Delay in Treatment of Neonatal Seizures: A Retrospective Cohort Study

Introduction: Neonatal seizures are common and caused by a variety of underlying disorders. There is increasing evidence that neonatal seizures result in further brain damage. Objective: To describe the time interval between diagnosis of amplitude-integrated electroencephalography (aEEG)-confirmed seizures and administration of anti-epileptic drugs (AEDs). Methods: Single-centre retrospective cohort study, with full-term infants (n = 106) admitted to a level III neonatal intensive care unit between 2012 and 2017 with seizures confirmed on 2-channel aEEG and corresponding raw electroencephalography traces, treated with AEDs. The time interval between the first seizure on the aEEG registration and AED administration was calculated. Factors associated with early treatment were analysed. Results: The median time interval of initiating treatment of aEEG-confirmed seizures was 01:50 h (interquartile range 00:43-4:30 h). Treatment of aEEG-confirmed seizures was initiated 1 h (67 vs. 42%, p = 0.02) and showed more clinical signs (79.4 vs. 37.5%, p < 0.01). There was no difference for out-of-office hours (23.5 vs. 22.2%, p = 0.88). Conclusion: With only 32.1% of the seizures being treated <1 h, there is room for improvement. Timely treatment occurred more often when seizures were clinical or recognised by the SDA. aEEG is a helpful tool for diagnosing seizures 24/7

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

Increased Use of Therapeutic Hypothermia in Infants with Milder Neonatal Encephalopathy due to Presumed Perinatal Asphyxia

INTRODUCTION: Adverse outcomes have been reported in infants with mild neonatal encephalopathy (NE). Increasing clinical experience with the application of therapeutic hypothermia (TH) may have resulted in the treatment of newborns with milder NE during recent years. OBJECTIVE: To determine whether infants treated with TH in the initial years following implementation had a higher degree of NE than infants treated during subsequent years. METHODS: Infants with NE treated with TH from February 2008 until July 2017 were included. Thompson and Sarnat scores, amplitude-integrated electroencephalography (aEEG) background patterns before the start of TH, and neurodevelopmental outcome at 2 years were compared between infants treated from February 2008 until October 2012 (period 1) and infants treated from November 2012 until July 2017 (period 2). RESULTS: 211 newborns with NE were treated with TH (period 1: n = 109, period 2: n = 102). Sarnat scores in period 1 and 2 were mild in 7.3 vs. 28.4%, moderate in 66.1 vs. 44.1%, and severe in 26.6 vs. 22.5%, respectively (p = 0.008). Thompson scores were lower in period 2 (median = 9, IQR 7-12) than in period 1 (median = 10, IQR 8.5-13.5, p = 0.018). The aEEGs and neurodevelopmental outcomes were comparable between the periods. CONCLUSIONS: Based on Thompson and Sarnat scores, but not aEEG background patterns, infants treated during the second period had milder NE than infants treated during the first years following implementation of TH. There was no difference in 2 years neurodevelopmental outcome. Further research is necessary to evaluate the value of TH for infants with clinically mild NE

Early recognition of characteristic conventional and amplitude-integrated EEG patterns of seizures in <i>SCN2A </i>and <i>KCNQ3</i>-related epilepsy in neonates

Purpose: Early recognition of seizures in neonates secondary to pathogenic variants in potassium or sodium channel coding genes is crucial, as these seizures are often resistant to commonly used anti-seizure medications but respond well to sodium channel blockers. Recently, a characteristic ictal amplitude-integrated electroencephalogram (aEEG) pattern was described in neonates with KCNQ2-related epilepsy. We report a similar aEEG pattern in seizures caused by SCN2A- and KCNQ3-pathogenic variants, as well as conventional EEG (cEEG) descriptions. Methods: International multicentre descriptive study, reporting clinical characteristics, aEEG and cEEG findings of 13 neonates with seizures due to pathogenic SCN2A- and KCNQ3-variants. As a comparison group, aEEGs and cEEGs of neonates with seizures due to hypoxic-ischemic encephalopathy (n = 117) and other confirmed genetic causes affecting channel function (n = 55) were reviewed. Results: In 12 out of 13 patients, the aEEG showed a characteristic sequence of brief onset with a decrease, followed by a quick rise, and then postictal amplitude attenuation. This pattern correlated with bilateral EEG onset attenuation, followed by rhythmic discharges ending in several seconds of post-ictal amplitude suppression. Apart from patients with KCNQ2-related epilepsy, none of the patients in the comparison groups had a similar aEEG or cEEG pattern. Discussion: Seizures in SCN2A- and KCNQ3-related epilepsy in neonates can usually be recognized by a characteristic ictal aEEG pattern, previously reported only in KCNQ2-related epilepsy, extending this unique feature to other channelopathies. Awareness of this pattern facilitates the prompt initiation of precision treatment with sodium channel blockers even before genetic results are available.</p

