title: Early prediction of poor outcome in severe hemispheric stroke by EEG patterns and gradings.
abstract: OBJECTIVE
  To find out the EEG abnormal patterns in massive cerebral hemispheric infarction (MCHI) and their correlation with poor outcome, and to construct an EEG grading for predicting the outcome of MCHI patients.
  
  
  METHODS
  Between 2000 and 2010, 162 patients with MCHI who met the selection criterions were selected for this study. All the patients underwent EEG examinations within 3 days after stroke onset and repeated on day 2 and 3. We classified the EEG recordings into 9 patterns and anglicized the correlation between EEG patterns and outcome. Then according to the results of the correlation between EEG patterns and outcome we constructed an EEG grading for predicting the outcome of MCHI patients.
  
  
  RESULTS
  We revealed that patterns of dominant alpha without reactivity, RAWOD, burst-suppression, α/θ-coma, epileptiform activity (without burst-suppression), and generalized suppression were correlated to poor outcome. We further modified the Young grading according to the correlation between EEG patterns and outcome. We found that the modified grading was superior to existing EEG gradings in predicting the outcome of MCHI patients, and it could predict the outcome of MCHI more accurately.
  
  
  CONCLUSIONS
  MCHI is common in N-ICU (Neurology Intensive Care Unit). The EEG analysis would detect the degree of brain lesion during the ischemia within the acute stage after stroke onset. The EEG evaluation might assist the neurophysicians to predict outcome of patients and make decisions on the treatments.
authors.affiliation: Neurological Intensive Care Unit, Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China. suyingying@xwh.ccmu.edu.cn
authors.forename: Ying Ying
authors.initials: YY
authors.lastname: Su
authors.forename: Miao
authors.initials: M
authors.lastname: Wang
authors.forename: Wei Bi
authors.initials: WB
authors.lastname: Chen
authors.forename: Paul
authors.initials: P
authors.lastname: Fu
authors.forename: Qing-Lin
authors.initials: QL
authors.lastname: Yang
authors.forename: Hong-Liang
authors.initials: HL
authors.lastname: Li
authors.forename: Xiao-Mei
authors.initials: XM
authors.lastname: Wang
authors.forename: Lin
authors.initials: L
authors.lastname: Wang
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D058256:Brain Waves / Q000502:physiology*; D002544:Cerebral Infarction / Q000175:diagnosis* / Q000503:physiopathology*; D042241:Early Diagnosis; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D011379:Prognosis