Despite progress in pain management, breakthrough pain, defined as sudden, severe episodes that occur despite ongoing treatment, remains a persistent challenge. Current assessment relies on subjective self-report scales that fail patients who are unable to communicate and cannot provide the continuous monitoring needed for episodes that peak within 5-30 min. Objective, real-time monitoring tools could improve pain assessment and management. This narrative review examines electrodermal activity, a noninvasive measure of sympathetic nervous system activation, as a potential objective biomarker for breakthrough pain. Evidence from experimental studies demonstrates that EDA responds proportionally to pain intensity and can differentiate painful stimuli from emotional or sensory confounders. Clinical studies using wearable devices show that machine learning models combining EDA with heart rate variability achieve over 80% accuracy in detecting pain levels and predicting analgesic use. However, clinical implementation faces challenges: inter-individual variability, autonomic changes in chronic pain, motion artifacts, and confounding variables from medications and emotional states. Clinical trials examining patient outcomes, rather than detection accuracy alone, are necessary to establish clinical value and integrate it in breakthrough pain management.
Keywords: breakthrough pain; electrodermal activity; machine learning; pain assessment; wearable devices.
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