睡眠联合脑电监测对重症患者意识状态改变的预报价值

Predictive value of combined sleep EEG monitoring for changes in conscious state in severe patients

目的探讨睡眠联合脑电监测对重症患者意识状态改变的预报价值。方法连续收集2020年11月至2021年12月在开滦总医院神经重症加强治疗病房诊治的不同意识状态重症患者81例,将不同程度意识障碍患者分为清醒组19例、嗜睡组48例和昏迷组14例。记录患者的一般资料、非直接接触式无感睡眠监测系统数据、定量脑电监测数据,采用logistic、多元逐步回归分析筛选出影响意识状态转变的相关指标。结果3组患者收缩期血压、REM期睡眠时间和REM期占比差异有统计学意义(P<0.05)。以清醒与嗜睡为因变量的logistic分析主要筛选出F4-δ、F4-慢波指数、F8-δ、F8-θ、F8-(δ+θ)左/右、F8-α指数、F8-慢波指数、T3-α3、T4-α左/右、T5-(δ+θ)左/右等指标,P均<0.05。以嗜睡与昏迷为因变量主要筛选出F4-δ、F4-α2、F4-α/(δ+θ)、F4-α指数、F4-慢波指数、F4-δ指数、F4-θ指数、C4-δ指数、F8-δ、F8-θ、F8-α2、F8-α指数、F8-慢波指数、F8-δ指数、F8-θ指数、T4-δ、T4-θ、T4-α2、T4-α3、T4-α左/右、T4-(δ+θ)左/右、T4-α指数、T4-慢波指数、T4-δ指数等指标,P均<0.05。分别以上述筛选出的指标为因变量的多元逐步回归分析显示心率变异指数、睡眠期平均心率、总睡眠时间、心血管健康指数、呼吸期最快呼吸、睡眠期平均呼吸等睡眠监测指标是这些高危因素的主要交互影响因素。结论在脑电指标中F4-慢波指数、F8-慢波指数、T4-α左/右是清醒重症患者发生嗜睡的高危因素,F4-α/(δ+θ)、F4-慢波指数、T4-δ、T4-θ、T4-(δ+θ)左/右、T4-慢波指数是嗜睡患者发生昏迷的高危因素。REM期睡眠与意识状态改变有明显的关系。通过非直接接触式睡眠监测技术与脑电生理学监测相结合,可以更好地预测患者的意识状态改变。

Objective To explore the predictive value of combined sleep EEG monitoring for the change of consciousness state in severe patients.Methods A total of 81 severe patients with different consciousness states admitted to the intensive neurological treatment unit in Kailuan General Hospital from November 2020 to December 2021 were collected.Patients with different degree of consciousness disorder were divided into the awake group(19 cases),sleepiness group(48 cases)and coma group(14 cases).General data,non-contact sensory sleep monitoring system data and quantitative EEG monitoring data were recorded.Logistic and multiple stepwise regression analysis were used to screen out relevant indicators affecting the transformation of consciousness state.Results Systolic blood pressure,REM sleep time and proportion of REM were significantly different among the 3 groups(P<0.05).Logistic analysis with wakefulness and sleepiness as dependent variables mainly screened out F4-δ,F4-slow wave index,F8-δ,F8-θ,F8-(δ+θ)left/right,F8-αindex,F8-slow wave index,T3-α3,T4-αleft/right,T5-(δ+θ)left/right indexes,all P<0.05.Logistic regression analysis with sleepiness and coma as the dependent variables mainly screened out F4-δ,F4-α2,F4-α/(δ+θ),F4-αindex,F4 slow wave index,F4-δindex,F4-θindex,C4-δindex,F8-δ,F8-θ,F8-α2,F8-αindex,F8 slow wave index,F8-δindex,F8-θindex,T4-δ,T4-θ,T4-α2,T4-α3,T4-αleft/right,T4-(δ+θ)left/right,T4-αindex,T4 slow wave index,T4-δindex,all P<0.05.The multiple stepwise regression analysis with the above screened indicators as dependent variables showed that the sleep monitoring indicators such as heart rate variation index,average heart rate during sleep period,total sleep time,cardiovascular health index,fastest breathing during breathing period,average breathing during sleep period were the main interactive influencing factors of these high-risk factors.Conclusions Among the EEG indicators,F4-slow wave index,F8-slow wave index and T4-αleft/right were the high risk factors for developing drowsiness in conscious critically ill patients,and F4-α/(δ+θ),F4-slow wave index,T4-δ,T4-θ,T4-(δ+θ)left/right and T4-slow wave index were the high risk factors for developing coma in patients with sleepiness.There is a significant relationship between REM sleep and changes in consciousness state.The combination of non-contact sensory sleep monitoring system and brain electrophysiology monitoring can better predict the change of patients'consciousness state.