Early qualitative and quantitative amplitude-integrated electroencephalogram and raw electroencephalogram for predicting long-term neurodevelopmental outcomes in extremely preterm infants in the Netherlands: a 10-year cohort study

Background: Extremely preterm infants (<28 weeks of gestation) are at great risk of long-term neurodevelopmental impairments. Early amplitude-integrated electroencephalogram (aEEG) accompanied by raw EEG traces (aEEG−EEG) has potential for predicting subsequent outcomes in preterm infants. We aimed to determine whether and which qualitative and quantitative aEEG–EEG features obtained within the first postnatal days predict neurodevelopmental outcomes in extremely preterm infants. Methods: This study retrospectively analysed a cohort of extremely preterm infants (born before 28 weeks and 0 days of gestation) who underwent continuous two-channel aEEG–EEG monitoring during their first 3 postnatal days at Wilhelmina Children's Hospital, Utrecht, the Netherlands, between June 1, 2008, and Sept 30, 2018. Only infants who did not have genetic or metabolic diseases or major congenital malformations were eligible for inclusion. Features were extracted from preprocessed aEEG–EEG signals, comprising qualitative parameters grouped in three types (background pattern, sleep–wake cycling, and seizure activity) and quantitative metrics grouped in four categories (spectral content, amplitude, connectivity, and discontinuity). Machine learning-based regression and classification models were used to evaluate the predictive value of the extracted aEEG–EEG features for 13 outcomes, including cognitive, motor, and behavioural problem outcomes, at 2–3 years and 5–7 years. Potential confounders (gestational age at birth, maternal education, illness severity, morphine cumulative dose, the presence of severe brain injury, and the administration of antiseizure, sedative, or anaesthetic medications) were controlled for in all prediction analyses. Findings: 369 infants were included and an extensive set of 339 aEEG–EEG features was extracted, comprising nine qualitative parameters and 330 quantitative metrics. The machine learning-based regression models showed significant but relatively weak predictive performance (ranging from r=0·13 to r=0·23) for nine of 13 outcomes. However, the machine learning-based classifiers exhibited acceptable performance in identifying infants with intellectual impairments from those with optimal outcomes at age 5–7 years, achieving balanced accuracies of 0·77 (95% CI 0·62–0·90; p=0·0020) for full-scale intelligence quotient score and 0·81 (0·65–0·96; p=0·0010) for verbal intelligence quotient score. Both classifiers maintained identical performance when solely using quantitative features, achieving balanced accuracies of 0·77 (95% CI 0·63–0·91; p=0·0030) for full-scale intelligence quotient score and 0·81 (0·65–0·96; p=0·0010) for verbal intelligence quotient score. Interpretation: These findings highlight the potential benefits of using early postnatal aEEG–EEG features to automatically recognise extremely preterm infants with poor outcomes, facilitating the development of an interpretable prognostic tool that aids in decision making and therapy planning. Funding: European Commission Horizon 2020

