To assess the use of continuous heart rate variability (HRV) as a predictor of brain injury severity in newborns with moderate to severe HIE that undergo therapeutic hypothermia.
Two cohorts of newborns (n1 = 55, n2 = 41) with moderate to severe hypoxic-ischemic encephalopathy previously treated with therapeutic hypothermia. HRV was characterized by root mean square in the short time scales (RMSS) during therapeutic hypothermia and through completion of rewarming. A logistic regression and Naïve Bayes models were developed to predict the MRI outcome of the infants using RMSS. The encephalopathy grade and gender were used as control variables.
For both cohorts, the predicted outcomes were compared with the observed outcomes. Our algorithms were able to predict the outcomes with an area under the receiver operating characteristic curve of about 0.8.
HRV assessed by RMSS can predict severity of brain injury in newborns with HIE.
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The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
Matlab code used to analyze the data is available upon request.
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This work was supported by internal special purpose funds available in the Prenatal Pediatrics Institute, by the Award Number P30 HD040677, UL1RR031988, and KL2 RR031987 from the National Center for Research Resources and by the American Heart Association Grant-in-Aid. We want to thank Patricia Saulino for her editorial assistance. We also want to thank Amaya Sinha for assisting in data cleaning.
The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Presacco, A., Chirumamilla, V.C., Vezina, G. et al. Prediction of outcome of hypoxic-ischemic encephalopathy in newborns undergoing therapeutic hypothermia using heart rate variability. J Perinatol (2023). https://doi.org/10.1038/s41372-023-01754-w