, Tianjin, China.
Clin Oral Investig. 2021 Apr;25(4):1915-1923. doi: 10.1007/s00784-020-03499-8. Epub 2020 Aug 10.
To develop and evaluate predictive nomogram for extended duration of surgery in patients following mandibular third molars (M3M) removal.
A retrospectively observational study was performed and designed. A credible random split-sample method was used to divide data into training and validation dataset (split ratio = 0.7:0.3). Least absolute shrinkage and selection operator (Lasso) logistic regression was applied to select predictors and develop the nomogram. The discrimination of the nomogram was assessed using the receiver operating characteristic (ROC) curve, and the calibration curve was used for evaluating the accuracy of prediction. The clinical usefulness of nomogram was also evaluated with decision curve analysis.
Root of curve, Winter classification, Pell-Gregory ramus classification, flap design, procedure, and surgical experience were identified as predictors and assembled into the nomogram. The nomogram showed good discrimination with AUC in training dataset (0.79, 95% CI: 0.73-0.85) and validation dataset (0.75, 95% CI: 0.65-0.84) and was well calibrated in both datasets (all P > 0.05). Decision curve analysis demonstrated that the nomogram was clinically useful.
This study proposed an effective nomogram with potentially application in facilitating the individualized prediction for extended operation time.
Individualized prediction of prolonged operation time can be conveniently facilitating an adequate treatment plan management and postoperative prevention.
开发并评估下颌第三磨牙（M3M）拔除后手术时间延长的预测列线图。
本研究为回顾性观察性研究设计。采用可靠的随机分组方法将数据分为训练集和验证集（分组比=0.7:0.3）。应用最小绝对收缩和选择算子（Lasso）逻辑回归选择预测因子并建立列线图。采用受试者工作特征（ROC）曲线评估列线图的判别能力，通过校准曲线评估预测准确性。还通过决策曲线分析评估列线图的临床实用性。
根型、Winter 分类、Pell-Gregory 分支分类、皮瓣设计、手术步骤和手术经验被确定为预测因子，并组合成列线图。该列线图在训练集（AUC：0.79，95%CI：0.73-0.85）和验证集（AUC：0.75，95%CI：0.65-0.84）中均具有良好的判别能力，且在两个数据集（均 P>0.05）中均具有良好的校准能力。决策曲线分析表明该列线图具有临床应用价值。
本研究提出了一种有效的列线图，可用于辅助预测手术时间的延长。
个体化预测手术时间延长可方便地辅助制定充分的治疗计划管理和术后预防措施。
