Early Postnatal Heart Rate Variability in Healthy Newborn Infants

Abstract

Background Despite the increasing interest in fetal and neonatal heart rate variability (HRV) analysis and its potential use as a tool for early disease stratification, no studies have previously described the normal trends of HRV in healthy babies during the first hours of postnatal life. Methods We prospectively recruited 150 healthy babies from the postnatal ward and continuously recorded their electrocardiogram during the first 24 h after birth. Babies were included if born in good condition and stayed with their mother. Babies requiring any medication or treatment were excluded. Five-minute segments of the electrocardiogram (non-overlapping time-windows) with more than 90% consecutive good quality beats were included in the calculation of hourly medians and interquartile ranges to describe HRV trends over the first 24 h. We used multilevel mixed effects regression with auto-regressive covariance structure for all repeated measures analysis and t -tests to compare group differences. Non-normally distributed variables were log-transformed. Results Nine out of 16 HRV metrics (including heart rate) changed significantly over the 24 h [Heart rate p < 0.01; Standard deviation of the NN intervals p = 0.01; Standard deviation of the Poincaré plot lengthwise p < 0.01; Cardiac sympathetic index (CSI) p < 0.01; Normalized high frequency power p = 0.03; Normalized low frequency power p < 0.01; Total power p < 0.01; HRV index p = 0.01; Parseval index p = 0.03], adjusted for relevant clinical variables. We observed an increase in several HRV metrics during the first 6 h followed by a gradual normalization by approximately 12 h of age. Between 6 and 12 h of age, only heart rate and the normalized low frequency power changed significantly, while between 12 and 18 h no metric, other than heart rate, changed significantly. Analysis with multilevel mixed effects regression analysis (multivariable) revealed that gestational age, reduced fetal movements, cardiotocography and maternal chronic or pregnancy induced illness were significant predictors of several HRV metrics. Conclusion Heart rate variability changes significantly during the first day of life, particularly during the first 6 h. The significant correlations between HRV and clinical risk variables support the hypothesis that HRV is a good indicator of overall wellbeing of a baby and is sensitive to detect birth-related stress and monitor its resolution over time.

Heart Rate Variability in the Perinatal Period: A Critical and Conceptual Review

“…In the last 40 years, heart rate variability (HRV) has emerged as a reliable and non-invasive measure to monitor preterm and term newborns (Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology, 1996). HRV evaluates the heart rate (HR) fluctuation-the variability of the time intervals between successive heartbeats-, and during the years, more and more techniques have appeared to improve its analysis, from which several metrics can be extracted ( Table 1; Bravi et al, 2011;Thiriez et al, 2015;Javorka et al, 2017;Oliveira et al, 2019b;Patural et al, 2019).…”

“…Several studies showed that HRV correlates with the newborn's stress and stress-related behaviors (Gardner et al, 2018;Hashiguchi et al, 2020), and that it could predict the baby's overall wellbeing and future neurological development. HRV could also accurately identify short-and long-term complications, such as the risk of sepsis (Javorka et al, 2017;Oliveira et al, 2019b;Kumar et al, 2020). HRV was also able to reveal the impact of prenatal stress on fetal brain development (Frasch et al, 2007;Lobmaier et al, 2020).…”

“…HRV may provide such useful insights since it correlates with the ANS development and functioning. The ANS regulates organic development and connects with the organism's ability to cope with stressors, as well as with cognitive and emotional development (Thayer et al, 2012;Jennings et al, 2015;Schneider et al, 2018;Oliveira et al, 2019b).…”

“…This focused review is divided into three sections: (1) the first section describes briefly the HRV metrics and examines the relationship between ANS and HRV in fetuses and newborns; (2) the second section examines the technology available in the NICU, how to monitor HRV efficiently, and the usefulness of real-time HRV; and (3) the third and final section will summarize the main findings and outline future perspectives for the clinical use of real-time HRV in the neonatal field, with a brief subsection about its usefulness in low-income countries. (Hoyer et al, 2013(Hoyer et al, , 2019Uhrikova et al, 2015;Pichot et al, 2016;Massaro et al, 2017;Shaffer and Ginsberg, 2017;Herry et al, 2019;Oliveira et al, 2019b;Patural et al, 2019;Frasch et al, 2020). From the work of the Task Force for HRV analysis in 1996, which constitutes the foundation for the majority of the papers on HRV, many metrics are developed to describe HRV, and they can be classified into three categories: time-domain, frequency-domain, and non-linear metrics ( Table 1).…”

“…The time-domain metrics are then calculated through statistical or geometric methods. The statistical methods consist in applying operations, such as mean, standard deviation, or square root, on: the direct NN interval measurements, thus obtaining metrics, such as standard deviation of RR (SDRR), standard deviation of NN (SDNN), standard deviation of the average NN (SDANN), and standard deviation of NN (SDNN) Index, and the difference between NN intervals, thus obtaining metrics, such as root mean square of consecutive RR interval differences (RMSSD), NN50, pNN50, and pNN20 (Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology, 1996;Oliveira et al, 2019b;Patural et al, 2019).…”