Improvement in the Prediction of Neonatal Hypoxic-Ischemic Encephalopathy with the Integration of Umbilical Cord Metabolites and Current Clinical Makers

[Investigation of a French cohort of neonatal anoxo-ischemic encephalopathy in the era of therapeutic hypothermia: Questions and answers]

OBJECTIVE

To improve knowledge of neonatal hypoxic-ischemic encephalopathy, a prospective, nationwide, population-based cohort of affected children is being set up between September 2015 and March 2017.

METHODS

During this period, 794 cases are collected, with information on pregnancy, delivery, neonatal stay and outcome at the end of hospitalization. Clinical and parental questionnaire follow-up is planned until the child is 4 years old.

RESULTS

This article presents the clinical presentation of the newborns included, the analysis of factors associated with short-term outcome at hospital discharge and the organizational factors associated with treatment with therapeutic hypothermia.

CONCLUSION

These data illustrate the value of a prospective cohort to analyze the management of anoxo-ischemic encephalopathy in France.

Searching molecular biomarkers correlating with BSID-III at 24 months in infants with neonatal hypoxic-ischemic encephalopathy

An early prediction of outcomes of neonatal hypoxic-ischemic encephalopathy (NE) is of key importance in reducing neonatal mortality and morbidity. The objectives were (i) to analyze the characteristics of miRNA expression and metabolic patterns of neonates with NE and (ii) to assess their predictive performance for neurodevelopmental outcomes. Plasma samples from moderate/severe NE patients (N = 92) of the HYPOTOP study were collected before, during, and after therapeutic hypothermia (TH) and compared to a control group (healthy term infants). The expression of miRNAs and concentrations of metabolites (hypoxia-related and energy, steroid, and tryptophan metabolisms) were analyzed. Neurodevelopmental outcomes were evaluated at 24 months postnatal age using Bayley Scales of Infant Development, ed. III, BSID-III. Differences in miRNA and metabolic profiles were found between NE vs. control infants, abnormal (i.e., mildly and moderately abnormal and severe) vs. normal, and severe vs. non-severe (i.e., normal and mildly and moderately abnormal) BSID-III. 4-Androstene-3,17-dione, testosterone, betaine, xanthine, and lactate were suitable for BSID-III outcome prediction (receiver operating characteristic areas under the curve (AUCs) ≥ 0.6), as well as 68 miRNAs (AUCs of 0.5-0.9). Significant partial correlations of xanthine and betaine levels and the expression of several miRNAs with BSID-III sub-scales were found. Conclusion: We have identified metabolites/miRNAs that might be useful to support the prediction of middle-term neurodevelopmental outcomes of NE. What is known and what is new: • The early prediction of outcomes of neonatal hypoxic-ischemic encephalopathy (NE) is of key importance in reducing neonatal mortality and morbidity. • Alterations of the metabolome and miRNAs had been observed in NE. • We performed miRNA sequencing and quantified selected metabolites (i.e., lactate, pyruvate, ketone bodies, Krebs cycle intermediates, tryptophan pathway, hypoxia-related metabolites, and steroids) by GC- and LC-MS. • Specific miRNAs and metabolites that allow prediction of middle-term neurodevelopmental outcomes of newborns with NE undergoing hypothermia treatment were identified.

Metabolite Biomarkers for Early Ischemic-Hypoxic Encephalopathy: An Experimental Study Using the NeoBase 2 MSMS Kit in a Rat Model

Hypoxic-ischemic encephalopathy (HIE) is one of the most common causes of childhood disability. Hypothermic therapy is currently the only approved neuroprotective approach. However, early diagnosis of HIE can be challenging, especially in the first hours after birth when the decision to use hypothermic therapy is critical. Distinguishing HIE from other neonatal conditions, such as sepsis, becomes a significant problem in diagnosis. This study explored the utility of a metabolomic-based approach employing the NeoBase 2 MSMS kit to diagnose HIE using dry blood stains in a Rice-Vannucci model of HIE in rats. We evaluated the diagnostic fidelity of this approach in a range between 3 and 6 h after the onset of HIE, including in the context of systemic inflammation and concomitant hypothermic therapy. Discriminant analysis revealed several metabolite patterns associated with HIE. A logistic regression model using glycine levels achieved high diagnostic fidelity with areas under the receiver operating characteristic curve of 0.94 at 3 h and 0.96 at 6 h after the onset of HIE. In addition, orthogonal partial least squares discriminant analysis, which included five metabolites, achieved 100% sensitivity and 80% specificity within 3 h of HIE. These results highlight the significant potential of the NeoBase 2 MSMS kit for the early diagnosis of HIE and could improve patient management and outcomes in this serious illness.

