From Prediction to Monitoring: Toward a Translational Framework of Biomarkers in Spinal Cord Stimulation

Spinal cord stimulation (SCS) is an established therapy for chronic pain, yet treatment response remains highly variable and patient selection largely empirical. The identification of biomarkers with the potential to predict and monitor therapeutic response is therefore critical for advancing toward precision neuromodulation. This study provides a structured narrative synthesis of current evidence on biomarkers in SCS, focusing on their predictive and monitoring roles and their translational potential. Available studies were analysed across electrophysiological, neuroimaging, autonomic, and molecular domains and conceptually organized into predictive biomarkers-reflecting baseline biological states associated with treatment susceptibility-and monitoring biomarkers, capturing physiological and molecular adaptations following stimulation. Among predictive approaches, intraoperative electroencephalography (EEG) and resting-state functional magnetic resonance imaging (rs-fMRI) have shown promising but exploratory discriminative performance. However, EEG findings are derived from intraoperative settings, limiting their applicability to pre-implantation patient selection. In contrast, monitoring biomarkers-including heart rate variability, metabolic imaging, and immunological parameters-provide objective measures of treatment-induced changes but do not currently support predictive use. Molecular and genomic biomarkers, while mechanistically informative, remain exploratory and lack validated clinical utility. A central limitation of the field is the fragmentation of biomarker research, with most studies evaluating single modalities in isolation. To address this gap, we propose a translational framework integrating predictive and monitoring biomarkers through a two-stage model combining baseline stratification with longitudinal response assessment. Although biomarker research in SCS is rapidly evolving, its clinical application remains limited. The development of multimodal, validated biomarker strategies may support improved patient selection and more objective evaluation of treatment response, enabling a transition toward mechanism-based neuromodulation.