Amanda A Vatinno1, Annie Simpson1,2, Viswanathan Ramakrishnan3, Heather S Bonilha1, Leonardo Bonilha4, Na Jin Seo5,6,7. 1. Department of Health Sciences and Research, College of Health Professions, 2345Medical University of South Carolina (MUSC), Charleston, SC, USA. 2. Department of Healthcare Leadership and Management, College of Health Professions, 2345MUSC, Charleston, SC, USA. 3. Department of Public Health Sciences, College of Medicine, 2345MUSC, Charleston, SC, USA. 4. Department of Neurology, College of Medicine, 2345MUSC, Charleston, SC, USA. 5. Ralph H. Johnson VA Medical Center, Charleston, SC, USA. 6. Department of Health Sciences and Research, 2345MUSC, Charleston, SC, USA. 7. Division of Occupational Therapy, Department of Rehabilitation Sciences, MUSC, Charleston, SC, USA.
Abstract
BACKGROUND: Improved ability to predict patient recovery would guide post-stroke care by helping clinicians personalize treatment and maximize outcomes. Electroencephalography (EEG) provides a direct measure of the functional neuroelectric activity in the brain that forms the basis for neuroplasticity and recovery, and thus may increase prognostic ability. OBJECTIVE: To examine evidence for the prognostic utility of EEG in stroke recovery via systematic review/meta-analysis. METHODS: Peer-reviewed journal articles that examined the relationship between EEG and subsequent clinical outcome(s) in stroke were searched using electronic databases. Two independent researchers extracted data for synthesis. Linear meta-regressions were performed across subsets of papers with common outcome measures to quantify the association between EEG and outcome. RESULTS: 75 papers were included. Association between EEG and clinical outcomes was seen not only early post-stroke, but more than 6 months post-stroke. The most studied prognostic potential of EEG was in predicting independence and stroke severity in the standard acute stroke care setting. The meta-analysis showed that EEG was associated with subsequent clinical outcomes measured by the Modified Rankin Scale, National Institutes of Health Stroke Scale, and Fugl-Meyer Upper Extremity Assessment (r = .72, .70, and .53 from 8, 13, and 12 papers, respectively). EEG improved prognostic abilities beyond prediction afforded by standard clinical assessments. However, the EEG variables examined were highly variable across studies and did not converge. CONCLUSIONS: EEG shows potential to predict post-stroke recovery outcomes. However, evidence is largely explorative, primarily due to the lack of a definitive set of EEG measures to be used for prognosis.
BACKGROUND: Improved ability to predict patient recovery would guide post-stroke care by helping clinicians personalize treatment and maximize outcomes. Electroencephalography (EEG) provides a direct measure of the functional neuroelectric activity in the brain that forms the basis for neuroplasticity and recovery, and thus may increase prognostic ability. OBJECTIVE: To examine evidence for the prognostic utility of EEG in stroke recovery via systematic review/meta-analysis. METHODS: Peer-reviewed journal articles that examined the relationship between EEG and subsequent clinical outcome(s) in stroke were searched using electronic databases. Two independent researchers extracted data for synthesis. Linear meta-regressions were performed across subsets of papers with common outcome measures to quantify the association between EEG and outcome. RESULTS: 75 papers were included. Association between EEG and clinical outcomes was seen not only early post-stroke, but more than 6 months post-stroke. The most studied prognostic potential of EEG was in predicting independence and stroke severity in the standard acute stroke care setting. The meta-analysis showed that EEG was associated with subsequent clinical outcomes measured by the Modified Rankin Scale, National Institutes of Health Stroke Scale, and Fugl-Meyer Upper Extremity Assessment (r = .72, .70, and .53 from 8, 13, and 12 papers, respectively). EEG improved prognostic abilities beyond prediction afforded by standard clinical assessments. However, the EEG variables examined were highly variable across studies and did not converge. CONCLUSIONS: EEG shows potential to predict post-stroke recovery outcomes. However, evidence is largely explorative, primarily due to the lack of a definitive set of EEG measures to be used for prognosis.
Authors: Ravikiran Mane; Effie Chew; Kok Soon Phua; Kai Keng Ang; Neethu Robinson; A P Vinod; Cuntai Guan Journal: IEEE Trans Neural Syst Rehabil Eng Date: 2019-06-24 Impact factor: 3.802
Authors: Kartik K Iyer; Anthony J Angwin; Sophia Van Hees; Katie L McMahon; Michael Breakspear; David A Copland Journal: Cortex Date: 2019-12-30 Impact factor: 4.027