Feng Qiao1, Xiaohuan Huang2, Bolong Li2, Rui Dong3, Xin Huang4, Jun Sun4. 1. Associate Chief Physician, Department of Oral and Maxillofacial Surgery, School and Hospital of Stomatology, Tianjin Medical University, Tianjin, China. Electronic address: qiaofeng@tmu.edu.cn. 2. Graduate Student, Department of Oral and Maxillofacial Surgery, School and Hospital of Stomatology, Tianjin Medical University, Tianjin, China. 3. Attending Surgeon, Department of Oral and Maxillofacial Surgery, School and Hospital of Stomatology, Tianjin Medical University, Tianjin, China. 4. Resident Physician, Department of Oral and Maxillofacial Surgery, School and Hospital of Stomatology, Tianjin Medical University, Tianjin, China.
Abstract
PURPOSE: The individualized prediction of postoperative symptom severity is essential for selecting interventions after mandibular third molar (M3M) removal. The purpose of the present study was to develop and validate a nomogram for personal prediction of postoperative symptom severity. MATERIALS AND METHODS: A prospective cohort study was performed in the Stomatology Hospital of Tianjin Medical University. The sample was divided into training and testing data sets by time. The demographic, anatomic, radiographic, and operative variables were recorded. The self-reported postoperative symptom severity was recorded and defined as the primary outcome variable. Stepwise forward algorithms were applied to informative predictors based on Akaike's information criterion. Multivariable logistic regression analysis was used to develop the nomogram. An independent testing data set was used to validate the nomogram. Receiver operating characteristic curves and the Hosmer-Lemeshow test were used to assess model performance. P < .05 was considered to indicate statistical significance. RESULTS: The sample included 321 subjects who had undergone M3M removal. An independent validation data set included 103 consecutive patients. The median operation time was 15.0 minutes (interquartile range, 8.3 to 21.6 minutes) in the training data set (n = 218). Patients with serious postoperative symptoms accounted for 48.6 and 47.6% of the training and testing data sets, respectively. Gender, age, smoking status, operation time, Pell-Gregory ramus classification, and preoperative symptoms were identified as predictors and assembled into the nomogram. The area under curve demonstrated adequate discrimination in the validation data set (0.69; 95% confidence interval, 0.59 to 0.80). The nomogram was well calibrated, with a Hosmer-Lemeshow χ2 statistic of 6.33 (P = .78) in the testing data set. The confusion matrix was also summarized, and the accuracy was 63.3 and 65.1% in the training and testing data set, respectively. CONCLUSIONS: The present study has proposed an effective nomogram with potential application in facilitating the individualized prediction of postoperative symptom severity after M3M removal.
PURPOSE: The individualized prediction of postoperative symptom severity is essential for selecting interventions after mandibular third molar (M3M) removal. The purpose of the present study was to develop and validate a nomogram for personal prediction of postoperative symptom severity. MATERIALS AND METHODS: A prospective cohort study was performed in the Stomatology Hospital of Tianjin Medical University. The sample was divided into training and testing data sets by time. The demographic, anatomic, radiographic, and operative variables were recorded. The self-reported postoperative symptom severity was recorded and defined as the primary outcome variable. Stepwise forward algorithms were applied to informative predictors based on Akaike's information criterion. Multivariable logistic regression analysis was used to develop the nomogram. An independent testing data set was used to validate the nomogram. Receiver operating characteristic curves and the Hosmer-Lemeshow test were used to assess model performance. P < .05 was considered to indicate statistical significance. RESULTS: The sample included 321 subjects who had undergone M3M removal. An independent validation data set included 103 consecutive patients. The median operation time was 15.0 minutes (interquartile range, 8.3 to 21.6 minutes) in the training data set (n = 218). Patients with serious postoperative symptoms accounted for 48.6 and 47.6% of the training and testing data sets, respectively. Gender, age, smoking status, operation time, Pell-Gregory ramus classification, and preoperative symptoms were identified as predictors and assembled into the nomogram. The area under curve demonstrated adequate discrimination in the validation data set (0.69; 95% confidence interval, 0.59 to 0.80). The nomogram was well calibrated, with a Hosmer-Lemeshow χ2 statistic of 6.33 (P = .78) in the testing data set. The confusion matrix was also summarized, and the accuracy was 63.3 and 65.1% in the training and testing data set, respectively. CONCLUSIONS: The present study has proposed an effective nomogram with potential application in facilitating the individualized prediction of postoperative symptom severity after M3M removal.