The Prognostic Utility of Electroencephalography in Stroke Recovery: A Systematic Review and Meta-Analysis. (April 2022)

• Record Type: Journal Article
• Authors: Vatinno, Amanda A; Simpson, Annie; Ramakrishnan, Viswanathan; Bonilha, Heather S.; Bonilha, Leonardo; Seo, Na Jin
• Is Part Of: Neurorehabilitation & neural repair. Volume 36:Number 4/5(2022)
• Page Start: 255
• Page End: 268
• Publication Date: 2022-04
• Subjects: stroke -- EEG -- rehabilitation -- prognosis -- meta-analysis; Nervous system -- Diseases -- Patients -- Rehabilitation -- Periodicals; Brain damage -- Patients -- Rehabilitation -- Periodicals; Spinal cord -- Wounds and injuries -- Patients -- Rehabilitation -- Periodicals; Nervous system -- Regeneration -- Periodicals; Neuroplasticity -- Periodicals; 616.804305
• DOI: 10.1177/15459683221078294
• Languages: English
• ISSNs: 1545-9683

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.