Machine Learning to Predict Successful Opioid Dose Reduction or Stabilization After Spinal Cord Stimulation

Variables associated with nonresponders to high-frequency (10 kHz) spinal cord stimulation

INTRODUCTION

The use of spinal cord stimulation (SCS) therapy to treat chronic pain continues to rise. Optimal patient selection remains one of the most important factors for SCS success. However, despite increased utilization and the existence of general indications, predicting which patients will benefit from neuromodulation remains one of the main challenges for this therapy. Therefore, this study aims to identify the variables that may correlate with nonresponders to high-frequency (10 kHz) SCS to distinguish the subset of patients less likely to benefit from this intervention.

MATERIALS AND METHODS

This was a retrospective single-center observational study of patients who underwent 10 kHz SCS implant. Patients were divided into nonresponders and responders groups. Demographic data and clinical outcomes were collected at baseline and statistical analysis was performed for all continuous and categorical variables between the two groups to calculate statistically significant differences.

RESULTS

The study population comprised of 237 patients, of which 67.51% were responders and 32.49% were nonresponders. There was a statistically significant difference of high levels of kinesiophobia, high self-perceived disability, greater pain intensity, and clinically relevant pain catastrophizing at baseline in the nonresponders compared to the responders. A few variables deemed potentially relevant, such as age, gender, history of spinal surgery, diabetes, alcohol use, tobacco use, psychiatric illness, and opioid utilization at baseline were not statistically significant.

CONCLUSION

Our study is the first in the neuromodulation literature to raise awareness to the association of high levels of kinesiophobia preoperatively in nonresponders to 10 kHz SCS therapy. We also found statistically significant differences with greater pain intensity, higher self-perceived disability, and clinically relevant pain catastrophizing at baseline in the nonresponders relative to responders. It may be appropriate to screen for these factors preoperatively to identify patients who are less likely to respond to SCS. If these modifiable risk factors are present, it might be prudent to consider a pre-rehabilitation program with pain neuroscience education to address these factors prior to SCS therapy, to enhance successful outcomes in neuromodulation.

Machine Learning in Spinal Cord Stimulation for Chronic Pain

Spinal cord stimulation (SCS) is an effective treatment for chronic neuropathic pain. The success of SCS is dependent on candidate selection, response to trialing, and programming optimization. Owing to the subjective nature of these variables, machine learning (ML) offers a powerful tool to augment these processes. Here we explore what work has been done using data analytics and applications of ML in SCS. In addition, we discuss aspects of SCS which have narrowly been influenced by ML and propose the need for further exploration. ML has demonstrated a potential to complement SCS to an extent ranging from assistance with candidate selection to replacing invasive and costly aspects of the surgery. The clinical application of ML in SCS shows promise for improving patient outcomes, reducing costs of treatment, limiting invasiveness, and resulting in a better quality of life for the patient.