Sabbagh David, Cartailler Jérôme, Touchard Cyril, Joachim Jona, Mebazaa Alexandre, Vallée Fabrice, Gayat Étienne, Gramfort Alexandre, Engemann Denis A
INSERM, Université de Paris, Paris, France.
Inria, CEA, Université Paris-Saclay, Palaiseau, France.
BJA Open. 2023 Jun 16;7:100145. doi: 10.1016/j.bjao.2023.100145. eCollection 2023 Sep.
Electroencephalography (EEG) is increasingly used for monitoring the depth of general anaesthesia, but EEG data from general anaesthesia monitoring are rarely reused for research. Here, we explored repurposing EEG monitoring from general anaesthesia for brain-age modelling using machine learning. We hypothesised that brain age estimated from EEG during general anaesthesia is associated with perioperative risk.
We reanalysed four-electrode EEGs of 323 patients under stable propofol or sevoflurane anaesthesia to study four EEG signatures (95% of EEG power <8-13 Hz) for age prediction: total power, alpha-band power (8-13 Hz), power spectrum, and spatial patterns in frequency bands. We constructed age-prediction models from EEGs of a healthy reference group (ASA 1 or 2) during propofol anaesthesia. Although all signatures were informative, state-of-the-art age-prediction performance was unlocked by parsing spatial patterns across electrodes along the entire power spectrum (mean absolute error=8.2 yr; =0.65).
Clinical exploration in ASA 1 or 2 patients revealed that brain age was positively correlated with intraoperative burst suppression, a risk factor for general anaesthesia complications. Surprisingly, brain age was negatively correlated with burst suppression in patients with higher ASA scores, suggesting hidden confounders. Secondary analyses revealed that age-related EEG signatures were specific to propofol anaesthesia, reflected by limited model generalisation to anaesthesia maintained with sevoflurane.
Although EEG from general anaesthesia may enable state-of-the-art age prediction, differences between anaesthetic drugs can impact the effectiveness and validity of brain-age models. To unleash the dormant potential of EEG monitoring for clinical research, larger datasets from heterogeneous populations with precisely documented drug dosage will be essential.
脑电图（EEG）越来越多地用于监测全身麻醉的深度，但来自全身麻醉监测的EEG数据很少被重新用于研究。在此，我们探索了将全身麻醉的EEG监测重新用于基于机器学习的脑龄建模。我们假设全身麻醉期间通过EEG估计的脑龄与围手术期风险相关。
我们重新分析了323例在丙泊酚或七氟醚稳定麻醉下患者的四电极EEG，以研究用于年龄预测的四种EEG特征（95%的EEG功率<8-13Hz）：总功率、α波段功率（8-13Hz）、功率谱和频段的空间模式。我们根据丙泊酚麻醉期间健康参照组（美国麻醉医师协会1或2级）的EEG构建年龄预测模型。尽管所有特征都提供了信息，但通过解析整个功率谱上电极间的空间模式，实现了最先进的年龄预测性能（平均绝对误差=8.2岁；=0.65）。
对美国麻醉医师协会1或2级患者的临床探索显示，脑龄与术中爆发抑制呈正相关，术中爆发抑制是全身麻醉并发症的一个风险因素。令人惊讶的是，美国麻醉医师协会评分较高的患者脑龄与爆发抑制呈负相关，这表明存在潜在的混杂因素。二次分析显示，与年龄相关的EEG特征对丙泊酚麻醉具有特异性，这体现在模型对七氟醚维持麻醉的泛化能力有限。
尽管全身麻醉的EEG可能实现最先进的年龄预测，但麻醉药物之间的差异会影响脑龄模型的有效性和准确性。为了释放EEG监测在临床研究中的潜在价值，来自异质人群且精确记录药物剂量的更大数据集至关重要。
