Improvement in the prediction of neonatal hypoxic-ischemic encephalopathy with the integration of umbilical cord metabolites and current clinical makers

Tools

Item Type:	Article
Title:	Improvement in the prediction of neonatal hypoxic-ischemic encephalopathy with the integration of umbilical cord metabolites and current clinical makers
Creators Name:	O'Boyle, D.S., Dunn, W.B., O'Neill, D., Kirwan, J.A., Broadhurst, D.I., Hallberg, B., Boylan, G.B. and Murray, D.M.
Abstract:	OBJECTIVE: To validate our previously identified candidate metabolites, and to assess the ability of these metabolites to predict hypoxic-ischemic encephalopathy (HIE) both individually and combined with clinical data. STUDY DESIGN: Term neonates with signs of perinatal asphyxia, with and without HIE, and matched controls were recruited prospectively at birth from two large maternity units. Umbilical cord blood was collected for later batch metabolomic analysis by mass spectroscopy along with clinical details. The optimum selection of clinical and metabolites features with the ability to predict the development of HIE was determined using logistic regression modelling and machine learning techniques. Outcome of HIE was determined by clinical Sarnat grading and confirmed by EEG grade at 24 hours. RESULTS: Fifteen of 27 candidate metabolites showed significant alteration in infants with PA or HIE when compared with matched controls. Metabolomic data predicted the development of HIE with an AUC of 0.67 (95% CI: 0.62-0.71). Lactic acid and alanine were the primary metabolite predictors for the development of HIE, and when combined with clinical data, gave an AUC of 0.96 (95% CI: 0.92 - 0.95). CONCLUSION: By combining clinical and metabolic data, accurate identification of infants who will develop HIE is possible shortly after birth, allowing early initiation of therapeutic hypothermia.
Keywords:	Alanine, Apgar Score, Asphyxia Neonatorum, Biomarkers, Brain Hypoxia-Ischemia, Case-Control Studies, Electroencephalography, Fetal Blood, Lactic Acid, Logistic Models, Machine Learning, Metabolomics, Predictive Value of Tests, Prospective Studies, Resuscitation, Sensitivity and Specificity
Source:	Journal of Pediatrics
ISSN:	0022-3476
Publisher:	Elsevier
Volume:	229
Page Range:	175-181
Date:	February 2021
Official Publication:	https://doi.org/10.1016/j.jpeds.2020.09.065
PubMed:	View item in PubMed

Repository Staff Only: item control page