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. Author manuscript; available in PMC: 2018 Nov 6.
Published in final edited form as: J Perinatol. 2018 Jun 21;38(9):1242–1245. doi: 10.1038/s41372-018-0156-1

Heart rate ranges in premature neonates using high resolution physiologic data

Corrie J Alonzo 1, Vijay P Nagraj 2, Jenna V Zschaebitz 1, Douglas E Lake 3, J Randall Moorman 3, Michael C Spaeder 1,3
PMCID: PMC6218802  NIHMSID: NIHMS994469  PMID: 29925866

Abstract

Objective

There are limited evidence-based published heart rate ranges for premature neonates. We determined heart rate ranges in premature neonates based on gestational and post-menstrual age.

Study Design

Retrospective observational study of premature neonates admitted to the neonatal intensive care unit at the University of Virginia between January 2009 and October 2015. We included gestational ages between 23 0/7 weeks and 34 6/7 weeks. We stratified data by gestational and post-menstrual age groups.

Results

Over two billion heart rate values in 1703 neonates were included in our study. We established percentile-based reference ranges based on gestational and post-menstrual age. Our results demonstrate a slight increase in the initial weeks after birth, followed by a gradual decline with age. The baseline heart rate is lower with advancing gestational age.

Conclusions

Knowing heart rate reference ranges in the premature neonatal population can be helpful in the bedside assessment of the neonate.

Introduction

In 2015, 383,128 premature neonates (< 37 weeks gestation) were born in the United States [1]. Premature neonates often require care in a neonatal intensive care unit (NICU) where routine measurement of heart rate (HR) is performed. Published guidelines exist for normal reference ranges in healthy term neonates [24], but not in premature neonates.

Clinical decision-making is often influenced by the presence of abnormal vital signs, so knowing what is truly normal is paramount. Identifying and using accurate, evidenced-based ranges can appropriately guide clinical care decisions and perhaps improve management strategies and avoid unnecessary treatments. We sought to establish HR reference ranges in premature neonates based on gestational age (GA) and post-menstrual age (PMA) in a large cohort of neonates.

Patients and methods

The Institutional Review Board at the University of Virginia School of Medicine approved this study. We performed a retrospective observational cohort study of all neonates admitted to the NICU at the University of Virginia Children’s Hospital between January 2009 and October 2015. We included neonates with GAs between 23 0/7 weeks and 34 6/7 weeks. There were no exclusion criteria. We collected HR data on a dedicated computer cluster. HR values were captured every 2 seconds from the bedside monitor, and analyzed in all neonates until a PMA of 38 6/7 weeks.

Values of HR were stratified by both GA and PMA. Average values for each stratum were calculated and used to generate a heat map. We further sub-divided the data into four clinically relevant GA groups: 23 0/7–25 6/7 weeks (group 1), 26 0/7–28 6/7 weeks (group 2), 29 0/7–31 6/7 weeks (group 3), and 32–34 6/7 weeks (group 4). Percentile curves and tables were constructed for each group.

Results

A total of 2,002,756,757 data points from 1703 premature neonates were included in our study. The median GA was 31 weeks (interquartile range 28–33 weeks). Table 1 shows patient demographics including GA, sex, birth weight, size, race/ethnicity, and delivery type.

Table 1.

Patient demographics are shown

Characteristic Number (%)
Gestational age
 23 0/7–25 6/7 weeks 212 (12%)
 26 0/7–28 6/7 weeks 259 (15%)
 29 0/7–31 6/7 weeks 392 (23%)
 32–34 6/7 weeks 840 (49%)
 Total 1703
Sex
 Male 927 (54%)
 Female 776 (46%)
Birth weight
 ELBW (< 1000 g) 349 (20%)
 VLBW (< 1500 g) 388 (23%)
 LBW (< 2500 g) 849 (50%)
 BW> = 2500 g 117 (7%)
Birth size
 AGA 957 (56%)
 LGA 36 (2%)
 SGA 161 (10%)
 Unknown 549 (32%)
Race/ethnicity
 Caucasian 1145 (67%)
 African American 348 (21%)
 Hispanic 89 (5%)
 Asian 18 (1%)
 Other 53 (3%)
 Unknown 50 (3%)
Delivery type
 Vaginal delivery 670 (39%)
 Elective Cesarean Section 114 (7%)
 Urgent Cesarean Section 630 (37%)
 Emergent Cesarean Section 230 (14%)
 Unknown 59 (3%)

SGA defined as birth weight < 10th percentile, AGA defined as birth weight between 10–90th percentile, and LGA defined as birth weight > 10th percentile

We plotted average HR values for each GA/PMA strata to create a heat map (Fig. 1). This demonstrates a slight increase in HR after birth followed by a small and gradual decline with PMA. It also demonstrates a lower baseline HR with advancing GA.

