量化脑电图对儿童重症监护病房非创伤性意识障碍患儿不良结局的预测价值

Predictive value of quantitative electroencephalogram in the poor outcome of children with non-traumatic disturbance of consciousness in pediatric intensive care unit

目的探讨量化脑电图(qEEG)对儿童重症监护病房非创伤性意识障碍患儿不良结局的预测价值。方法前瞻性研究。研究对象为2019年1至5月蚌埠医学院第一附属医院儿童重症监护病房收治的1月龄至11岁的62例非创伤性意识障碍患儿。入院24 h内NicoletOne脑功能仪床旁监测,记录qEEG参数,包括振幅整合脑电图(aEEG)、相对α变异(RAV)、相对频带能量(RBP)和光谱熵(SE),监测前完成改良儿童格拉斯哥昏迷评分(MPGCS)。以儿童脑功能分类量表为出院后1年结局判断依据,将患儿分为结局良好组和结局不良组。变量与不良结局的相关性采用单因素和多因素Logistic回归分析,变量对不良结局的预测效能采用受试者工作特征曲线分析。结果62例患儿中男39例、女23例,年龄12.0(5.8,24.0)月龄,结局良好组50例(81%),结局不良组12例(19%)。单因素Logistic回归分析显示年龄(OR=1.037,95%CI 1.001~1.074,P=0.041)、重度异常aEEG(OR=128.000,95%CI 10.274~1594.656,P<0.01)、RAV(OR=0.877,95%CI 0.810~0.949,P=0.001)、SE(OR=0.892,95%CI 0.814~0.978,P=0.015)和MPGCS(OR=0.511,95%CI 0.349~0.747,P=0.001)与不良结局显著相关,多因素Logistic回归分析显示仅有重度异常aEEG(OR=315.692,95%CI 6.091~16362.298,P=0.004)和RAV(OR=0.808,95%CI 0.664~0.983,P=0.033)与不良结局显著相关。aEEG和RAV预测不良结局的曲线下面积(AUC)分别为0.848(95%CI 0.735~0.927,P<0.01)和0.847(95%CI 0.733~0.926,P<0.01),最佳截断值分别为重度异常和38%,灵敏度分别为67%和83%,特异度分别为98%和84%,阳性预测值分别为89%和55%,阴性预测值分别为92%和95%,约登指数分别为0.647和0.673。aEEG+RAV联合指标预测不良结局的AUC增加至0.974(95%CI 0.898~0.998,P<0.01)。结论aEEG和RAV是非创伤性意识障碍患儿不良结局可靠的预测因子,aEEG+RAV联合指标可提高预测效能,qEEG客观性好,可作为结局评估的常规方法。

Objective To explore the predictive value of quantitative electroencephalogram(qEEG)in the poor outcome of children with non-traumatic disturbance of consciousness(DoC)in the pediatric intensive care unit(PICU).Methods A prospective study was conducted.From January 2019 to May 2019,a total of 62 patients aged from 1 month to 11 years with non-traumatic DoC in the PICU of the First Affiliated Hospital of Bengbu Medical College were enrolled.Bedside monitoring with NicoletOne monitor was performed within 24 hours after admission,and qEEG parameters,including amplitude-integrated electroencephalogram(aEEG),relative alpha variability(RAV),relative band power(RBP),and spectral entropy(SE)were recorded.The state of consciousness was assessed with modified pediatric Glasgow coma scale(MPGCS)before monitoring.According to the pediatric cerebral performance category score at 1 year after discharge,the enrolled subjects were divided into good and poor outcome groups.The association between these variables and the poor outcome was analyzed by univariate and multivariate logistic regression analysis,and the predictive performance was analyzed by receiver operator characteristic(ROC)curve.Results There were 39 males and 23 females,with the age of 12.0(5.8,24.0)months.Fifty patients(81%)were in the good outcome group and 12 patients(19%)in the poor outcome group.The univariate Logistic regression analysis showed that age(OR=1.037,95%CI 1.001-1.074,P=0.041),severe abnormal aEEG(OR=128.000,95%CI 10.274-1594.656,P<0.01),RAV(OR=0.877,95%CI 0.810-0.949,P=0.001),SE(OR=0.892,95%CI 0.814-0.978,P=0.015),and MPGCS score(OR=0.511,95%CI 0.349-0.747,P=0.001)were significantly associated with the poor outcome.However,the multivariate Logistic regression analysis showed that only severe abnormal aEEG(OR=315.692,95%CI 6.091-16362.298,P=0.004)and RAV(OR=0.808,95%CI 0.664-0.983,P=0.033)were significantly associated with the poor outcome.The area under the curve(AUC)of the aEEG and RAV in predicting the poor outcome were 0.848(95%CI 0.735-0.927,P<0.01)and 0.847(95%CI 0.733-0.926,P<0.01),respectively.The optimal cut-off value was severe abnormal for the aEEG and 38%for the RAV,with sensitivity of 67%and 83%,specificity of 98%and 84%,positive predictive value of 89%and 55%,negative predictive value of 92%and 95%,and Youden index of 0.647 and 0.673,respectively.The AUC of the novel combined index of aEEG and RAV for predicting the poor outcome was 0.974(95%CI 0.898-0.998,P<0.01).Conclusions The aEEG and RAV are reliable predictors for the poor outcome of children with non-traumatic DoC,and the novel combined index of aEEG and RAV can improve the predictive performance.The qEEG can be used as a routine method for outcome assessment due to its good objectivity.