Machine learning on encephalographic activity may predict opioid analgesia.

ABSTRACT

BACKGROUND: Opioids are used for the treatment of pain. However, 30-50% of patients have insufficient effect to the opioid initially selected by the physician, and there is an urgent need for biomarkers to select responders to the most appropriate drug. Since opioids mediate their effect in the central nervous system, this study aimed to investigate if electroencephalography (EEG) during rest or pain before treatment could predict the analgesic response. METHODS: EEG from 62 channels was recorded in volunteers during rest and tonic pain (cold pressor test). Morphine (30 mg) or placebo was then administered, and the pain test repeated 60 min after. Washout period between drugs was 7 days. Based on pain ratings, subjects were stratified into responders and non-responders. Spectral analysis was performed on the EEG. Conventional statistics on group basis were used and, furthermore, the most discriminative EEG features were subjected to support vector machine classification to predict the response for the individual subjects. RESULTS: Conventional statistics on the frequency bands revealed no differences between responders and non-responders. On the individual basis, no differences between groups were found using resting EEG. However, EEG during cold pain was able to classify responders with an accuracy of 72% (p = 0.01) and the result was reproducible using baseline data from both study days. CONCLUSIONS: Machine learning based on EEG before treatment enabled separation between responders and non-responders. This study represents the first step towards the prediction of opioid analgesia based on EEG features prior to drug administration, and advocates for the use of machine learning in future studies.