Department of Intensive Care Medicine and University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands; Department of Psychiatry and University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.
Department of Intensive Care Medicine and University Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.
Clin Neurophysiol. 2024 May;161:93-100. doi: 10.1016/j.clinph.2024.01.009. Epub 2024 Feb 20.
This exploratory study examined quantitative electroencephalography (qEEG) changes in delirium and the use of qEEG features to distinguish postoperative from non-postoperative delirium.
This project was part of the DeltaStudy, a cross-sectional,multicenterstudy in Intensive Care Units (ICUs) and non-ICU wards. Single-channel (Fp2-Pz) four-minutes resting-state EEG was analyzed in 456 patients. After calculating 98 qEEG features per epoch, random forest (RF) classification was used to analyze qEEG changes in delirium and to test whether postoperative and non-postoperative delirium could be distinguished.
An area under the receiver operatingcharacteristic curve (AUC) of 0.76 (95% Confidence Interval (CI) 0.71-0.80) was found when classifying delirium with a sensitivity of 0.77 and a specificity of 0.63 at the optimal operating point. The classification of postoperative versus non-postoperative delirium resulted in an AUC of 0.50 (95%CI 0.38-0.61).
RF classification was able to discriminate delirium from no delirium with reasonable accuracy, while also identifying new delirium qEEG markers like autocorrelation and theta peak frequency. RF classification could not distinguish postoperative from non-postoperative delirium.
Single-channel EEG differentiates between delirium and no delirium with reasonable accuracy. We found no distinct EEG profile for postoperative delirium, which may suggest that delirium is one entity, whether it develops postoperatively or not.
本探索性研究旨在检查谵妄的定量脑电图（qEEG）变化，并利用 qEEG 特征来区分术后和非术后谵妄。
该项目是 DeltaStudy 的一部分，这是一项在重症监护病房（ICU）和非 ICU 病房进行的横断面、多中心研究。对 456 名患者进行了单通道（Fp2-Pz）四分钟静息状态 EEG 分析。在计算每个时段 98 个 qEEG 特征后，使用随机森林（RF）分类分析谵妄的 qEEG 变化，并测试术后和非术后谵妄是否可以区分。
当使用 RF 分类以最佳工作点时，以 0.77 的灵敏度和 0.63 的特异性对谵妄进行分类时，发现受试者工作特征曲线下面积（AUC）为 0.76（95%置信区间 0.71-0.80）。术后与非术后谵妄的分类导致 AUC 为 0.50（95%CI 0.38-0.61）。
RF 分类能够以合理的准确性区分谵妄与非谵妄，同时还能识别新的谵妄 qEEG 标志物，如自相关和 theta 峰频率。RF 分类无法区分术后和非术后谵妄。
单通道 EEG 以合理的准确性区分谵妄与非谵妄。我们没有发现术后谵妄的明显 EEG 特征，这可能表明谵妄是一种实体，无论是否在术后发生。
