Related Experiment Video
Updated: Aug 17, 2025
An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP
Published on: January 27, 2010
Application of multilevel models for predicting pain following root canal treatment
Mi Du1,2,3, Dandara Gabriela Haag2,3,4, John W Lynch2,3,5
1Department of Implantology, School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong Key Laboratory of Oral Tissue Regeneration & Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration, Shandong Provincial Clinical Research Center for Oral Diseases, Shandong University, Jinan, China.
Objectives:
This study developed predictive models for one-week acute and six-month persistent pain following root canal treatment (RCT). An additional aim was to study the gain in predictive efficacy of models containing clinical factors only, over models containing sociodemographic characteristics.
Methods:
A secondary data analysis of 708 patients who received RCTs was conducted. Three sets of predictors were used: (1) combined set, containing all predictors in the data set; (2) clinical set and (3) sociodemographic set. Missing data were handled by multiple imputation using the missing indicator method. The multilevel least absolute selection and shrinkage operator (LASSO) regression was used to select predictors into the final multilevel logistic models. Three measures, the area under the receiver operating characteristic curve (AUROC) and precision-recall curve (AUPRC) and calibration curves, were used to assess the predictive performance of the models.
Results:
The selected-in factors in the final models, using LASSO regression, are related to pre- and intra-treatment clinical symptoms and pain experience. Predictive performance of the models remained the same with the inclusion (exclusion) of the socio-demographic factors. For predicting one-week outcome, the model built with combined set of predictors yielded the highest AUROC and AUPRC of 0.85 and 0.72, followed by the models built with clinical factors (AUROC = 0.82, AUPRC = 0.66). The lowest predictive ability was found in models with only sociodemographic characteristics (AUROC = 0.68, AUPRC = 0.40). Similar patterns were observed in predicting six-month outcome, where the AUROC for models with combined, clinical and sociodemographic sets of predictors were 0.85, 0.89 and 0.66, respectively, and the AUPRC were 0.48, 0.53 and 0.22, respectively.
Conclusions:
Clinical factors such as the severity and experience of pre-operative and intra-operative pain were discovered important to the subsequent development of pain following RCTs. Adding sociodemographic characteristics to the models with clinical factors did not change the models' predictive performance or the proportion of explained variance.
