Background: Spinal cord stimulation (SCS) is a highly effective therapy for treating and management of refractory chronic pain. However, complex nature of pain and infrequent in-clinic visits, determining subject’s long-term response to the therapy remains difficult. Frequent measurement of pain in this population can help with early diagnosis, disease progression monitoring, and evaluating long-term therapeutic efficacy. This paper compares the utilization of the common subjective patient-reported outcomes with objective measures captured through a wearable device for predicting the response to SCS therapy. Method: Data is from the ongoing international prospective post-market REALITY clinical study, which collects long-term patient-reported outcomes from 557 subjects and the sub-study designed for collecting additional wearables data on a subset of participants for up to six months after SCS implantation. We first implemented a combination of dimensionality reduction algorithms and correlation analyses to explore the mathematical relationships between objective wearable data and subjective patient-reported outcomes. We then developed machine learning models to predict SCS therapy outcome based on the subject’s response to NRS or PGIC. Results: Principal component analysis results showed that psychological aspects of pain were closely associated with heart rate variability, while movement-related measures were closely associated with patient-reported outcomes related to physical function and social role participation. Our machine learning models using objective wearable data predicted both PGIC and NRS outcomes with high accuracy in the absence of subjective data. The prediction accuracy was higher for PGIC compared with the NRS using subjective-only measures primarily driven by the patient satisfaction feature. Similarly, the PGIC questions, reflects an overall change since the study onset and could be a better predictor of long-term therapy outcome. Conclusions: The significance of this study is to introduce a novel use of wearable data collected from a subset of patients to capture multi-dimensional aspects of pain and compare the prediction power with the subjective data from a larger data set. The discovery of pain digital biomarkers could result in a better understanding of the patient’s response to therapy and their general well-being.
