ID: 199213 Artificial Intelligence-Driven Closed-Loop SCS: Optimizing Patient Outcomes Using a Comprehensive Measure of Well-being

Introduction: Closing the loop involves using sensed signals from the patient to provide feedback to modify the system output to reach a desired therapy goal. Some chronic diseases, like diabetes, have clear biomarkers and normal ranges that can be applied across the population (e.g., HbA1c, blood glucose). However, in chronic pain, the challenge in closing the loop is much more complicated; i.e., there are many signals that could be used to the close the loop, optimal values may be patient specific, and a single domain may not always adequately capture effectiveness. We propose an approach where signals used to close the loop in patients with chronic pain are related to the therapeutic goals, namely improvement in pain and/or quality of life (QOL). We evaluate this approach in early pilots of chronic pain patients with Spinal Cords Stimulation (SCS).