Predicting post-operative pain in lung cancer patients using pre-operative peak alpha frequency

Samantha K. Millard, Andrew J Furman, Amy kerr, David Seminowicz, Babu Naidu, Fang Gao Smith, Ali Mazaheri

Research output: Contribution to journalLetterpeer-review

Abstract

Aims and Objectives Experimental 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.

Methods Recorded 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-operatively

Results and Significance Pre-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.
Original languageEnglish
JournalBritish Journal of Anaesthesia
Early online date4 Apr 2022
DOIs
Publication statusE-pub ahead of print - 4 Apr 2022

Keywords

  • alpha oscillations
  • EEG
  • monitor
  • pain
  • postoperative pain
  • thoracic surgery

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