Introduction

A significant proportion of patients with back and leg pain end up with chronic pain for which spinal cord stimulation (SCS) has proven to be an effective treatment modality.^1-2 It is unclear which patient benefits most from which therapy and at what timing. Real world big data collection in the diverse population in daily practice forms an opportunity to optimize treatments.

Methods/Materials

A patient driven data big data collection application, the Back-App, was developed. Hundred sixteen (n=116) patients selected for SCS treatment were included in a hierarchical cluster analysis by Ward’s method. The analysis was based on baseline characteristics for pain (VAS leg and back), the Pain Catastrophizing Scale, the Oswestry disability Index and quality of life (EQ-5D) before treatment. Clusters were compared for the use of pain medication and employment status.

Results

The multivariate analysis illustrated that three clusters can be explained by the significant effect of all five variables in the analyses (p<0.001).
Cluster 1 (n=77) is characterized by the highest pain scores for leg (6,92, p<0,001), but mostly back pain (8,26, p<0,001), high PCS and high disability. This results in a significantly lower QOL (0,10, p<0,001) compared to cluster 2 and 3. Patients in cluster 2 state lower VAS scores for back and leg pain, 4,02 and 2,82 respectively (p<0,001) with lower PCS and ODI. QOL is preserved in cluster 2 (0,731, p<0,001). The last cluster 3 includes patients with high VAS for back and leg pain (7,13 and 7,00, p<0,001) with high PCS and disability. In contrast to cluster 1, there is no impact on QOL in cluster 3. VAS for back pain was higher in cluster 1 than in cluster 2 and 3, whereas VAS for leg pain was comparable with cluster 3. The is no difference in employment status. Cluster 1 patients used more pain medication from the WHO II and III classification than patients in cluster 2 and 3 (p<0,001).

Discussion

It is the first study to include pain, pain catastrophizing, QOL and disability together in the cluster analysis for SCS patients. This research gives insight in the complex population selected for SCS for chronic low back and/or leg and is an added value to sparsely existing literature on big data collection in spinal cord stimulation.

Conclusions

Future research will focus on the outcome analysis in the different patient populations that were illustrated by this data collection.

References

1. Trust, L., Oklahoma City, O. K., & Grider, J. (2016). Effectiveness of spinal cord stimulation in chronic spinal pain: a systematic review. Pain physician, 19, E33-E54.

2. Kumar, K., Taylor, R. S., Jacques, L., Eldabe, S., Meglio, M., Molet, J., ... & North, R. B. (2008). The effects of spinal cord stimulation in neuropathic pain are sustained: a 24-month follow-up of the prospective randomized controlled multicenter trial of the effectiveness of spinal cord stimulation. Neurosurgery, 63(4), 762-770.