EEG monitoring during anesthesia in children aged 0 to 18 months: amplitude-integrated EEG and age effects.

BACKGROUND

The amplitude-integrated EEG (aEEG) is a widely used monitoring tool in neonatology / pediatric intensive care. It takes into account the amplitudes, but not the frequency composition, of the EEG. Advantages of the aEEG are clear criteria for interpretation and time compression. During the first year of life, the electroencephalogram (EEG) during sedation / anesthesia changes from a low-differentiated to a differentiated EEG; higher-frequency waves develop increasingly. There are few studies on the use of aEEG during pediatric anesthesia. A systematic evaluation of the aEEG in defined EEG stages during anesthesia / sedation is not yet available. Parameters of pediatric EEGs (power, median frequency, spectral edge frequency) recorded during anesthesia and of the corresponding aEEGs (upper and lower value of the aEEG trace) should be examined for age-related changes. Furthermore, it should be examined whether the aEEG can distinguish EEG stages of sedation / anesthesia in differentiated EEGs.

METHODS

In a secondary analysis of a prospective observational study EEGs and aEEGs (1-channel recordings, electrode positions on forehead) of 50 children (age: 0-18 months) were evaluated. EEG stages: A (awake), Slow EEG, E, F, and F in low-differentiated EEGs and A (awake), B, C, D, E, F in differentiated EEGs.

RESULTS

Median and spectral edge frequency increased significantly with age (p < 0.001 each). In low-differentiated EEGs, the power of the Slow EEG increased significantly with age (p < 0.001). In differentiated EEGs, the power increased significantly with age in each of the EEG stages B to E (p = 0.04, or less), and the upper and lower values of the aEEG trace increased with age (p < 0.001). A discriminant analysis using the upper and lower values of the aEEG showed that EEG epochs from the stages B to E were assigned to the original EEG stage in only 19.3% of the cases. When age was added as the third variable, the rate of correct reclassifications was 28.5%.

CONCLUSIONS

The aEEG was not suitable for distinguishing EEG stages above the burst suppression range. For this purpose, the frequency composition of the EEG should be taken into account.