Machine Learning Approach in Predicting Clinically Significant Improvements After Surgery in Patients with Cervical Ossification of the Posterior Longitudinal Ligament

Study Design. A retrospective analysis of prospectively collected data. Objective. This study aimed to create a prognostic model for surgical outcomes in patients with cervical ossification of the posterior longitudinal ligament (OPLL) using machine learning (ML). Summary of Background Data. Determining surgical outcomes helps surgeons provide prognostic information to patients and manage their expectations. ML is a mathematical model that finds patterns from a large sample of data and makes predictions outperforming traditional statistical methods. Methods. Of 478 patients, 397 and 370 patients had complete follow-up information at 1 and 2 years, respectively, and were included in the analysis. A minimal clinically important difference (MCID) was defined as an acquired Japanese Orthopedic Association (JOA) score of ≥2.5 points, after which a ML model that predicts whether MCID can be achieved 1 and 2 years after surgery was created. Patient background, clinical symptoms, and imaging findings were used as variables for analysis. The ML model was created using LightGBM, XGBoost, random forest, and logistic regression, after which the accuracy and area under the receiver-operating characteristic curve (AUC) were calculated. Results. The mean JOA score was 10.3 preoperatively, 13.4 at 1 year after surgery, and 13.5 at 2 years after surgery. XGBoost showed the highest AUC (0.72) and high accuracy (67.8) for predicting MCID at 1 year, whereas random forest had the highest AUC (0.75) and accuracy (69.6) for predicting MCID at 2 years. Among the included features, total preoperative JOA score, duration of symptoms, body weight, sensory function of the lower extremity sub-score of the JOA, and age were identified as having the most significance in most of ML models. Conclusion. Constructing a prognostic ML model for surgical outcomes in patients with OPLL is feasible, suggesting the potential application of ML for predictive models of spinal surgery. Level of Evidence: 4