OBJECTIVE: Prolonged continuous video-electroencephalography (cEEG) is recommended for neonates at risk of seizures. The cost and expertise required to provide a real-time response to detected seizures often limits its utility. We hypothesised that the first hour of cEEG could predict subsequent seizures.
DESIGN AND SETTING: Retrospective multicentre diagnostic accuracy study.
PATIENTS: 266 term neonates at risk of seizure or with suspected seizures.
INTERVENTION: The first hour of cEEG was graded by expert and novice interpreters as normal, mildly, moderately or severely abnormal; seizures were identified.
MAIN OUTCOME MEASURES: Association between abnormalities in the first hour of cEEG and the presence of seizures during total cEEG monitoring.
RESULTS: 50/98 (51%) of neonates who developed seizures had their first seizure in the first hour of cEEG monitoring. The 'time-to-event' risk of seizure from 0 to 96 hours was 0.38 (95% CI 0.32 to 0.44) while the risk in the first hour was 0.19 (95% CI 0.15 to 0.24). cEEG background was normal in 48% of neonates, mildly abnormal in 30%, moderately abnormal in 13% and severely abnormal in 9%. Inter-rater agreement for determination of background was very good (weighted kappa=0.81, 95% CI 0.72 to 0.91). When neonates with seizures during the first hour were excluded, an abnormal background resulted in 2.4 times increased risk of seizures during the subsequent monitoring period (95% CI 1.3 to 4.4, p<0.003) while a severely abnormal background resulted in a sevenfold increased risk (95% CI 3.4 to 14.3, p<0.0001).
CONCLUSIONS: The first hour of cEEG in at-risk neonates is useful in identifying and predicting whether seizures occur during cEEG monitoring up to 96 hours. This finding enables identification of high-risk neonates who require closer observation.
|Original language||English (US)|
|Number of pages||6|
|Journal||Archives of Disease in Childhood: Fetal and Neonatal Edition|
|State||Published - Mar 1 2021|
Bibliographical noteFunding Information:
Acknowledgements We thank the patients and parents who participated in this study. We acknowledge the work of the many NICU nurses, respiratory technicians and EEG technicians who contributed to this study. Warm thanks to Melly Massie for providing IT support for the study. We thank Cadwell for EEG systems support, Persyst for their seizure detection and trending software and Corticare for providing EEG technician real-time monitoring of subjects. Grateful thanks to the Starship Foundation for funding provided to SLD.
© 2021 BMJ Publishing Group. All rights reserved.
- intensive care
- Severity of Illness Index
- Risk Factors
- Kaplan-Meier Estimate
- Gestational Age
- Time Factors
- Infant, Newborn, Diseases/diagnosis
- Retrospective Studies
- Infant, Newborn
PubMed: MeSH publication types
- Randomized Controlled Trial
- Multicenter Study
- Clinical Trial, Phase II
- Journal Article