The impact of perinatal inflammation on the electroencephalogram in preterm infants: a systematic review

Background To summarise the association between perinatal inflammation (PI) exposure and electroencephalography (EEG) features in preterm infants. Methods This systematic review included clinical studies of preterm infants born <37 weeks of gestational age (GA), who had both a PI exposure and an EEG assessment performed during the neonatal period. Studies were identified from Medline and Embase databases on the 15th of September 2021. PI was defined by histological chorioamnionitis, clinical chorioamnionitis, or early-onset neonatal infection (EONI). The risk of bias in included studies was assessed using the Joanna Briggs Institute (JBI) appraisal tool. A narrative approach was used to synthesise results. This review followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 statement. Results Two cross-sectional studies enrolling 130 preterm children born <32 weeks of GA assessed with one-channel amplitude-integrated EEG (aEEG) during the first four days of life were included. A PI exposure was described in 39 (30%) infants and was associated with a decrease in amplitude and a reduced incidence of sleep-wake cycling patterns. Conclusion These results should be interpreted with caution because of the small number of included studies and their heterogeneity. Further clinical studies evaluating the association of PI with EEG findings are needed. Impact A method to assess developmental trajectories following perinatal inflammation is required. Insufficient data exist to determine EEG features associated with perinatal inflammation. Further clinical studies evaluating this association are needed.

Automated summary

This study aimed to summarise the association between perinatal inflammation exposure and electroencephalography (EEG) features in preterm infants. The results showed that perinatal inflammation exposure was associated with a decrease in amplitude and a reduced incidence of sleep-wake cycling patterns.