A screening test proposal for congenital defects based on maternal serum metabolomics profile

BACKGROUND

Historically, noninvasive techniques are only able to identify chromosomal anomalies that accounted for <50% of all congenital defects; the other congenital defects are diagnosed via ultrasound evaluations in the later stages of pregnancy. Metabolomic analysis may provide an important improvement, potentially addressing the need for novel noninvasive and multicomprehensive early prenatal screening tools. A growing body of evidence outlines notable metabolic alterations in different biofluids derived from pregnant women carrying fetuses with malformations, suggesting that such an approach may allow the discovery of biomarkers common to most fetal malformations. In addition, metabolomic investigations are inexpensive, fast, and risk-free and often generate high performance screening tests that may allow early detection of a given pathology.

OBJECTIVE

This study aimed to evaluate the diagnostic accuracy of an ensemble machine learning model based on maternal serum metabolomic signatures for detecting fetal malformations, including both chromosomal anomalies and structural defects.

STUDY DESIGN

This was a multicenter observational retrospective study that included 2 different arms. In the first arm, a total of 654 Italian pregnant women (334 cases with fetuses with malformations and 320 controls with normal developing fetuses) were enrolled and used to train an ensemble machine learning classification model based on serum metabolomics profiles. In the second arm, serum samples obtained from 1935 participants of the New Zealand Screening for Pregnancy Endpoints study were blindly analyzed and used as a validation cohort. Untargeted metabolomics analysis was performed via gas chromatography-mass spectrometry. Of note, 9 individual machine learning classification models were built and optimized via cross-validation (partial least squares-discriminant analysis, linear discriminant analysis, naïve Bayes, decision tree, random forest, k-nearest neighbor, artificial neural network, support vector machine, and logistic regression). An ensemble of the models was developed according to a voting scheme statistically weighted by the cross-validation accuracy and classification confidence of the individual models. This ensemble machine learning system was used to screen the validation cohort.

RESULTS

Significant metabolic differences were detected in women carrying fetuses with malformations, who exhibited lower amounts of palmitic, myristic, and stearic acids; N-α-acetyllysine; glucose; L-acetylcarnitine; fructose; para-cresol; and xylose and higher levels of serine, alanine, urea, progesterone, and valine (P<.05), compared with controls. When applied to the validation cohort, the screening test showed a 99.4%±0.6% accuracy (specificity of 99.9%±0.1% [1892 of 1894 controls correctly identified] with a sensitivity of 78%±6% [32 of 41 fetal malformations correctly identified]).

CONCLUSION

This study provided clinical validation of a metabolomics-based prenatal screening test to detect the presence of congenital defects. Further investigations are needed to enable the identification of the type of malformation and to confirm these findings on even larger study populations.

Brain Injury in Infants Evaluated for, But Not Treated with, Therapeutic Hypothermia

Defining neonatal encephalopathy clinically to qualify for therapeutic hypothermia is challenging. This study examines magnetic resonance imaging outcomes of 39 infants who were evaluated and not cooled using criteria inclusive of mild encephalopathy. Infants evaluated for therapeutic hypothermia are at risk for brain injury and may benefit from neuroimaging and follow-up.

Risk factors for unfavorable outcome at discharge of newborns with hypoxic-ischemic encephalopathy in the era of hypothermia

OBJECTIVE

To re-visit short-term outcomes and associated risk factors of newborns with hypoxic-ischemic encephalopathy (HIE) in an era where hypothermia treatment (HT) is widespread.

