Monitoring Neurological Deterioration in Anaesthetised Patients With Electroencephalogram (EEG)

CVA

The incidence of perioperative stroke in the non-cardiac, non-vascular, non-neurological high risk surgical population is 2%. It is higher (\ 5%) for cardiac surgery and carotid endarterectomy patients, with a stroke associated mortality of up to 60%. These patients could be immediately treated if their condition was detected on time. Currently, there is no standard brain monitoring procedure for anaesthetised patients. The purpose of the proposed study is to optimize and validate an online monitor for neurological deterioration under anaesthesia based on an easily operatable EEG system comprised of 4 electrodes, a reference electrode and a newely developed algorithm for analyzing the EEG signal. The monitor aims at generating an immediate warning at the onset of neurological deterioration. For the purpose of technology development with a minimal sample size, it is necessary to select a patient population that demonstrates significant neurological dynamics under anaesthesia. We will therefore focus on anesthesized patients undergoing neurovascular thrombectomy after CVA.

The research aim is to validate a novel ground-breaking and easy-to-use EEG-based algorithm for sensing cerebrovascular accident (CVA) events under anaesthesia. Today there is no standard tool for CVA detection in anaesthetized patients. Thus the possibility of waking up from anaesthesia with severe brain damage is devastating. Patients undergoing cardiac surgery (such as valve replacement, CABG, thoracic aorta replacement, PTCA, TAVI (trans-aortic valve implantation)), carotid endarterectomy and surgery in sitting position, are at exceptionally high risk. We have developed a unique algorithm for detecting brain ischemia in anaesthetized patients, based on data acquired from 4 EEG electrodes. Our final goal is to develop a system that triggers an alarm when an ischemia occurs in an anaesthetized patient and during the peri-operative period. In order to validate and optimize our system, we require a small sample size to test and validate the technology. The population for this study includes patients with acute CVA who undergo mechanical thrombectomy under anaesthesia. Although these patients already have CVA, clinical dynamics is high in this population, and will enable a demonstration of our ability to recognize immediate changes in neurological status (improvement or deterioration). We believe this innovative EEG based system could be of major significance to millions of patients annually.

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