Machine Learning-Based Prediction of Quality of Life Improvement After Surgery for Spinal Metastases

Study Design. – A prospective multicenter cohort study. Objective. – To develop and validate machine learning models for predicting health-related quality of life (HRQoL) improvements in patients after one month and six months of surgery for spinal metastases. Summary of Background Data. – The prediction of postoperative HRQoL of spinal metastases surgery remains understudied compared with studies of survival outcomes. Methods. – We analyzed data from 413 patients who underwent surgery for spinal metastases at 40 participating institutions in Japan. The primary outcome was HRQoL improvement, defined as an increase in the EuroQol 5-Dimension 5-Level (EQ-5D) utility value of ≥0.32 from baseline. We developed two models for 1-month (n=360) and 6-month (n=189) outcomes using various machine learning algorithms. Missing values were imputed, and feature selection was performed using recursive feature elimination with cross-validation. We split the data into training (80%) and test (20%) sets for each model. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, precision, and F1-score. SHapley Additive exPlanations (SHAP) analysis was used to interpret feature importance. Results. – The 6-month model outperformed the 1-month model across all metrics. For 1-month predictions, Logistic Regression achieved an AUC of 0.8136 and an accuracy of 0.7639 on the test set. For 6-month predictions, Naive Bayes demonstrated an AUC of 0.8928 and an accuracy of 0.8684. The 1-month model used 12 features, while the 6-month model required seven. SHAP analysis revealed that EQ-5D Mobility was the most influential feature in both models. Conclusions. – Our models demonstrate high predictive accuracy for HRQoL improvements following spinal metastases surgery, with superior performance of the 6-month model. These models could enhance clinical decision-making and patient counseling by providing personalized predictions of postoperative QoL. Future research should focus on external validation and integration of these models into clinical practice.