Quantitative electroencephalography predicts postoperative delirium in cardiac surgical patients after...

Despite its frequency and associated negative effect, delirium remains poorly recognized in postoperative patients after ICU admission, especially among those who have undergone cardiac surgery with cardiopulmonary bypass. Postoperative delirium is triggered by a wide variety of acute medical conditions associated with impaired neuronal network connectivity. The lack of objective biomarkers primarily hinders the early detection of delirium. Seeking early biomarkers for tracking POD could potentially assist in predicting the onset of delirium and assessing the severity of delirium and response to interventions.

QEEGs were taken from 46 sedated postoperative patients, with 24 of them having undergone cardiac surgery. The assessment of delirium was performed twice daily using the Confusion Assessment Method for the ICU (CAM-ICU) to screen for postoperative delirium (POD). QEEG data were interpreted clinically by neurophysiologists and processed by open-source EEGLAB to identify features in patients who had or did not have POD after cardiac or non-cardiac surgery.

The incidence of delirium in patients after undergoing cardiac surgery was nine times greater than in those after non-cardiac surgeries (41.7% vs. 4.5%; p = 0.0046). Patients with delirium experienced longer use of mechanical ventilation (118 h (78,323) compared to 20 h (18,23); p < 0.0001) and an extended ICU length of stay (7 days (6, 20) vs. 2 days (2, 4); p < 0.0001). The depth of anesthesia, as measured by RASS scores (p = 0.3114) and spectral entropy (p = 0.1504), showed no significant difference. However, notable differences were observed between delirious and non-delirious patients in terms of the amplitude-integrated EEG (aEEG) upper limit, the relative power of the delta band, and spectral edge frequency 95 (SEF95) (p = 0.0464, p = 0.0417, p = 0.0337, respectively).

In a homogenous population of sedated postoperative patients, robust qEEG parameters strongly correlate with delirium and could serve as valuable biomarkers for early detection of delirium and assist in clinical decision-making.