367 Development of Machine Learning Based Models to Predict Treatment Response to Spinal Cord Stimulation

Abstract

INTRODUCTION: Despite spinal cord stimulation’s (SCS) proven efficacy, failure rates are high with no clear understanding of which patients benefit long-term. Currently, patient selection for SCS is based on the subjective experience of the implanting physician. METHODS: A combined unsupervised (clustering) and supervised (classification) ML techniques was applied on a prospectively collected cohort of 151 patients which included 31 features. Clusters identified using unsupervised K-means clustering were fitted with individualized predictive models of logistic regression, random forest and XGboost. RESULTS: Two distinct clusters were found and patients in the cohorts significantly differed in age, duration of chronic pain, pre-operative numeric rating scale (NRS) and pre-operative pain catastrophizing scale scores. Using the 10 most influential features, logistic regression predictive models with a nested cross-validation demonstrated the highest overall performance with area under the curve (AUC) of 0.757 and 0.708 for each respective cluster. CONCLUSION: This combined unsupervised-supervised learning approach yielded high predictive performance, suggesting advance ML-derived approaches have potential to be utilized as a functional clinical tool to improve long-term SCS outcomes.