A Novel Composite Metric for Predicting Patient Satisfaction With Spinal Cord Stimulation

Abstract

Objective: To identify relationships between clinical assessments of chronic pain to enable the generation of a multivariate model to predict patient satisfaction with spinal cord stimulation (SCS) treatment.

Materials and methods: Data from an exploratory clinical trial of sub-perception SCS (SPSCS) were reviewed. Forty-seven subjects tested multiple SPSCS programs for three to four days each. At the end of each program period, subjects recorded pain intensity, patient satisfaction with treatment (PSWT), modified patient global impression of change, and physical activity tolerance times. Twelve outcome variables were evaluated. Pearson's correlation coefficient was used to assess pair-wise correlations. Multigenerational mixed effects modeling was performed to create a model to best explain relationships between those variables.

Results: A final model was generated that predicted PSWT using evening pain intensity (EPI) and the interaction between EPI and walking tolerance time. The mixed effects model allows for visualization of the interactions between EPI, walking tolerance time, and patient satisfaction with SCS.

Conclusions: Patient-centered outcomes are desirable when evaluating complex multidimensional health impairments but accurately predicting patient satisfaction with treatment remains a challenge. Understanding the variables that predict (either by causation or association) satisfaction would be useful for clinicians. The results of this study suggest that a composite measure of activity tolerance (i.e., walking tolerance) and pain intensity can predict patient satisfaction with SCS therapy. This study highlights the utility of composite outcomes metrics in evaluating the benefits of SCS for chronic low back and leg pain.