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 and describe overall seizure management in a large neonatal cohort. Study design: Newborns (36-44 weeks of gestation) requiring electroencephalographic (EEG) monitoring recruited to 2 multicenter European studies were included. Infants who received antiseizure medication exclusively after electrographic seizure onset were grouped based on the time to treatment of the first seizure: antiseizure medication within 1 hour, between 1 and 2 hours, and after 2 hours. Outcomes measured were seizure burden, maximum seizure burden, status epilepticus, number of seizures, and antiseizure medication dose over the first 24 hours after seizure onset. Results: Out of 472 newborns recruited, 154 (32.6%) had confirmed electrographic seizures. Sixty-nine infants received antiseizure medication exclusively after the onset of electrographic seizure, including 21 infants within 1 hour of seizure onset, 15 between 1 and 2 hours after seizure onset, and 33 at >2 hours after seizure onset. Significantly lower seizure burden and fewer seizures were noted in the infants treated with antiseizure medication within 1 hour of seizure onset (P =.029 and.035, respectively). Overall, 258 of 472 infants (54.7%) received antiseizure medication during the study period, of whom 40 without electrographic seizures received treatment exclusively during EEG monitoring and 11 with electrographic seizures received no treatment. Conclusions: Treatment of neonatal seizures may be time-critical, but more research is needed to confirm this. Improvements in neonatal seizure diagnosis and treatment are also needed

Perinatal thalamic injury: MRI predictors of electrical status epilepticus in sleep and long-term neurodevelopment

Objective: Perinatal thalamic injury is associated with epilepsy with electrical status epilepticus in sleep (ESES). The aim of this study was to prospectively quantify the risk of ESES and to assess neuroimaging predictors of neurodevelopment. Methods: We included patients with perinatal thalamic injury. MRI scans were obtained in the neonatal period, around three months of age and during childhood. Thalamic and total brain volumes were obtained from the three months MRI. Diffusion characteristics were assessed. Sleep EEGs distinguished patients into ESES (spike-wave index (SWI) >85%), ESES-spectrum (SWI 50–85%) or no ESES (SWI < 50%). Serial Intelligence Quotient (IQ)/Developmental Quotient (DQ) scores were obtained during follow-up. Imaging and EEG findings were correlated to neurodevelopmental outcome. Results: Thirty patients were included. Mean thalamic volume at three months was 8.11 (±1.67) ml and mean total brain volume 526.45 (±88.99) ml. In the prospective cohort (n = 23) 19 patients (83%) developed ESES (-spectrum) abnormalities after a mean follow-up of 96 months. In the univariate analysis, larger thalamic volume, larger total brain volume and lower SWI correlated with higher mean IQ/DQ after 2 years (Pearson's r = 0.74, p = 0.001; Pearson's r = 0.64, p = 0.005; and Spearman's rho -0.44, p = 0.03). In a multivariable mixed model analysis, thalamic volume was a significant predictor of IQ/DQ (coefficient 9.60 [p < 0.001], i.e., corrected for total brain volume and SWI and accounting for repeated measures within patients, a 1 ml higher thalamic volume was associated with a 9.6 points higher IQ). Diffusion characteristics during childhood correlated with IQ/DQ after 2 years. Significance: Perinatal thalamic injury is followed by electrical status epilepticus in sleep in the majority of patients. Thalamic volume and diffusion characteristics correlate to neurodevelopmental outcome

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

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. FUNDING: Wellcome Trust, Science Foundation Ireland, and Nihon Kohden

Multi-ethnic genome-wide association study for atrial fibrillation

Atrial fibrillation (AF) affects more than 33 million individuals worldwide and has a complex heritability. We conducted the largest meta-analysis of genome-wide association studies (GWAS) for AF to date, consisting of more than half a million individuals, including 65,446 with AF. In total, we identified 97 loci significantly associated with AF, including 67 that were novel in a combined-ancestry analysis, and 3 that were novel in a European-specific analysis. We sought to identify AF-associated genes at the GWAS loci by performing RNA-sequencing and expression quantitative trait locus analyses in 101 left atrial samples, the most relevant tissue for AF. We also performed transcriptome-wide analyses that identified 57 AF-associated genes, 42 of which overlap with GWAS loci. The identified loci implicate genes enriched within cardiac developmental, electrophysiological, contractile and structural pathways. These results extend our understanding of the biological pathways underlying AF and may facilitate the development of therapeutics for AF