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振幅整合脑电图在早产儿脑损伤早期诊断的研究

Application of Amplitutude Integrated Electroencephalography for Early Diagnosis of Brain Injury in Premature Infants

  • 摘要: 目的 研究脑损伤早产儿振幅整合脑电图(amplitude integrated electroencephalography,aEEG)的特点,为早产儿临床脑损伤提供早期诊断的依据。 方法 采用脑电图仪(日本光电工业株式会社QP-160AK)对62例胎龄28~36周脑损伤早产儿(脑损伤组)和51例无脑损伤早产儿(对照组)分别于生后48 h内进行监测,每次连续监测不低于2 h。对比分析脑损伤早产儿组与对照组aEEG背景连续性、睡眠觉醒周期(周期性)、窄带下界振幅特点。 结果 aEEG背景连续性、周期性和窄带下界电压脑损伤组均较对照组早产儿低(P均<0.05)。单因素logistic回归分析显示,仅aEEG周期性与脑损伤的发生相关(OR值为0.217,P<0.05);aEEG连续性、周期性和窄带下界电压与脑损伤诊断的相关操作特征(ROC)曲线下面积(AUC)分别为0.677、0.769、0.602,表明aEEG周期性预测脑损伤准确性更高;aEEG连续性、周期性及窄带下界电压及总评分与胎龄相关,相关系数分别为0.546、0.488、0.536、0.588(P均<0.05)。 结论 28~36周早产儿出生早期aEEG的周期性与脑损伤密切相关,可用于预测早产儿脑损伤。

     

    Abstract: Objective To study the characteristics of amplitude integrated electroencephalography (aEEG) in preterm infants with brain injury. Methods This study included 62 cerebral damage infants with 28-36 weeks gestational age (GA), and another 51 normal infants in control group, aEEG recording was performed to each infant during the first 48 h of life, the duration of each recording was at least 2 h. The features of aEEG, such as continuity(Co), sleep-wake cycling (Cy) and amplitude of the lower border (LB), were evaluated by semi-quantitative analysis and compared between the two groups. Results All the aEEG features were found having significantly lower values in brain injuries group (P<0.05). Logistic regression of aEEG features to the presence of brain injury revealed that only Cy was significantly correlated to the outcome (OR=0.217,P<0.05). ROC curve demonstrated Cy of the best sensitivity and specificity with 0.769 AUC. Co, LB yielded 0.677,0.602 AUC respectively. Correlation analysis of GA to Co, Cy, LB and total score showed significantly correlated, the correlation coefficient for Co, Cy, LB and total scores were 0.546,0.488,0.536,0.588 respectively(P<0.05). Conclusion The Cy in the initial aEEG is predictive for brain injury in premature infants with 28-36 weeks GA. The older the GA at birth,the more mature the aEEG pattern in premature neonates.

     

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