Poorva Deshpande1,2, Patrick J McNamara3,4, Cecil Hahn3,4, Prakesh S Shah5,6, Anne-Marie Guerguerian3,4. 1. Department of Paediatrics, Mount Sinai Hospital, 600 University Ave, Toronto, ON, M5G 1X5, Canada. poorva.deshpande@sinaihealth.ca. 2. Division of Neonatology, University of Toronto, Toronto, Canada. poorva.deshpande@sinaihealth.ca. 3. Hospital for Sick Children, Toronto, Canada. 4. Division of Neonatology, Departments of Pediatrics and Internal Medicine, Neurosciences and Mental Health Program, University of Iowa, Iowa, USA. 5. Department of Paediatrics, Mount Sinai Hospital, 600 University Ave, Toronto, ON, M5G 1X5, Canada. 6. Division of Neonatology, University of Toronto, Toronto, Canada.
Abstract
The developing preterm brain is vulnerable to injury, especially during periods of clinical instability; therefore, monitoring the brain may provide important information on brain health. Over the last 2 decades, a growing body of literature has been reported on preterm amplitude integrated electroencephalography (aEEG) with regards to normative data and associations with adverse outcomes. Despite this, the use of aEEG for preterm infants remains mostly a research tool with limited clinical applicability. In this article, we review the literature on normal and abnormal aEEG patterns in preterm infants and propose a stepwise clinical algorithm for aEEG assessment at the bedside that takes into account assessment of maturation and identification of pathological patterns. CONCLUSION: This algorithm may be used by clinicians at the bedside for interpretation to integrate it in clinical practice for neurological surveillance of preterm infants. WHAT IS KNOWN: • Studies have reported normative data on aEEG in preterm infants for different gestational ages. • Burst suppression pattern and absent sleep-wake cycling have been described to be associated with brain pathology and adverse outcomes in preterm infants. WHAT IS NEW: • We have synthesized aEEG characteristics in preterm infants across the spectrum of prematurity reported in the literature. • We present a stepwise approach for clinically applicable interpretation of aEEG in preterm infants.
The developing preterm brain is vulnerable to injury, especially during periods of clinical instability; therefore, monitoring the brain may provide important information on brain health. Over the last 2 decades, a growing body of literature has been reported on preterm amplitude integrated electroencephalography (aEEG) with regards to normative data and associations with adverse outcomes. Despite this, the use of aEEG for preterm infants remains mostly a research tool with limited clinical applicability. In this article, we review the literature on normal and abnormal aEEG patterns in preterm infants and propose a stepwise clinical algorithm for aEEG assessment at the bedside that takes into account assessment of maturation and identification of pathological patterns. CONCLUSION: This algorithm may be used by clinicians at the bedside for interpretation to integrate it in clinical practice for neurological surveillance of preterm infants. WHAT IS KNOWN: • Studies have reported normative data on aEEG in preterm infants for different gestational ages. • Burst suppression pattern and absent sleep-wake cycling have been described to be associated with brain pathology and adverse outcomes in preterm infants. WHAT IS NEW: • We have synthesized aEEG characteristics in preterm infants across the spectrum of prematurity reported in the literature. • We present a stepwise approach for clinically applicable interpretation of aEEG in preterm infants.
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