Prognostic accuracy of heart rate variability analysis in neonatal encephalopathy: a systematic review

BACKGROUND: Heart rate variability analysis offers real-time quantification of autonomic disturbance after perinatal asphyxia, and may therefore aid in disease stratification and prognostication after neonatal encephalopathy (NE). OBJECTIVE: To systematically review the existing literature on the accuracy of early heart rate variability (HRV) to predict brain injury and adverse neurodevelopmental outcomes after NE. DESIGN/METHODS: We systematically searched the literature published between May 1947 and May 2018. We included all prospective and retrospective studies reporting HRV metrics, within the first 7 days of life in babies with NE, and its association with adverse outcomes (defined as evidence of brain injury on magnetic resonance imaging and/or abnormal neurodevelopment at ≥1 year of age). We extracted raw data wherever possible to calculate the prognostic indices with confidence intervals. RESULTS: We retrieved 379 citations, 5 of which met the criteria. One further study was excluded as it analysed an already-included cohort. The 4 studies provided data on 205 babies, 80 (39%) of whom had adverse outcomes. Prognostic accuracy was reported for 12 different HRV metrics and the area under the curve (AUC) varied between 0.79 and 0.94. The best performing metric reported in the included studies was the relative power of high-frequency band, with an AUC of 0.94. CONCLUSIONS: HRV metrics are a promising bedside tool for early prediction of brain injury and neurodevelopmental outcome in babies with NE. Due to the small number of studies available, their heterogeneity and methodological limitations, further research is needed to refine this tool so that it can be used in clinical practice