Prediction of Spinal Pain Intensity from Perception-Dependent Stimulation Thresholds and Patient Characteristics: A Regression and Machine Learning Approach

Background and Objective: Spinal pain is highly prevalent, yet clinically accessible tools for pain quantification remain limited. This study evaluated whether machine learning (ML) algorithms using StimaWELL 120MTRS-derived scores alongside demographic variables can predict self-reported spinal pain intensity and compared their performance against linear mixed model (LMM)-based associations.

Methods

Seventy-eight adults with spinal pain (44 female, 34 male; mean age 43.08 ± 11.50 years) were assessed under two electrode configurations: opposing electrode (OE) and adjacent electrode (AE). Self-reported pain was measured using the Numeric Pain Rating Scale (NPRS). Repeated measures correlation, intraclass correlation coefficients (ICC), LMM, and eight ML algorithms were applied to evaluate predictive capacity.