Early prediction of malignant midge cerebral artery infarction with bedside electroencephalography / 国际脑血管病杂志

ABSTRACT

Objective To investigate the possibility and accuracy of predicting malignant middle cerebral artery infarction (mMCAI) with bedside electroencephalography (EEG). Methods Thirty-five patients with massive hemispheric infarction (MHI) underwent bedside EEG monitoring within 48 h of onset. The EEG indicators were interpreted blindly, and the clinical, laboratory and imaging parameters were analyzed. The patients were divided into mMCAI group and non-mMCAI group according to whether they had occurred mMCAI or not within 7 days of onset. The differences of EEG indicators, clinical, laboratory and imaging parameters between the 2 groups were compared. When the parameters of significant difference and statistical significance appeared the odds ratio (OR) of occurring mMCAI were analyzed, and their accuracy of predicting mMCAI was calculated. Results Of the 35 patients with MHI, 20 were in the mMCAI group and 15 were in the non-mMCAI group. There were significant differences in the EEG indicators (infarction on the contralateral side, including disintegration of occipital α rhythm, generalized slow-wave, dominant frequency wave low amplitude, regional attenuation without delta [RAWOD]pattern, and absence of EEG reactivity), clinical parameters (nausea accompanied with vomiting), and imaging parameters (the infracted area more than the entire MCA territory, and midline shifting 3 to 5 mm at the level of septum pellucidum) between the 2 groups (P < 0. 05). Of those, the risk of mMCAI was the highest in patients with disintegration of occipital a rhythm on the contralateral side of infarction (P = 22. 67, 95% CI 3. 89-132. 10). The sensitivity of predicting mMCAI was 85. 0%, the specificity was 80.0%, the positive predictive value was 85.0%, and the negative predictive value was 80. 0%, which were superior to other EEG indicators and clinical or imaging parameters. Conclusions Bedside EEG indicators can early predict mMCAI, moreover, the predictive accuracy is superior to the clinical and imaging parameters.