Predictive Effect Of Quantitative Electroencephalography On Patients With Middle Cerebral Artery Infarction

ObejectiveTo observe the predictive value of quantitative electroencephalography (QEEG) on prognosis of patients with large middle cerebral artery infarction. To explore the accuracy and feasibility of the indicators such as amplitude integrated electroencephalography (aEEG), spectral edge frequence (SEF) and absolute energy (AE) in patients with large middle cerebral artery infarction. To assist the physician more accurate assessment of patients with large middle cerebral artery infarction.MethodsThe retrospective study was conducted in a large teaching hospital. Between December2007and April2011, patients with LMCAI were recruited. Ethical approval was obtained from the Hospital. Written informed consent was obtained from each patient or his/her immediate family.Inclusion criteriaThe inclusion criteria were subjects:(ⅰ) receiving the first assessment within72h of infarct onset;(ⅱ) aged≥18years;(ⅲ) with stroke of>50%of the MCA territory in early cerebral CT scan or conventional MRI studies (T1and T2)#;(ⅳ) receiving conservative medical treatment; and (ⅴ) not having a history of cerebral stroke. Exclusion criteriaThe exclusion criteria were:(ⅰ) aged<18years;(ⅱ) combining infarction of unmiddle cerebral artery, such as, brain stem infarction, occipital lobe infarction, and cerebellum infarction, bihemispheric infarction;(ⅲ) receiving anti-epileptic or sedative drugs24h before the start of monitoring;(ⅳ) apparent interference artifact.MeasurementsQEEG were recorded within the first72h of infarct onset. Simultaneously, the GCS, basic information, and some clinical information were obtained. All these assessments were completed by two neurologists who did not participate in this study separately.EEG and QEEG monitoring:A NicoletOne EEG monitor was used to record for at least30minutes. While recording, voice and pressing hyponychium were used to stimulate the patients. Silver-chloride EEG electrodes were applied according to the international EEG10-20classification. QEEG and conventional EEG were recorded from scalp Fp1-A1, C3-A1, T3-A1, P3-A1, O1-A1; Fp2-A2, C4-A2, T4-A2, P4-A2, O2-A2and affixed with Collodion adhesive. The raw EEG signal was first amplified, then narrowly filtered to attenuate electrical activity of less than2Hz and more than15Hz, minimizing artifacts from sources such as sweating, muscle activity, and environmental electrical interference. Additional processing included semilogarithmic amplitude presentation, rectification and smoothing. The aEEG tracing is viewed on a highly compressed time scale, historically at a rate of6cm/h. Thus, a full minute of EEG was represented by only a single millimeter of aEEG display. The aEEG signals include the upper and lower margins. And the continuous frequency information obtained through FFT, could be analyzed by combining data into specific bands according to frequency range. Finally, the data of QEEG were obtained. Clinical data collection:ID, name, sex, age, diagnosis, infarction location, SBP,DBP, HCY, receive thrombolytic treatment.Follow-upCerebral Performance Category score (CPCs) was obtained on the third month after clinical evaluation. CPC1,2were defined as favorable prognosis; CPC3,4,5were considered as poor prognosis. CPC1:good cerebral performance, such as, conscious, alert, able to work, might have mild psychological, deficit. CPC2: moderate cerebral disability, sufficient cerebral function for independent activities of daily life. Able to work in sheltered environment. CPC3:sever cerebral disability, conscious, dependent on others for daily support because of impaired brain function. Ranges from ambulatory state to severe dementia or paralysis. CPC4:coma or vegetative sate, any degree of coma without the presence of all brain death criteria. Unawareness, even if appears awake without interaction with environment, may have spontaneous eye opening and sleep/awake cycles. Cerebral unresponsiveness. CPC5: brain death, apnea, areflexia, EEG silence, etc.Statistical analysesThe continuous data were compared using the t test. The discrete data were analyzed by the χ2test or the Fisher exact text. The pearson χ2test were used to analyse the relationship of location and receiving thrombolytic between the group of death and survival, and the group of good outcome and bad outcome. The t test was used to analyse the relationship of aEEG, SEF95%, AE, GCS score between the group of death and survival,and the group of good outcome and bad outcome. Multivarite logistic regression was used to evaluated the progonostic value of age, SBP, HCY, aEEG, SEF95%, AE and GCS score. P<0.05was considered statistically significant. SPSS13.0was used as statistic software. ResultClinical information of all patients:207patients were admitted to our NICU for cerebral infarction. Of them,74patients were excluded. A total of133patients with LMCAI were included in short-term outcome study. And three months after clinical evaluation,6were withdrawn from the study.In the short-term outcome, between death and survive groups,there were significant differences of GCS (t=2.455, P=0.015)、SEF95%(t=3.458, P=0.001)、 AE of8(t=-2.826, P=0.006)、AE of0(t=-2.228, P=0.028).χ2test showed that the location of infarction and receiving thrombolytsis had no differences between two groups.We undertook logistic regression analyses. We chose outcome as a dependent variable and observed parameters as independent variables. The regression equation showed that the SEF95%, AE of theta and delta activity and Glasgow Coma Score (GCS) were put into the equation, and had statistical significance. We found that SEF95%, AE of theta activity, and the GCS had a significant relationship with the short-term prognosis.In the long-term outcome, between good prognosis and poor prognosis groups, the age(t=-2.949, P=0.004)、GCS (t=2.966, P=0.004)、the upper and lower margin of aEEG (t=2.355, P=0.020; t=2.612, P=0.010), SEF95%(t=2.753, P=0.007), AE of delta (t=-3.102, P=0.002), indicated significant differences. Pearsonx2test showed that the location of infarction and receiving thrombolytsis had no differences between two groups.We undertook logistic regression analyses. We chose outcome as a dependent variable and observed parameters as independent variables. The regression equation showed that the SEF95%and GCS, age, lower margin of aEEG were put into the equation, and had statistical significance. The SEF95%and GCS, age, lower margin of aEEG showed significant correlation with long-term outcome.Conclusion 1. Quantitative electroencephalography could be used to monitor and predict the short-and long-term outcome of large middle cerebral artery infarction patients.2. With the univariate analysis in patients with large middle cerebral artery infarction patients of the early prognosis, between death and survive groups,there were significant differences of GCS, SEF95%, and absolute energy of theta activity. With the lowere SEF95%、GCS and higherδ、θAE, patients were more likely to die.3. With the univariate analysis in patients with large middle cerebral artery infarction patients of the long prognosis, between good and poor outcome groups, the Student’s t-test of age, GCS, SEF95%, the upper and lower margin of aEEG, AE of delta indicated significant differences. With the lower GCS, SEF95%, the upper and lower margin of aEEG, and higher AE of delta and age, patients were more likely to have poor long-term outcome.4. We undertook logistic regression analyses. We chose outcome as a dependent variable and observed parameters as independent variables. The regression equation showed that SEF95%, AE of theta activity, and the GCS had a significant relationship with the short-term prognosis.5. We again undertook logistic regression analyses. We chose outcome as a dependent variable and observed parameters as independent variables. The regression equation showed that SEF95%and GCS, age, lower margin of aEEG showed significant correlation with long-term outcome.6. QEEG included several parameters that could provide information about amplitude and frequency. We should combine them with the GCS as well as image examination for predicting the prognosis. QEEG could be used to monitor and predict the short-and long-term outcome of LMCAI patients. However, additional research designed with many patients is needed to better define and guide its application in the NICU.