IMPORTANCE Therapeutic hypothermia reduces risk of death and disability in infants with moderate to severe hypoxic ischemic encephalopathy (HIE). Randomized clinical trials of therapeutic hypothermia to date have not included infants with mild HIE because of a perceived good prognosis.OBJECTIVE To test the hypothesis that children with mild HIE have worse neurodevelopmental outcomes than their healthy peers.

Background Despite the availability of continuous conventional electroencephalography (cEEG), accurate diagnosis of neonatal seizures is challenging in clinical practice. Algorithms for decision support in the recognition of neonatal seizures could improve detection. We aimed to assess the diagnostic accuracy of an automated seizure detection algorithm called Algorithm for Neonatal Seizure Recognition (ANSeR). Methods This multicentre, randomised, two-arm, parallel, controlled trial was done in eight neonatal centres across Ireland, the Netherlands, Sweden, and the UK. Neonates with a corrected gestational age between 36 and 44 weeks with, or at significant risk of, seizures requiring EEG monitoring, received cEEG plus ANSeR linked to the EEG monitor displaying a seizure probability trend in real time (algorithm group) or cEEG monitoring alone (nonalgorithm group). The primary outcome was diagnostic accuracy (sensitivity, specificity, and false detection rate) of health-care professionals to identify neonates with electrographic seizures and seizure hours with and without the support of the ANSeR algorithm. Neonates with data on the outcome of interest were included in the analysis. This study is registered with ClinicalTrials.gov, NCT02431780. Findings Between Feb 13, 2015, and Feb 7, 2017, 132 neonates were randomly assigned to the algorithm group and 132 to the non-algorithm group. Six neonates were excluded (four from the algorithm group and two from the non-algorithm group). Electrographic seizures were present in 32 (25•0%) of 128 neonates in the algorithm group and 38 (29•2%) of 130 neonates in the non-algorithm group. For recognition of neonates with electrographic seizures, sensitivity was 81•3% (95% CI 66•7-93•3) in the algorithm group and 89•5% (78•4-97•5) in the non-algorithm group; specificity was 84•4% (95% CI 76•9-91•0) in the algorithm group and 89•1% (82•5-94•7) in the non-algorithm group; and the false detection rate was 36•6% (95% CI 22•7-52•1) in the algorithm group and 22•7% (11•6-35•9) in the non-algorithm group. We identified 659 h in which seizures occurred (seizure hours): 268 h in the algorithm versus 391 h in the nonalgorithm group. The percentage of seizure hours correctly identified was higher in the algorithm group than in the non-algorithm group (177 [66•0%; 95% CI 53•8-77•3] of 268 h vs 177 [45•3%; 34•5-58•3] of 391 h; difference 20•8% [3•6-37•1]). No significant differences were seen in the percentage of neonates with seizures given at least one inappropriate antiseizure medication (37•5% [95% CI 25•0 to 56•3] vs 31•6% [21•1 to 47•4]; difference 5•9% [-14•0 to 26•3]). Interpretation ANSeR, a machine-learning algorithm, is safe and able to accurately detect neonatal seizures. Although the algorithm did not enhance identification of individual neonates with seizures beyond conventional EEG, recognition of seizure hours was improved with use of ANSeR. The benefit might be greater in less experienced centres, but further study is required.

ObjectiveThe aim of this multicentre study was to describe detailed characteristics of electrographic seizures in a cohort of neonates monitored with multichannel continuous electroencephalography (cEEG) in 6 European centres.MethodsNeonates of at least 36 weeks of gestation who required cEEG monitoring for clinical concerns were eligible, and were enrolled prospectively over 2 years from June 2013. Additional retrospective data were available from two centres for January 2011 to February 2014. Clinical data and EEGs were reviewed by expert neurophysiologists through a central server.ResultsOf 214 neonates who had recordings suitable for analysis, EEG seizures were confirmed in 75 (35%). The most common cause was hypoxic-ischaemic encephalopathy (44/75, 59%), followed by metabolic/genetic disorders (16/75, 21%) and stroke (10/75, 13%). The median number of seizures was 24 (IQR 9–51), and the median maximum hourly seizure burden in minutes per hour (MSB) was 21 min (IQR 11–32), with 21 (28%) having status epilepticus defined as MSB>30 min/hour. MSB developed later in neonates with a metabolic/genetic disorder. Over half (112/214, 52%) of the neonates were given at least one antiepileptic drug (AED) and both overtreatment and undertreatment was evident. When EEG monitoring was ongoing, 27 neonates (19%) with no electrographic seizures received AEDs. Fourteen neonates (19%) who did have electrographic seizures during cEEG monitoring did not receive an AED.ConclusionsOur results show that even with access to cEEG monitoring, neonatal seizures are frequent, difficult to recognise and difficult to treat.Oberservation study numberNCT02160171