Role of EEG background activity, seizure burden and MRI in predicting neurodevelopmental outcome in full-term infants with hypoxic-ischaemic encephalopathy in the era of therapeutic hypothermia

OBJECTIVE: To investigate the role of EEG background activity, electrographic seizure burden, and MRI in predicting neurodevelopmental outcome in infants with hypoxic-ischaemic encephalopathy (HIE) in the era of therapeutic hypothermia. METHODS: Twenty-six full-term infants with HIE (September 2011-September 2012), who had video-EEG monitoring during the first 72 h, an MRI performed within the first two weeks and neurodevelopmental assessment at two years were evaluated. EEG background activity at age 24, 36 and 48 h, seizure burden, and severity of brain injury on MRI, were compared and related to neurodevelopmental outcome. RESULTS: EEG background activity was significantly associated with neurodevelopmental outcome at 36 h (p = 0.009) and 48 h after birth (p = 0.029) and with severity of brain injury on MRI at 36 h (p = 0.002) and 48 h (p = 0.018). All infants with a high seizure burden and moderate-severe injury on MRI had an abnormal outcome. The positive predictive value (PPV) of EEG for abnormal outcome was 100% at 36 h and 48 h and the negative predictive value (NPV) was 75% at 36 h and 69% at 48 h. The PPV of MRI was 100% and the NPV 85%. The PPV of seizure burden was 78% and the NPV 71%. CONCLUSION: Severely abnormal EEG background activity at 36 h and 48 h after birth was associated with severe injury on MRI and abnormal neurodevelopmental outcome. High seizure burden was only associated with abnormal outcome in combination with moderate-severe injury on MRI

Neonatal Seizure Management – Is the Timing of Treatment Critical?

OBJECTIVE: To assess the impact of the time to treatment of the first electrographic seizure on subsequent seizure burden; secondary aim was to describe overall seizure management in a large neonatal cohort. STUDY DESIGN: Newborns (36-44 weeks' gestation) requiring electroencephalographic (EEG) monitoring recruited to two multicentre European studies were included. Infants who received anti-seizure medication exclusively after electrographic seizure onset, were grouped based on time to treatment of the first seizure: ASM within 1-hour, ASM between 1-2 hours and ASM after 2-hours. Outcomes measured were seizure burden, maximum seizure burden, status epilepticus, number of seizures and ASM dose over 24-hours following seizure onset. RESULTS: Out of 472 newborns recruited, 154(32.6%) infants had confirmed electrographic seizures. Sixty-nine infants were exclusively treated after onset of electrographic seizures: 21 infants received ASM within 1 hour, 15 infants between 1-2 hours and 33 infants after 2 hours of seizure onset. Significantly lower seizure burden and less seizures were noted in infants treated with ASM within 1 hour from seizure onset (p value=0.029 and 0.035, respectively). Overall, 258/472(54.7%) infants received ASM throughout the study period, of which 40 infants without electrographic seizures had treatment during EEG monitoring and 11 infants with electrographic seizures had no treatment. CONCLUSION: Treatment of neonatal seizures may be time-critical, but more research is required to confirm this. We also need to improve neonatal seizure diagnosis and treatment

Characterisation of neonatal seizures and their treatment using continuous EEG monitoring: a multicentre experience.

OBJECTIVE: The 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. METHODS: Neonates 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. RESULTS: Of 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. CONCLUSIONS: Our results show that even with access to cEEG monitoring, neonatal seizures are frequent, difficult to recognise and difficult to treat. OBERSERVATION STUDY NUMBER: NCT02160171

Recommendations for the design of therapeutic trials for neonatal seizures

Although seizures have a higher incidence in neonates than any other age group and are associated with significant mortality and neurodevelopmental disability, treatment is largely guided by physician preference and tradition, due to a lack of data from welldesigned clinical trials. There is increasing interest in conducting trials of novel drugs to treat neonatal seizures, but the unique characteristics of this disorder and patient population require special consideration with regard to trial design. The Critical Path Institute formed a global working group of experts and key stakeholders from academia, the pharmaceutical industry, regulatory agencies, neonatal nurse associations, and patient advocacy groups to develop consensus recommendations for design of clinical trials to treat neonatal seizures. The broad expertise and perspectives of this group were invaluable in developing recommendations addressing: (1) use of neonate-specific adaptive trial designs, (2) inclusion/exclusion criteria, (3) stratification and randomization, (4) statistical analysis, (5) safety monitoring, and (6) definitions of important outcomes. The guidelines are based on available literature and expert consensus, pharmacokinetic analyses, ethical considerations, and parental concerns. These recommendations will ultimately facilitate development of a Master Protocol and design of efficient and successful drug trials to improve the treatment and outcome for this highly vulnerable population

The development and validation of a cerebral ultrasound scoring system for infants with hypoxic-ischaemic encephalopathy

Background: Hypoxic-ischaemic encephalopathy (HIE) is an important cause of morbidity and mortality in neonates. When the gold standard MRI is not feasible, cerebral ultrasound (CUS) might offer an alternative. In this study, the association between a novel CUS scoring system and neurodevelopmental outcome in neonates with HIE was assessed. Methods: (Near-)term infants with HIE and therapeutic hypothermia, a CUS on day 1 and day 3–7 after birth and available outcome data were retrospectively included in cohort I. CUS findings on day 1 and day 3–7 were related to adverse outcome in univariate and the CUS of day 3–7 also in multivariable logistic regression analyses. The resistance index, the sum of deep grey matter and of white matter involvement were included in multivariable logistic regression analyses. A comparable cohort from another hospital was used for validation (cohort II). Results: Eighty-three infants were included in cohort I and 35 in cohort II. The final CUS scoring system contained the sum of white matter (OR = 2.6, 95% CI 1.5–4.7) and deep grey matter involvement (OR = 2.7, 95% CI 1.7–4.4). The CUS scoring system performed well in cohort I (AUC = 0.90) and II (AUC = 0.89). Conclusion: This validated CUS scoring system is associated with neurodevelopmental outcome in neonates with HIE.</p

A machine-learning algorithm for neonatal seizure recognition: a multicentre, randomised, controlled trial

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 (non-algorithm 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 non-algorithm 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