METHODS

This is a prospective population-based cohort in French neonatal intensive care units (NICU). Neonates born at or after 34 weeks of gestational age with HIE were included; main outcomes were in-hospital death and discharge with abnormal or normal MRI. Associations of early perinatal risk factors, present at birth or at admission to NICU, with these outcomes were studied.

RESULTS

A total of 794 newborns were included and HT was administered to 670 (84.4%); 18.3% died and 28.5% and 53.2% survived with abnormal and normal MRI, respectively. Severe neurological status, Apgar score at 5 mn ≤5, lactate at birth ≥11 mMoles/l, and glycemia ≥100 mg/dL at admission were associated with an increased risk of death (relative risk ratios (aRRR) (95% CI) 19.93 (10.00-39.70), 2.89 (1.22-1.62), 3.06 (1.60-5.83), and 2.55 (1.38-4.71), respectively). Neurological status only was associated with survival with abnormal MRI (aRRR (95% CI) 1.76 (1.15-2.68)).

CONCLUSION

Despite high use of HT in this cohort, 46.8% died or presented brain lesions. Early neurological and biological examinations were associated with unfavorable outcomes and these criteria could be used to target children who warrant further neuroprotective treatment.

TRIAL REGISTRATION

Clinical trial registry, NCT02676063, ClinicalTrials.gov.

IMPACT

In this population-based cohort of newborns with HIE where 84% received hypothermia, 46.8% still had an unfavorable evolution (death or survival with abnormal MRI). Risk factors for death were high lactate, low Apgar score, severe early neurological examination, and high glycaemia. While studies have established risk factors for HIE, few have focused on early perinatal factors associated with short-term prognosis. This French population-based cohort updates knowledge about early risk factors for adverse outcomes in the era of widespread cooling. In the future, criteria associated with an unfavorable evolution could be used to target children who would benefit from another neuroprotective strategy with hypothermia.

Sodium butyrate mediates histone crotonylation and alleviated neonatal rats hypoxic–ischemic brain injury through gut–brain axis

Neonatal hypoxic–ischemic encephalopathy (HIE) refers to nervous system damage caused by perinatal hypoxia, which is the major cause of long-term neuro-developmental disorders in surviving infants. However, the mechanisms still require further investigation. In this study, we found that the butanoate metabolism pathway exhibited significantly decreased and short chain fatty acid (SCFAs)-producing bacteria, especially butyrate-producing bacteria, were significantly decreased in fecal of neonatal hypoxic–ischemic brain damage (HIBD) rats. Surprisingly, Sodium butyrate (SB) treatment could ameliorate pathological damage both in the cerebral cortex and hippocampus and facilitate recovery of SCFAs-producing bacteria related to metabolic pathways in neonatal HIBD rats. Moreover, we found that in samples from SB treatment neonatal HIBD rats cortex with high levels of butyrate acid along with aberrant key crotonyl-CoA-producing enzymes ACADS levels were observed compared HIBD rats. We also demonstrated that a decrease in histone 3-lysine 9-crotonylation (H3K9cr) downregulated expression of the HIE-related neurotrophic genes Bdnf, Gdnf, Cdnf, and Manf in HIBD rats. Furthermore, SB restored H3K9cr binding to HIE-related neurotrophic genes. Collectively, our results indicate that SB contributes to ameliorate pathology of HIBD by altering gut microbiota and brain SCFAs levels subsequently affecting histone crotonylation-mediated neurotrophic-related genes expression. This may be a novel microbiological approach for preventing and treating HIE.

Diagnostic and Therapeutic Roles of the “Omics” in Hypoxic–Ischemic Encephalopathy in Neonates

