Electroencephalographic monitoring in detection and prediction of delayed cerebral ischemia due to non-traumatic subarachnoid hemorrhage - I. V. Okuneva; M. V. Sinkin; A. I. Evdokimov; A. V. Prirodov (Эпилепсия и пароксизмальные состояния, 2024)
Intensive care of patients with acute non-traumatic subarachnoid hemorrhage primarily relies on diagnostics of delayed cerebral ischemia (DCI). The major difficulty in detecting DCI emerges upon suppression of wakefulness, when clinical assessment of growing neurological deficit becomes complicated. Widely used transcranial dopplerography allows solely to verify a vasospasm development not always leading to DCI exhibiting a multifactorial underlying mechanism. Electroencephalography (EEG) is the only broadly available instrumental tool ensuring a continuous monitoring of cerebral functional status including in subjects at intensive care unit. To date, non-specific EEG parameters pointing at development of acute cerebral injury were identified that provide varying diagnostic and predictive informative value in DCI. We reviewed publications aimed at assessing the data on visual and quantitative EEG parameters such as regional slowing, alpha rhythm spectral power and relative variability, alpha-to-delta power ratio, and detection of epileptiform activity. Having searched international and Russia-wide medical databases, we found only 7 publications quantitatively assessing diagnostic value of EEG monitoring, which showed that for DCI diagnosis its sensitivity ranged from 76% to 100%, and specificity – from 54% to 100%. We also present a clinical case with a 70-year-old female patient who underwent surgery for non-traumatic subarachnoid hemorrhage due to a ruptured aneurysm of the communicating segment of the right internal carotid artery. During the continuous videoEEG monitoring 2 days before clinical deterioration and appearance of ischemic changes in the right cerebral hemisphere on computed tomography scans, an ictal-interictal continuum pattern was noted to emerge. Future studies should be aimed at clarifying and validating the most informative DCI biomarkers including while recording EEG with intracranial electrodes that may contribute to development of automated algorithms for DCI detection.