Abstract:Objective: To investigate the quantitative EEG (qEEG) features associated with poor prognosis in patients with hypertensive intracerebral hemorrhage (HICH). Methods: A prospective study was conducted, continuously enrolling 69 patients with HICH from the intensive care unit of the Department of Neurology, Kailuan General Hospital, between January 2017 and June 2023. Based on the modified Rankin score (mRS) at 30 days post-discharge, patients were divided into two groups: the good prognosis group (mRS ≤ 2) and the poor prognosis group (mRS 3-6). General patient data and qEEG monitoring parameters, including alpha, theta, delta, and beta wave power values in the left and right frontal (Fp1, Fp2), frontal (F3, F4), central (C3, C4), parietal (P3, P4), occipital (O1, O2), anterior temporal (F7, F8), mid-temporal (T3, T4), and posterior temporal (T5, T6) regions, were collected. Results: The mean age in the poor prognosis group was significantly higher than in the good prognosis group (P<0.05). The delta wave power in the Fp2, F4, C4, P4, F7-8, and T4 regions in the poor prognosis group was significantly higher than in the good prognosis group (P<0.05). The theta wave power in the Fp1-2, F3, C3, P4, O1, F7, and T3 regions in the poor prognosis group was significantly higher than in the good prognosis group (P<0.05). The overall slow wave index power, including delta and theta waves, in the Fp1-2, F3-4, C3-4, P3-4, O1, F7-8, T4, and whole-brain average slow wave index in the poor prognosis group was significantly higher than in the good prognosis group (P<0.05). Conclusion: Patients with poor prognosis following hypertensive intracerebral hemorrhage show significantly increased delta, theta, and whole-brain average slow wave index power values on qEEG.
[1] An SJ,Kim TJ,Yoon BW.Epidemiology,risk factors,and clinical features of intracerebral hemorrhage: an update[J].Stroke,2017,19(1): 3-10. [2] 陈亦豪.脑电图在神经重症病人中的应用及进展[J].中国微侵袭神经外科杂志,2018,23(10):475-478. [3] 徐斌,元小冬,张萍淑,等.定量脑电图指标对急性脑损伤患者意识状态的预测价值研究[J].实用心脑肺血管病杂志,2022,30(4):52-59. [4] Chen Y,Xu W,Wang L,et al.Transcranial Doppler combined with quantitative EEG brain function monitoring and outcome prediction in patients with severe acute intracerebral hemorrhage[J].Crit Care,2018,22(1):36. [5] Kaduka L,Muniu E,Mbui J,et al.Disability-adjusted life-years due to stroke in Kenya[J].Neuroepidemiology,2019,53(1-2):48-54. [6] Deresse B,Shaweno D.Epidemiology and in-hospital outcome of stroke in South Ethiopia[J].Neurol Sci,2015,355(1-2):138-142. [7] Brandon Foreman,Jan Claassen.Quantitative EEG for the detection of brain ischemia[J].2012,16(2):216. [8] Gollwitzer S,GroemerT,Rampp S,et al.Early Prediction of delayed cerebral ischemia in subarachnoid hemorrhage based on quantitative EEG: a prospective study in adults[J].Clin Neurophysiol,2015,126(8):1514-1523. [9] Balanca B,Dailler F,Boulogne eS,etal.Diagnostic accuracy of quantitative EEG to detect delayed cerebral ischemia after subarachnoid hemorrhage: a preliminary study[J].Clin Neurophysiol,2018,129(9): 1926-1936.