Perinatal asphyxia and neonatal encephalopathy remain major causes of neonatal mortality, despite the improved availability of diagnostic and therapeutic tools, contributing to neurological and intellectual disabilities worldwide. An approach using a combination of clinical data, neuroimaging, and biochemical parameters is the current strategy towards the improved diagnosis and prognosis of the outcome in neonatal hypoxic–ischemic encephalopathy (HIE) using bioengineering methods. Traditional biomarkers are of little use in this multifactorial and variable phenotype-presenting clinical condition. Novel systems of biology-based “omics” approaches (genomics, transcriptome proteomics, and metabolomics) may help to identify biomarkers associated with brain and other tissue injuries, predicting the disease severity in HIE. Biomarker studies using omics technologies will likely be a key feature of future neuroprotective treatment methods and will help to assess the successful treatment and long-term efficacy of the intervention. This article reviews the roles of different omics as biomarkers of HIE and outlines the existing knowledge of our current understanding of the clinical use of different omics molecules as novel neonatal brain injury biomarkers, which may lead to improved interventions related to the diagnostic and therapeutic aspects of HIE.

Urinary metabotypes of newborns with perinatal asphyxia undergoing therapeutic hypothermia

Perinatal asphyxia (PA) still occurs in about three to five per 1,000 deliveries in developed countries; 20% of these infants show hypoxic-ischemic encephalopathy (HIE) on brain magnetic resonance imaging (MRI). The aim of our study was to apply metabolomic analysis to newborns undergoing therapeutic hypothermia (TH) after PA to identify a distinct metabotype associated with the development of HIE on brain MRI. We enrolled 53 infants born at >35 weeks of gestation with PA: 21 of them showed HIE on brain MRI (the “HIE” group), and 32 did not (the “no HIE” group). Urine samples were collected at 24, 48 and 72 hours of TH. Metabolomic data were acquired using high-resolution mass spectrometry and analyzed with univariate and multivariate methods. Considering the first urines collected during TH, untargeted analysis found 111 relevant predictors capable of discriminating between the two groups. Of 35 metabolites showing independent discriminatory power, four have been well characterized: L-alanine, Creatine, L-3-methylhistidine, and L-lysine. The first three relate to cellular energy metabolism; their involvement suggests a multimodal derangement of cellular energy metabolism during PA/HIE. In addition, seven other metabolites with a lower annotation level (proline betaine, L-prolyl-L-phenylalanine, 2-methyl-dodecanedioic acid, S-(2-methylpropionyl)-dihydrolipoamide-E, 2,6 dimethylheptanoyl carnitine, Octanoylglucuronide, 19-hydroxyandrost-4-ene-3,17-dione) showed biological consistency with the clinical picture of PA. Moreover, 4 annotated metabolites (L-lysine, L-3-methylhistidine, 2-methyl-dodecanedioic acid, S-(2-methylpropionyl)-dihydrolipoamide-E) retained a significant difference between the “HIE” and “no HIE” groups during all the TH treatment. Our analysis identified a distinct urinary metabotype associated with pathological findings on MRI, and discovered 2 putative markers (L-lysine, L-3-methylhistidine) which may be useful for identifying neonates at risk of developing HIE after PA.

Rationale for the Use of Cord Blood in Hypoxic-Ischaemic Encephalopathy

Hypoxic-ischaemic encephalopathy (HIE) is a severe complication of asphyxia at birth. Therapeutic hypothermia, the standard method for HIE prevention, is effective in only 50% of the cases. As the understanding of the immunological basis of these changes increases, experiments have begun with the use of cord blood (CB) because of its neuroprotective properties. Mechanisms for the neuroprotective effects of CB stem cells include antiapoptotic and anti-inflammatory actions, stimulation of angiogenesis, production of trophic factors, and mitochondrial donation. In several animal models of HIE, CB decreased oxidative stress, cell death markers, CD4+ T cell infiltration, and microglial activation; restored normal brain metabolic activity; promoted neurogenesis; improved myelination; and increased the proportion of mature oligodendrocytes, neuron numbers in the motor cortex and somatosensory cortex, and brain weight. These observations translate into motor strength, limb function, gait, and cognitive function and behaviour. In humans, the efficacy and safety of CB administration were reported in a few early clinical studies which confirmed the feasibility and safety of this intervention for up to 10 years. The results of these studies showed an improvement in the developmental outcomes over hypothermia. Two phase-2 clinical studies are ongoing under the United States regulations, namely one controlled study and one blinded study.

