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OBJECTIVES: Lumbar spine surgery is a common procedure for treating disabling spine-related pain. In recent decades, both the number and cost of spine surgeries have increased despite technological advances and modification in surgical technique. For those patients that have continued uncontrolled back and/or lower extremity pain following lumbar spine surgery, spinal cord stimulation (SCS) has emerged as a viable treatment option. However, the impact of lumbar spine surgical history remains largely unstudied. Specifically, the current study considers the impact of number of prior lumbar spine surgeries on pain relief outcomes following SCS implantation. MATERIALS AND METHODS: We queried the electronic medical record of five separate pain practices for all patients who have undergone a SCS implant between January 1, 2017, and March 1, 2020. Inclusion criteria consisted of any patients with an SCS implant who underwent a prior lumbar spine surgery. The primary outcome was the mean calculated percentage pain relief in patients based on number of prior lumbar spine surgeries. RESULTS: There was a total of 1974 total SCS implant cases identified across five separate pain clinics. There was no difference in mean calculated pain relief in patients with one prior spine surgery versus those with two or more prior spine surgeries (28.2% vs. 25.8%, adjusted ß-coefficient -3.1, 95% CI -8.9 to 2.7, p = 0.290). Similarly, when analyzing number of spine surgeries as a continuous variable, there was no association between number of spine surgeries and calculated pain relief (adjusted ß-coefficient -1.5, 95% CI -4.0 to 1.1, p = 0.257). Additionally, after patients were stratified based on waveform, there was no association between number of prior lumbar spine surgeries (analyzed both as a categorical and continuous variable) and calculated percentage pain relief. CONCLUSIONS: This multicentered retrospective study found that there was no significant difference in pain scores in individuals who received SCS following one or more lumbar spine surgeries. Additionally, the waveform of the SCS device had no statistically significant impact on post-operative pain scores following one or more lumbar spine surgeries.
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Artificial intelligence was introduced 60 years ago and has evolved immensely since that time. While artificial intelligence is found in nearly all aspects of our life, the use of artificial intelligence in the healthcare industry has only recently become apparent and more widely discussed. It is expected that artificial intelligence will allow improved disease recognition, treatment optimization, cost and time savings, product development, decision making, and marketing. For pain medicine specifically, these same benefits will be translatable and we can expect better disease recognition and treatment selection. As adoption occurs with this impressive technology, it will be imperative for the pain medicine community to be informed on proper definitions and expected use cases for artificial intelligence. Our objective was to provide pain medicine physicians an overview of artificial intelligence, including important definitions to aid understanding, and to offer potential clinical applications pertinent to the specialty.
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Diabetes mellitus remains a public health problem, affecting 422 million people worldwide. Currently, there is no consensus around treating painful diabetic peripheral neuropathy in a step-wise manner. Among the non-pharmacological interventions, neuromodulation has become a promising alternative. Over the past decade, significant clinical trials have paved the way for prompt inclusion of high-frequency spinal cord stimulation within the painful diabetic peripheral neuropathy treatment algorithm. This article aims to provide an updated evidence-based approach for the management of painful diabetic peripheral neuropathy.
