Predicting Post-operative Pain in Lung Cancer Patients using Pre-operative Peak Alpha Frequency

Abstract

Aims and ObjectivesExperimental models of neuropathic pain suggest that individual peak alpha frequency (PAF), measured using electroencephalography (EEG), can predict future pain sensitivity in experimental settings. Here, we tested whether PAF could predict future pain severity in a clinical setting in patients undergoing thoracotomy.MethodsRecorded using wearable around the ear electrodes (cEEGrids), the feasibility and efficacy of pre-operative PAF as a neuro-marker for post-operative pain was assessed in 16 patients undergoing thoracic surgery for lung cancer (age = 67.53 ± 4.38 [SD]). Patients also provided numerical ratings (0-10) of current, average, and worst pain pre-operatively as well as within three days post-operativelyResults and SignificancePre-operative PAF of less than 9 Hz was highly sensitive (1.0) and specific (0.86) in identifying patients who would go on to experience severe (>7/10) worst pain. Moreover, PAF was negatively correlated with patients’ current, average, and worst post-operative pain. PAF was significantly higher for those reporting lower pain severity compared to those reporting higher pain severity in the immediate post-operative period. This suggests that PAF is a promising neuro-marker to pre-operatively assess individual susceptibility to severe pain in the immediate post-operative period, possibly enabling more informed assessment of an individual’s suitability for surgery.

A novel cortical biomarker signature predicts individual pain sensitivity

“…Nonetheless, the mixed findings could also arise from differences in methodological choices in the estimation of PAF e.g. frequency windows [39] and use of sensor vs. component space data [40] and estimation of CME e.g. map volume [21] vs. area [33].…”

The reliability of two prospective cortical biomarkers for pain: EEG peak alpha frequency and TMS corticomotor excitability

“…Recent work has shown promise for a cortical biomarker pain signature involving two measures: peak alpha frequency (PAF), which is measured using resting-state electroencephalography (EEG) and refers to the dominant oscillatory frequency in the 8-12Hz range [4-6] and; corticomotor excitability (CME), which is measured using transcranial magnetic stimulation (TMS), and can be indexed as the volume of the primary motor cortex (M1) representation of a peripheral muscle [7]. Studies using pain models in healthy individuals have shown that PAF and CME can predict pain severity and duration [8-12]. One of these studies used intramuscular injections of Nerve Growth Factor (NGF), which produces progressively developing and clinically meaningful pain sustained over several days.…”