LAUSR

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.