Predictive modelling of hypoxic ischaemic encephalopathy risk following perinatal asphyxia

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.

The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review

Machine learning and deep learning are two subsets of artificial intelligence that involve teaching computers to learn and make decisions from any sort of data. Most recent developments in artificial intelligence are coming from deep learning, which has proven revolutionary in almost all fields, from computer vision to health sciences. The effects of deep learning in medicine have changed the conventional ways of clinical application significantly. Although some sub-fields of medicine, such as pediatrics, have been relatively slow in receiving the critical benefits of deep learning, related research in pediatrics has started to accumulate to a significant level, too. Hence, in this paper, we review recently developed machine learning and deep learning-based solutions for neonatology applications. We systematically evaluate the roles of both classical machine learning and deep learning in neonatology applications, define the methodologies, including algorithmic developments, and describe the remaining challenges in the assessment of neonatal diseases by using PRISMA 2020 guidelines. To date, the primary areas of focus in neonatology regarding AI applications have included survival analysis, neuroimaging, analysis of vital parameters and biosignals, and retinopathy of prematurity diagnosis. We have categorically summarized 106 research articles from 1996 to 2022 and discussed their pros and cons, respectively. In this systematic review, we aimed to further enhance the comprehensiveness of the study. We also discuss possible directions for new AI models and the future of neonatology with the rising power of AI, suggesting roadmaps for the integration of AI into neonatal intensive care units.

Maternal and Fetal Risk Factors for Neonatal Hypoxic-Ischemic Encephalopathy: A Retrospective Study

Background

Neonatal hypoxic-ischemic encephalopathy (HIE) leads to different degree of neurological sequelae. The incidence of HIE is relatively high, and the risk factors associated with HIE are still controversial. It is necessary to identify the risk factors associated with HIE.

Methods

A total of 258 neonates (110 HIE patients and 148 controls) were enrolled in this study. The characteristics of pregnant women and fetuses during pregnancy and delivery were compared between HIE patients and controls, and the risk factors of HIE were analyzed.

Results

The proportions of premature infants, low-birth-weight infants and the levels of 1-minute Apgar score, 5-minute Apgar score in HIE group were significantly lower than those in control group, while the proportion of amniotic fluid contamination in the HIE group was significantly higher than those of the controls. When HIE was taken as the end point of 1-minute Apgar score, and 5-minute Apgar score, the cut-off value of 1-minute Apgar score was 3, and 5-minute Apgar score was 7 by receiver operating characteristic (ROC) curve analysis. The results of multivariate logistic regression analysis showed that low birth weight (<2.5 kg) (OR 1.780, 95% CI: 0.124-25.463, P=0.016), amniotic fluid contamination (OR 3.223, 95% CI: 1.049-9.901, P=0.041), low 1-minute Apgar score (≤3) (OR 92.425, 95% CI: 15.522-550.343, P<0.001), and low 5-minute Apgar score (≤7) (OR 12.641, 95% CI: 2.894-55.227, P=0.001) may increase risk of HIE. In addition, amniotic fluid contamination, low 1-minute Apgar score (≤3), and low 5-minute Apgar score (≤7) may increase risk of HIE among newborns born to women without previous childbearing history, but not in newborns born to women with previous childbearing history.

Conclusion

Low birth weight (<2.5 kg), amniotic fluid contamination, low 1-minute Apgar score (≤3), and 5-minute Apgar score (≤7) may increase risk of HIE.

Longitudinal perturbations of plasma nuclear magnetic resonance profiles in neonatal encephalopathy

BACKGROUND

Neonatal encephalopathy (NE) is a major cause of mortality and severe neurological disability in the neonatal period and beyond. We hypothesized that the degree of brain injury is reflected in the molecular composition of peripheral blood samples.

METHODS

A sub-cohort of 28 newborns included in the HYPOTOP trial was studied. Brain injury was assessed by magnetic resonance imaging (MRI) once per patient and neurodevelopment at 24 months of age was evaluated using the Bayley III Scales of Infant and Toddler Development. The nuclear magnetic resonance (NMR) profile of 60 plasma samples collected before, during, and after cooling was recorded.

RESULTS

In total, 249 molecular features were quantitated in plasma samples from newborns and postnatal age showed to affect detected NMR profiles. Lactate, beta-hydroxybutyrate, pyruvate, and three triglyceride biomarkers showed the ability to discern between different degrees of brain injury according to MRI scores. The prediction performance of lactate was superior as compared to other clinical and biochemical parameters.

CONCLUSIONS

This is the first longitudinal study of an ample compound panel recorded by NMR spectroscopy in plasma from NE infants. The serial determination of lactate confirms its solid position as reliable candidate biomarker for predicting the severity of brain injury.

IMPACT

The use of nuclear magnetic resonance (NMR) spectroscopy enables the simultaneous quantitation of 249 compounds in a small volume (i.e., 100 μL) of plasma. Longitudinal perturbations of plasma NMR profiles were linked to magnetic resonance imaging (MRI) outcomes of infants with neonatal encephalopathy (NE). Lactate, beta-hydroxybutyrate, pyruvate, and three triglyceride biomarkers showed the ability to discern between different degrees of brain injury according to MRI scores. Lactate is a minimally invasive candidate biomarker for early staging of MRI brain injury in NE infants that might be readily implemented in clinical guidelines for NE outcome prediction.