Control Signals in Closed-Loop Spinal Cord Stimulation in Patients with Chronic Pain: A Scoping Review

Spinal cord stimulation (SCS) provides significant relief for patients with chronic pain; however, many approaches have limitations in programming complexity, personalization, and long-term efficacy. Traditional open-loop systems require manual programming and fail to adapt to patient-specific anatomical or physiological changes over time. In response, closed-loop SCS systems have emerged, offering real-time modulation based on biomarkers such as position and evoked compound action potentials (ECAPs). However, these systems still largely fail to integrate subjective aspects of pain alongside objective neural biomarkers.

The purpose of this scoping review is to evaluate the control signals and algorithms used by closed-loop SCS devices and identify directions for improving their efficacy.

Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews guidelines, the PubMed, SCOPUS, and Web of Science data bases were queried on December 14, 2024. Peer-reviewed studies written in English related to closed-loop SCS were included. The inclusion criteria were 1) SCS therapy for chronic pain, and 2) real-time modulation of stimulation parameters. The exclusion criteria included review studies, book chapters, conference proceedings, small animal studies, or works unrelated to chronic pain. Initially, 688 unique articles were identified. After screening by two independent reviewers, 28 articles met all the established criteria, encompassing 19 unique studies.

Three studies investigated subjective states, such as rating of pain, mood, and paresthesias; seven used objective features, including position and movement, and nine studies incorporated ECAP characteristics as a control signal. To our knowledge, no existing model has fully integrated both subjective and biophysical markers to inform closed-loop stimulation parameters.

A closed-loop SCS algorithm that incorporates subjective and objective features may hold potential to improve quality of life in patients with chronic pain. Combining these approaches in a temporally resolved manner, for example, integrating patient reports with continuous electrophysiologic information using a state space mathematical model, could allow more optimized and patient-specific SCS programming.