Prediction of outcome of hypoxic-ischemic encephalopathy in newborns undergoing therapeutic hypothermia using heart rate variability.

Objective: 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.

Study Design: 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 (RMS) 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 RMS. The encephalopathy grade and gender were used as control variables.

Results: 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.

Conclusions: HRV assessed by RMS can predict severity of brain injury in newborns with HIE.