Current Status and Future Directions of Neuromonitoring With Emerging Technologies in Neonatal Care

Neonatology has experienced a significant reduction in mortality rates of the preterm population and critically ill infants over the last few decades. Now, the emphasis is directed toward improving long-term neurodevelopmental outcomes and quality of life. Brain-focused care has emerged as a necessity. The creation of neonatal neurocritical care units, or Neuro-NICUs, provides strategies to reduce brain injury using standardized clinical protocols, methodologies, and provider education and training. Bedside neuromonitoring has dramatically improved our ability to provide assessment of newborns at high risk. Non-invasive tools, such as continuous electroencephalography (cEEG), amplitude-integrated electroencephalography (aEEG), and near-infrared spectroscopy (NIRS), allow screening for seizures and continuous evaluation of brain function and cerebral oxygenation at the bedside. Extended and combined uses of these techniques, also described as multimodal monitoring, may allow practitioners to better understand the physiology of critically ill neonates. Furthermore, the rapid growth of technology in the Neuro-NICU, along with the increasing use of telemedicine and artificial intelligence with improved data mining techniques and machine learning (ML), has the potential to vastly improve decision-making processes and positively impact outcomes. This article will cover the current applications of neuromonitoring in the Neuro-NICU, recent advances, potential pitfalls, and future perspectives in this field.

Prediction of patient survival following postanoxic coma using EEG data and clinical features

Electroencephalography (EEG) is an effective and non-invasive technique commonly used to monitor brain activity and assist in outcome prediction for comatose patients post cardiac arrest. EEG data may demonstrate patterns associated with poor neurological outcome for patients with hypoxic injury. Thus, both quantitative EEG (qEEG) and clinical data contain prognostic information for patient outcome. In this study we use machine learning (ML) techniques, random forest (RF) and support vector machine (SVM) to classify patient outcome post cardiac arrest using qEEG and clinical feature sets, individually and combined. Our ML experiments show RF and SVM perform better using the joint feature set. In addition, we extend our work by implementing a convolutional neural network (CNN) based on time-frequency images derived from EEG to compare with our qEEG ML models. The results demonstrate significant performance improvement in outcome prediction using non-feature based CNN compared to our feature based ML models. Implementation of ML and DL methods in clinical practice have the potential to improve reliability of traditional qualitative assessments for postanoxic coma patients.

The Term Newborn: Evaluation for Hypoxic-Ischemic Encephalopathy

Neonatal encephalopathy due to perinatal hypoxia-ischemia (hypoxic-ischemic encephalopathy [HIE]) occurs at a rate of 1 to 3 per 1000 live births. Therapeutic hypothermia is the standard of care and the only currently available therapy to reduce the risk of death or disability in newborns with moderate to severe HIE. Hypothermia therapy needs to be initiated within 6 hours after birth in order to provide the best chance for neuroprotection. All pediatricians and delivery room attendants should be trained to recognize encephalopathy and understand the eligibility criteria for treatment. The modified Sarnat examination is the most frequently used tool to assess the degree of encephalopathy and has six categories, each of which can have mild, moderate, severe abnormalities. Apart from historical and biochemical criteria, a neonate must have 3 of 6 categories scored in the moderate or severe range in order to qualify for hypothermia as was done in the randomized trials. Whether an infant qualifies or there is concern that an infant might have HIE, transfer to a center that can perform treatment should be initiated immediately. Hypothermia significantly reduces the risk of death or moderate to severe impairments at 2 years and at school age. On average, only 7 neonates need to be treated for one neonate to benefit. Although easy in concept, implementation of hypothermia does require expertise and should be carried out under the guidance of a neonatologist. If infants are passively cooled prior to transport, core temperature needs to be closely monitored with a target of 33.5°C ± 0.5°C. Maintenance of homeostasis is important in order to prevent conditions that may result in additional brain injury. Seizures are common in neonates with HIE, but electrographic seizures are rare in the first few hours after birth if the insult occurred during labor and delivery. Prophylactic antiepileptic drugs should not be administered. Brain monitoring in the form of electroencephalogram (EEG) and or amplitude-integrated EEG should be implemented as soon as possible to help with prognosis and to accurately diagnose seizures.

