Sejal V. Jain

Background: Spinal cord stimulators can offer an effective treatment in chronic pain refractory to conventional medical management. However, with a failure rate of up to 44% and an annual explantation rate of 6–9%, there is a need to better identify patients at high risk for therapeutic failure. The objective of this retrospective cohort study was to determine predictors of early SCS explantation following device placement. Methods: The Medical Informatics Operating room Vitals and Events Repository database was queried for patients with a spinal cord stimulator and at least two years of follow-up (n = 56). A multivariate logistic regression was fitted. Recursive factor elimination with cross-validation and L1 penalization were used to reduce the number of predictors and minimize the risk of overfitting. The model was used to predict risk factors for explantation, odds ratio (OR), 95% confidence interval (CI), and false discovery rate-adjusted p-value. Results: The final model displayed adequate performance with an average precision of 0.769. Sleep disorders were identified as a statistically significant predictor of SCS explantation (OR: 3.88, CI: 1.36–11.04, FDR p-value: 0.0497). Conclusions: While further prospective studies are needed, our study indicates that sleep disorders are a risk factor for spinal cord stimulator explantation and should be considered during pre-operative evaluation.

Background: Spinal cord stimulators can offer an effective treatment in chronic pain refractory to conventional medical management. However, with a failure rate of up to 44% and an annual explant rate of 6-9%, there is a need to better identify patients at high risk of therapeutic failure. The objective of this retrospective cohort study was to determine predictors of early SCS explant following device placement. Methods: The Medical Informatics Operating room Vitals and Events Repository database was queried for patients with a spinal cord stimulator and at least two years of follow up (n = 56). A multivariate logistic regression was fitted. Recursive factor elimination and bootstrap validation were used to minimize risk of overfitting. The model was used to predict risk factors for explant, odds ratio (OR), and 95% confidence interval (CI). Results: The final model displayed good performance with a bootstrap mean Area Under the Receiver Operating Curve of 0.91 (bootstrap CI: 0.74 – 1.0) and bootstrap mean accuracy of 82.1% (bootstrap CI: 63.0% – 97.7%). Fibromyalgia (OR: 2.38; CI: 2.34 – 2.41), hyperlipidemia (OR: 2.24; CI: 2.20 – 2.27), sleep disorder (OR: 2.10; CI: 2.07 – 2.14), obesity (OR: 1.93; CI: 1.90 – 1.95), and irritable bowel syndrome (OR: 1.63; CI: 1.61 – 1.65) displayed statistically significant increased risk of explantation. Conclusions: A medical history of fibromyalgia, hyperlipidemia, sleep disorders, obesity, and irritable bowel syndrome are novel risk factors for spinal cord stimulator explantation. While further prospective studies are needed, our study would suggest these factors may be worth considering in pre-operative evaluation.

Sleep disturbances and chronic pain are prevalent and interrelated conditions that have significant impact on individuals’ quality of life. Understanding the intricate dynamics between sleep and pain is crucial for developing effective treatments that enhance the well-being of affected individuals and reduce the economic burden of these debilitating conditions. This narrative review examines the complex relationship between sleep disturbances and chronic pain. We describe the prevalence and types of sleep disturbances and sleep disorders in chronic pain patients. Posteriorly, we critically review the clinical and experimental evidence, investigating the relationship between sleep disturbances and chronic pain, aiming to clarify the impact of chronic pain on sleep and, conversely, the impact of sleep disturbances on pain perception. In conclusion, the literature largely agrees on the existence of a bidirectional relationship between chronic pain and sleep disturbances, though the strength of each direction in this association remains uncertain. Current evidence suggests that sleep impairment more strongly predicts pain than pain does sleep impairment. Additionally, addressing sleep disturbances in chronic pain patients is crucial, as poor sleep has been linked to higher levels of disability, depression, and pain-related catastrophizing.

Sleep disturbances and chronic pain are prevalent and interrelated conditions that significantly impact individuals' quality of life. Understanding the intricate dynamics between sleep and pain is crucial for developing effective treatments that enhance the well-being of affected individuals and reduce the economic burden of these debilitating conditions. This narrative review examines the complex relationship between sleep disturbances and chronic pain. We describe the prevalence and types of sleep disturbances and sleep disorders in chronic pain patients. Posteriorly we critically review the clinical and experimental evidence investigating the relationship between sleep disturbances and chronic pain aiming to clarify the impact of chronic pain on sleep and, conversely, the impact of sleep disturbances on pain perception. We conclude that a bidirectional relationship between chronic pain and sleep disturbances is almost a consensus in the literature, however, the strength of each direction of the association is less clear. As of now, the literature suggests that sleep impairment is a stronger predictor of pain than pain is a predictor of sleep impairment. Additionally, addressing sleep disturbances in chronic pain patients is crucial, as poor sleep has been linked to higher levels of disability, depression, and pain-related catastrophizing.