Newswise — Spinal cord stimulation is a minimally invasive FDA-approved treatment to manage chronic pain such as back and neck pain. This neuromodulation technique uses electricity and an implantable device, which has been increasingly used over the past five years as a non-pharmacological approach to pain conditions due in part to the opioid epidemic.
Although patients undergo psychological assessment and a trial treatment before the implant, failure rates are estimated at around 25 to 30 percent. The ability to accurately predict which patients will benefit from this treatment in the long term is unclear and currently relies on the subjective experience of the implanting physician.
A study led by Julie G. Pilitsis, M.D., Ph.D., dean and vice president of medical affairs at Florida Atlantic University’s Schmidt College of Medicine, in collaboration with researchers from Albany Medical College, is the first to use machine-learning algorithms in the neuromodulation field to predict long-term patient response to spinal cord stimulation.
For the study, researchers used Pilitsis’ database – the largest single-center resource of prospectively collected longitudinal spinal cord stimulation outcomes – to develop and internally validate predictive models. They applied a combination of unsupervised clustering and supervised classification to obtain individualized models. Each subgroup/cluster comprised of a cohort of 151 patients and included 31 features. The study’s objective was to determine which patients would ultimately do well with spinal cord stimulation.