Predictive modelling of hypoxic ischaemic encephalopathy risk following perinatal asphyxia

Hypoxic Ischemic Encephalopathy (HIE) remains a major cause of neurological disability. Early intervention with therapeutic hypothermia improves outcome, but prediction of HIE is difficult and no single clinical marker is reliable. Machine learning algorithms may allow identification of patterns in clinical data to improve prognostic power. Here we examine the use of a Random Forest machine learning algorithm and five-fold cross-validation to predict the occurrence of HIE in a prospective cohort of infants with perinatal asphyxia. Infants with perinatal asphyxia were recruited at birth and neonatal course was followed for the development of HIE. Clinical variables were recorded for each infant including maternal demographics, delivery details and infant's condition at birth. We found that the strongest predictors of HIE were the infant's condition at birth (as expressed by Apgar score), need for resuscitation, and the first postnatal measures of pH, lactate, and base deficit. Random Forest models combining features including Apgar score, most intensive resuscitation, maternal age and infant birth weight both with and without biochemical markers of pH, lactate, and base deficit resulted in a sensitivity of 56-100% and a specificity of 78-99%. This study presents a dynamic method of rapid classification that has the potential to be easily adapted and implemented in a clinical setting, with and without the availability of blood gas analysis. Our results demonstrate that applying machine learning algorithms to readily available clinical data may support clinicians in the early and accurate identification of infants who will develop HIE. We anticipate our models to be a starting point for the development of a more sophisticated clinical decision support system to help identify which infants will benefit from early therapeutic hypothermia.

Precision Medicine in Neonates: A Tailored Approach to Neonatal Brain Injury

Despite advances in neonatal care to prevent neonatal brain injury and neurodevelopmental impairment, predicting long-term outcome in neonates at risk for brain injury remains difficult. Early prognosis is currently based on cranial ultrasound (CUS), MRI, EEG, NIRS, and/or general movements assessed at specific ages, and predicting outcome in an individual (precision medicine) is not yet possible. New algorithms based on large databases and machine learning applied to clinical, neuromonitoring, and neuroimaging data and genetic analysis and assays measuring multiple biomarkers (omics) can fulfill the needs of modern neonatology. A synergy of all these techniques and the use of automatic quantitative analysis might give clinicians the possibility to provide patient-targeted decision-making for individualized diagnosis, therapy, and outcome prediction. This review will first focus on common neonatal neurological diseases, associated risk factors, and most common treatments. After that, we will discuss how precision medicine and machine learning (ML) approaches could change the future of prediction and prognosis in this field.

A Metabolomic Approach in Search of Neurobiomarkers of Perinatal Asphyxia: A Review of the Current Literature

Perinatal asphyxia and the possible sequelae of hypoxic-ischemic encephalopathy (HIE), are associated with high morbidity and mortality rates. The use of therapeutic hypothermia (TH) commencing within the first 6 h of life-currently the only treatment validated for the management of HIE-has been proven to reduce the mortality rate and disability seen at follow up at 18 months. Although there have been attempts to identify neurobiomarkers assessing the severity levels in HIE; none have been validated in clinical use to date, and the lack thereof limits the optimal treatment for these vulnerable infants. Metabolomics is a promising field of the "omics technologies" that may: identify neurobiomarkers, help improve diagnosis, identify patients prone to developing HIE, and potentially improve targeted neuroprotection interventions. This review focuses on the current evidence of metabolomics, a novel tool which may prove to be a useful in the diagnosis, management and treatment options for this multifactorial complex disease. Some of the most promising metabolites analyzed are the group of acylcarnitines: Hydroxybutyrylcarnitine (Malonylcarnitine) [C3-DC (C4-OH)], Tetradecanoylcarnitine [C14], L-Palmitoylcarnitine [C16], Hexadecenoylcarnitine [C16:1], Stearoylcarnitine [C18], and Oleoylcarnitine [C18:1]. A metabolomic "fingerprint" or "index," made up of 4 metabolites (succinate × glycerol/(β-hydroxybutyrate × O-phosphocholine)), seems promising in identifying neonates at risk of developing severe HIE.