Development and validation of machine learning based prediction model for postoperative pain risk after extraction of impacted mandibular third molars

Dongsheng Yu; Zifeng Liu; Weijie Zhuang; Kechen Li; Yaxin Lu

doi:10.1016/j.heliyon.2023.e23052

Development and validation of machine learning based prediction model for postoperative pain risk after extraction of impacted mandibular third molars

Heliyon. 2023 Nov 30;9(12):e23052. doi: 10.1016/j.heliyon.2023.e23052. eCollection 2023 Dec.

Authors

Dongsheng Yu¹, Zifeng Liu², Weijie Zhuang¹, Kechen Li¹, Yaxin Lu²

Affiliations

¹ Hospital of Stomatology, Sun Yat-sen University, Guangdong Provincial Clinical Research Center of Oral Diseases, Guangdong Provincial Key Laboratory of Stomatology, Guangdong Key Laboratory for Dental Disease Prevention and Control, Guangzhou, China, No.56, Lingyuan West Road, Yuexiu District, Guangzhou City, Guangdong Province, 510030, China.
² Big Data and Artificial Intelligence Center, The Third Affiliated Hospital of Sun Yat-sen University, No. 600, Tianhe Road, Tianhe District, Guangzhou City, Guangdong Province, 510630, China.

Abstract

Background: Predicting postoperative pain risk in patients with impacted mandibular third molar extractions is helpful in guiding clinical decision-making, enhancing perioperative pain management, and improving the patients' medical experience. This study aims to develop a prediction model based on machine learning algorithms to identify patients at high risk of postoperative pain after tooth extraction.

Methods: We conducted a prospective cohort study. Outpatients with impacted mandibular third molars were recruited and the outcome was defined as the NRS (Numerical Rating Scale) score of peak postoperative pain within 24 h after the operation ≥7, which is considered a high risk of postoperative pain. We compared the models built using nine different machine learning algorithms and conducted internal and time-series external validations to evaluate the model's predictive performances in terms of the area under the curve (AUC), accuracy, sensitivity, specificity, and F1-value.

Results: A total of 185 patients and 202 cases of impacted mandibular third molar data were included in this study. Five modeling variables were screened out using least absolute selection and shrinkage operator regression, including physician qualification, patient self-reported maximum pain sensitivity, OHI-S-CI, BMI, and systolic blood pressure. The overall performance of the random forest model was evaluated. The AUC, sensitivity, and specificity of the prediction model built using the random forest method were 0.879 (0.861-0.891), 0.857, and 0.846, respectively, for the training set and 0.724 (0.673-0.732), 0.667, and 0.600, respectively, for the time series validation set.

Conclusions: This study developed a machine learning-based postoperative pain risk prediction model for impacted mandibular third molar extraction, which is promising for providing a theoretical basis for better pain management to reduce postoperative pain after third molar extraction.

Keywords: Impacted mandibular third molars (IMTMs); Machine learning; Postoperative pain; Prediction model; Risk stratification.