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

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

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

Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure

Heart failure (HF) is a leading cause of morbidity and mortality worldwide. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained. We report results from a GWAS meta-analysis of HF comprising 47,309 cases and 930,014 controls. Twelve independent variants at 11 genomic loci are associated with HF, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function, suggesting shared genetic aetiology. Functional analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homoeostasis (BAG3), and cellular senescence (CDKN1A). Mendelian randomisation analysis supports causal roles for several HF risk factors, and demonstrates CAD-independent effects for atrial fibrillation, body mass index, and hypertension. These findings extend our knowledge of the pathways underlying HF and may inform new therapeutic strategies

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

Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure

Abstract: Heart failure (HF) is a leading cause of morbidity and mortality worldwide. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained. We report results from a GWAS meta-analysis of HF comprising 47,309 cases and 930,014 controls. Twelve independent variants at 11 genomic loci are associated with HF, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function, suggesting shared genetic aetiology. Functional analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homoeostasis (BAG3), and cellular senescence (CDKN1A). Mendelian randomisation analysis supports causal roles for several HF risk factors, and demonstrates CAD-independent effects for atrial fibrillation, body mass index, and hypertension. These findings extend our knowledge of the pathways underlying HF and may inform new therapeutic strategies

Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure

Abstract: Heart failure (HF) is a leading cause of morbidity and mortality worldwide. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained. We report results from a GWAS meta-analysis of HF comprising 47,309 cases and 930,014 controls. Twelve independent variants at 11 genomic loci are associated with HF, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function, suggesting shared genetic aetiology. Functional analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homoeostasis (BAG3), and cellular senescence (CDKN1A). Mendelian randomisation analysis supports causal roles for several HF risk factors, and demonstrates CAD-independent effects for atrial fibrillation, body mass index, and hypertension. These findings extend our knowledge of the pathways underlying HF and may inform new therapeutic strategies

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Reply to Letter to the editor https://www.sciencedirect.com/science/article/pii/S0022347618300611 The prognostic value of a novel magnetic resonance imaging/magnetic resonance spectroscopy score after hypoxic ischemic encephalopathy: methodological concern

A Comparison of the Thompson Encephalopathy Score and Amplitude-Integrated Electroencephalography in Infants with Perinatal Asphyxia and Therapeutic Hypothermia

BACKGROUND: In previous studies clinical signs or amplitude-integrated electroencephalography (aEEG)-based signs of encephalopathy were used to select infants with perinatal asphyxia for treatment with hypothermia. AIM: The objective of this study was to compare Thompson encephalopathy scores and aEEG, and relate both to outcome. SUBJECTS AND METHODS: Thompson scores, aEEG, and outcome were compared in 122 infants with perinatal asphyxia and therapeutic hypothermia. Of these 122 infants, 41 died and 7 had an adverse neurodevelopmental outcome. A receiver operating characteristics (ROC) analysis was also performed. RESULTS: Thompson scores were higher in infants with more abnormal aEEG background patterns (ANOVA, p < 0.001). The ROC analysis demonstrated that a Thompson score of 11 or higher or an aEEG background pattern of continuous low voltage or worse was associated with an adverse outcome (AUC 0.84 for both). CONCLUSIONS: High Thompson scores and a suppressed aEEG background pattern are associated with an adverse outcome after perinatal asphyxia and therapeutic hypothermia. Further studies are needed to identify the best technique with which to select patients for therapeutic hypothermia