Dr Wayne Loudon1, Associate Professor Andrew Wong2, Professor Emma Bosley1, Mr Mark Disney1, Dr Daniel Bodnar1, Dr Judith Bellapart5, Professor Vivienne Tippett3, Dr Frances Williamson4, Professor Craig Winter6, Dr Matteo Masceloni7, Dr Denise Bunting1, Dr Stephen Rashford1
1Queensland Ambulance Service, Brisbane, Australia, 2Department of Neurology, Royal Brisbane and Womens Hospital, Brisbane, Australia, 3Queensland University of Technology, Brisbane, Australia, 4Department of Emergency Medicine, Royal Brisbane and Womens Hospital, Brisbane, Australia, 5Intensive Care Service, Royal Brisbane and Womens Hospital, Brisbane, Australia, 6Department of Neurosurgery, Royal Brisbane and Womens Hospital, Brisbane, Australia, 7University of Sydney, Sydney, Australia
Biography:
Wayne Loudon is an experienced critical care paramedic and holds a PhD with doctoral research in paramedicine. His work focuses on acute stroke and traumatic brain injury, leading novel prehospital studies. He serves on several committees advancing evidence based stroke systems of care across research, education, and clinical practice.
Abstract:
Early assessment of traumatic brain injury (TBI) in the prehospital environment remains heavily reliant on indirect clinical indicators, despite the critical importance of timely identification of cerebral dysfunction. This presentation describes the practical implementation and evaluation of prehospital quantitative electroencephalography (qEEG) as a novel monitoring tool to support clinical decision making in high acuity ambulance care.
A portable EEG system was embedded into routine operations within a specialist prehospital response model, using a simplified electrode montage. EEG acquisition was integrated into existing workflows without delaying patient care, demonstrating feasibility and clinician acceptability. Post hoc Quantitative EEG metrics were analysed against established indicators of injury severity, including CT based structural classification.
EEG data acquisition was successful in all cases, with interpretable signals obtained during ambulance care and transport. Key qEEG metrics showed biologically plausible associations with injury severity, providing objective insight into cerebral dysfunction well before hospital imaging. Spearman correlation analysis demonstrated significant associations between qEEG metrics and structural injury severity. Relative delta power showed a strong positive correlation with Marshall CT classification (ρ = 0.83, p = 0.005, pFDR = 0.032), while the high alpha-to-delta ratio demonstrated a strong inverse correlation (ρ = −0.86, p = 0.003, pFDR = 0.032).
This work demonstrates successful acquisition of neurophysiological data in the out of hospital setting during emergency care. Further investigation may lead to development of mobile, real-time neurophysiologic monitoring that could guide clinical management.