Development of prediction models and predictors analysis for axial neck pain in patients undergoing cervical laminoplasty based on machine learning

Background Axial neck pain (ANP) is one of the most common complications after cervical laminoplasty, leading to severe pain, disability and economic loss. By predicting patient outcomes pre-operatively, patients undergoing cervical laminoplasty can benefit from more accurate patient care strategies. However, predicting postoperative ANP is challenging. The aim of this study was to develop a machine learning model to predict at the individual level whether a patient experiences postoperative ANP and to reveal baseline predictors of persistent neck pain after laminoplasty. Methods This retrospective study includes 1982 patients. The population characteristics, clinical symptoms and signs, imaging features and preoperative scale of patients were retrospectively collected as input variables. The outcome measure was whether the patient achieved minimal clinically significant difference (MCID) in the visual analogue scale (VAS) score for postoperative ANP. Models were trained and optimized by process of machine learning (ML), including feature engineering, data pre-processing, and 8:2 training/validation-testing split of datasets. The feature-reduced model was established afterwards, and its performance and feature importance were evaluated through internal and external testing. Results Among the models generated by 45 features, XGBoost model yielded the highest AUROC of 0.7631 (95% CI, 0.7221–0.8051). Age, preoperative mJOA score, VAS score, SF36-body pain, SF36-mental health, SF36-role emotional, SF36-physiological function, lower limb weakness, and positive Hoffmann’ sign were selected as input features to build the feature-reduced model. In both internal and external testing of the feature-reduced models, model of Logistic_Regression algorithms reached the best performance, with AUROC of 0.9047 (95% CI, 0.8633–0.9406) for internal testing and 0.9200 (95% CI, 0.8678–0.9676) for external testing. Conclusion In this study, models for predicting the progress of postoperative ANP based on machine learning were established. The Logistic Regression model had a good ability to predict ANP progression of CSM patients and achieved best performance in a multicenter independent testing cohort. Feature importance analysis revealed key baseline predictors of postoperative ANP. This study proved that the potential of ML to predict the progress of ANP after cervical laminoplasty was significant, providing research basis for the training of machine learning models with larger samples and more features in the future.