Please use this identifier to cite or link to this item: https://doi.org/10.1007/s12028-010-9495-2
Title: Using continuous electroencephalography in the management of delayed cerebral ischemia following subarachnoid hemorrhage
Authors: Rathakrishnan, R. 
Gotman, J.
Dubeau, F.
Angle, M.
Keywords: Continuous electroencephalography
Delayed cerebral ischemia
Milrinone
Quantitative electroencephalography
Subarachnoid hemorrhage
Vasospasm
Issue Date: Apr-2011
Citation: Rathakrishnan, R., Gotman, J., Dubeau, F., Angle, M. (2011-04). Using continuous electroencephalography in the management of delayed cerebral ischemia following subarachnoid hemorrhage. Neurocritical Care 14 (2) : 152-161. ScholarBank@NUS Repository. https://doi.org/10.1007/s12028-010-9495-2
Abstract: Background: Using clinical parameters to identify and monitor treatment response in patients with delayed cerebral ischemia (DCI) following subarachnoid hemorrhage is challenging. We sought to determine whether continuous electroencephalography (CEEG) aids the prediction of the clinical course and response to treatment of DCI. Methods: Patients deemed high-risk for DCI based on the modified Fisher scale were prospectively monitored. A novel CEEG parameter measuring relative alpha power and variability in the anterior brain quadrants termed composite alpha index (CAI) was graphically displayed. Predictions of the status of patients for the ensuing day were made by an independent reviewer, first using clinical data then repeated following the addition of CAI trends. These were compared to the actual clinical state. The reviewer was blinded to the presence and treatment of DCI. Patients with DCI were further studied by trending the daily mean alpha power against the modulation of treatment and clinical evolution. Results: Fifty-nine predictions were made in 12 patients (mean age 54.3 years, range 35-70; nine females) with Hunt-Hess grades ranging I-V. Sensitivity of predicting clinical deterioration with CEEG improved from 40 to 67% and clinical improvement from 8 to 50%. In three patients, CEEG was predictive greater than 24 h prior to clinical change. Tracking the daily mean alpha power accurately identified DCI recurrence and poor responders to first-line therapy at pre-clinical stages. Conclusion: CEEG is a useful non-invasive tool to supplement routine clinical parameters in the prediction of DCI. It can dynamically monitor the response to treatment and might aid pre-clinical management decisions. © 2010 Springer Science+Business Media, LLC.
Source Title: Neurocritical Care
URI: http://scholarbank.nus.edu.sg/handle/10635/125474
ISSN: 15416933
DOI: 10.1007/s12028-010-9495-2
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