Fig. 1.

Fig. 1

Heat map of average heart rates based on gestational age and post-menstrual age

Figure 2 shows the HR percentiles for each GA group with the 5th–95th percentiles displayed as a function of PMA, demonstrating an overall HR increase during the initial weeks after birth followed by a gradual decline with age. Table 2 shows a HR reference table with 5th and 95th percentile data points listed.

Fig. 2.

Fig. 2

Heart rate percentiles for each gestational age group displayed as a function of post-menstrual age

Table 2.

Heart rate percentile reference table is shown

Post-menstrual age
Gestational age HR percentile 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38
23 5th 135 139 139 140 140 141 141 139 137 137 139 139 136 133 130 129
95th 177 179 180 180 181 180 181 184 183 182 186 185 187 188 187 189
24 5th 139 140 141 141 141 138 138 138 137 135 135 132 133 131 130
95th 175 181 181 181 183 182 182 184 185 184 184 185 186 184 185
25 5th 137 142 141 141 138 136 137 138 135 137 135 132 132 131
95th 176 180 180 181 181 181 181 182 184 184 184 184 182 182
26 5th 134 143 143 137 140 137 137 139 139 135 135 134 127
95th 176 182 183 181 181 183 184 184 183 182 182 184 182
27 5th 134 143 144 141 139 140 138 135 134 131 131 124
95th 178 182 184 182 183 184 183 184 185 183 184 184
28 5th 131 140 141 142 140 140 138 136 133 128 125
95th 176 184 184 184 185 185 185 186 185 184 183
29 5th 130 141 142 141 143 142 139 137 136 134
95th 177 182 182 183 185 185 186 186 185 185
30 5th 126 135 137 139 140 135 134 132 128
95th 176 179 180 182 184 184 183 176 172
31 5th 123 134 137 139 135 130 126 113
95th 176 180 182 185 186 185 188 185
32 5th 123 133 136 135 133 130 127
95th 175 178 182 183 185 184 178
33 5th 119 130 133 133 132 131
95th 172 179 184 186 185 182
34 5th 117 127 128 128 129
95th 172 179 183 184 185

Gestational age and post-menstrual age are shown in weeks.

Discussion

There are limited evidenced-based HR reference ranges available for the premature neonatal population. By providing a heat map, percentile curves, and a reference table for this patient population, clinicians will be more informed of appropriate HR ranges, which can potentially lead to improvement in management strategies and more prompt identification of patients in need of immediate medical attention.

Considerable attention has been placed in recent years on the issue of alarm fatigue. Although determining appropriate lower limits for alarm settings in the neonatal population was outside the scope of this project, our data provides some interesting insights that may inform future study. For example, among neonates with PMA of 38 weeks, irrespective of GA, we found the 1st percentile and 0.1st percentile of HRs to be 95 and 72, respectively. Efforts to evaluate the impact of different lower limits on alarm fatigue and response to clinically significant events are needed.

This study has substantial strengths. We have included over two billion data points in our analysis and our patient population includes GAs typically seen in NICUs. There are some additional limitations as well. Our patient demographics, such as race, are not representative of all other institutions. It is possible some of these demographic factors unknowingly influence HR values. It is also possible that confounders other than vasoactive use and supplemental oxygen exist in this dataset.

Analysis of HR values from a large cohort of premature neonates has provided a useful heat map, percentile curves, and a reference tables in this vulnerable population. These results can be used at the bedside while assessing the critically ill neonate. Additional research efforts will focus on the development of an interactive website that will be easily accessible to all care providers.

Acknowledgements

We acknowledge Karen Fairchild and Robert Sinkin for their helpful insights into the completion of this study.

Footnotes

Compliance with ethical standards

Conflict of interest Randall Moorman, MD, is the Chief Medical Officer of Advanced Medical Predictive Devices, Diagnostics, and Displays. All other authors declare that they have no conflict of interest.

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