Factors Affecting Heart Rate Variability in Preterm Infants

Cabal, Luis A; Siassi, Bijan; Zanini, Bernardino; Hodgman, Joan E.; Hon, Edward E.

doi:10.1097/00006254-198011000-00006

Cited by 15 publications

(24 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For example, the HRV parameters among our premature infants were all somewhat lower than those reported by those evaluating HRV in term infants (50,51), consistent with reports that HRV parameters are lower among infants of lower postconceptional age (21,22,25,36,50,(52)(53)(54). Likewise, our results for the values of HRV parameters in pediatric oncology patients are similar to those found in earlier studies (13); values for HRV parameters in oncology patients may be lower than healthy children of similar ages due to cardiac toxicity of anthracycline chemotherapy, pain or other symptoms (55).…”

Section: Hrv Outlier Methodssupporting

confidence: 89%

Heart Rate Variability: Impact of Differences in Outlier Identification and Management Strategies on Common Measures in Three Clinical Populations

2007

View full text Add to dashboard Cite

Heart rate variability (HRV) is reported increasingly in pediatric research, but different strategies used to identify and manage potential outlier beats impact HRV parameter values in adults and animals. Do they in pediatrics? To compare the impact of different strategies to identifying and managing outliers, we used interbeat interval (IBI) data from three different populations: 10 stable premature infants, 33 stable pediatric oncology patients, and 15 healthy adults. Five commonly reported HRV parameters were compared using three identification and two management strategies to filter potential outliers. The three populations had different resting heart rates: 155 Ϯ 9 beats per minute (bpm) in infants, 105 Ϯ 17 bpm in children, and 87 Ϯ 12 bpm in adults. All three identification strategies flagged fewer than 2% of IBIs; the threshold identification strategy, excluding IBIs denoting heart rates Ͻ30 or Ͼ300 bpm, identified significantly fewer outliers than the other two strategies and generated higher HRV parameters in all populations (p Ͻ 0.001). There were no significant differences in HRV parameters calculated by managing identified outliers by "tossing" them versus "interpolating" values. Different strategies for identifying potential outliers are associated with significant differences in HRV parameters. Pediatric researchers who report HRV should detail their outlier filtering strategies. (Pediatr Res 62: 337-342, 2007) H eart rate variability (HRV) measurements are increasingly used by researchers to describe physiology, provide prognostic information, and measure response to therapies (1). HRV is the beat-to-beat variation of the interbeat interval (IBI) of the ECG (ECG). This variability is modulated primarily by the sympathetic and parasympathetic autonomic nervous system (2-4). Methods for calculating time and frequency-domain parameters of HRV were recommended by the Task Force of the European Society of Cardiology (5) and these methods have been widely adopted (6).The SD of the IBI (SDNN) is the most widely used measure of HRV, followed by the root mean square of the difference of successive IBIs (RMSSD). Other commonly used parameters include pNN50 (the percentage of IBIs that differ by more than 50 ms from the previous IBI), and parameters from power spectral analyses based on fast Fourier transformations, particularly low frequency (LF) and high frequency (HF) power. HF oscillations (0.15-0.4 Hz) are thought to be markers of parasympathetic and particularly vagal autonomic tone. LF oscillations (0.04 -0.15 Hz) reflect sympathetic and to a lesser extent, parasympathetic tone (7).Increases in HRV parameters, up to a point, reflect adaptability, resilience and well being, while decreases in HRV reflect rigidity, poor health status and a poor prognosis in both adults and children with a wide variety of clinical conditions (8 -23). In children HRV generally increases between the birth and fourteen years of age as the autonomic nervous system matures (24,25). In adults HRV generally decreases...

show abstract

Section: Hrv Outlier Methodssupporting

confidence: 89%

Heart Rate Variability: Impact of Differences in Outlier Identification and Management Strategies on Common Measures in Three Clinical Populations

2007

View full text Add to dashboard Cite

show abstract

“…These fHRV properties are differentially affected by LPS-induced fetal and neonatal inflammatory response (Lake et al 2002, Stone et al 2013. Decreased fetal and neonatal HRV and transient repetitive heart rate decelerations coincide with or precede clinical signs of sepsis (Rudolph et al 1965, Cabal et al 1980, Fairchild and O'Shea 2010. This may be due to an altered vagal tone with intermittent vagal firing in the setting of a systemic fetal inflammatory response during sepsis (Fairchild and O'Shea 2010).…”

Section: Discussionmentioning

confidence: 99%

Does heart rate variability reflect the systemic inflammatory response in a fetal sheep model of lipopolysaccharide-induced sepsis?

et al. 2015

View full text Add to dashboard Cite

Fetal inflammatory response occurs during chorioamnionitis, a frequent and often subclinical inflammation associated with increased risk for brain injury and life-lasting neurologic deficits. No means of early detection exist. We hypothesized that systemic fetal inflammation without septic shock will be reflected in alterations of fetal heart rate (FHR) variability (fHRV) distinguishing baseline versus inflammatory response states.In chronically instrumented near-term fetal sheep (n = 24), we induced an inflammatory response with lipopolysaccharide (LPS) injected intravenously (n = 14). Ten additional fetuses served as controls. We measured fetal plasma inflammatory cytokine IL-6 at baseline, 1, 3, 6, 24 and 48 h. 44 fHRV measures were determined continuously every 5 min using continuous individualized multi-organ variability analysis (CIMVA). CIMVA creates an fHRV measures matrix across five signal-analytical domains, thus describing complementary properties of fHRV. Using principal component analysis (PCA), a widely used technique for dimensionality reduction, we derived and quantitatively compared the CIMVA fHRV PCA signatures of inflammatory response in LPS and control groups.In the LPS group, IL-6 peaked at 3 h. In parallel, PCA-derived fHRV composite measures revealed a significant difference between LPS and control group at different time points. For the LPS group, a sharp increase compared to baseline levels was observed between 3 h and 6 h, and then abating to baseline levels, thus tracking closely the IL-6 inflammatory profile. This pattern was not observed in the control group. We also show that a preselection of fHRV measures prior to the We propose a fHRV composite measure that correlates well with levels of inflammation and tracks well its temporal profile. Our results highlight the potential role of HRV to study and monitor the inflammatory response non-invasively over time.

show abstract

“…An example of a simple time domain index is the SD of (R-R) i intervals within a period. This index has been thought to result from the dynamic balance between the sympathetic and parasympathetic components of the autonomic nervous system (36). The SD of (R-R) i intervals or SBP-to-SBP values within a certain period reflects the total variance of the signal.…”

Section: Discussionmentioning

confidence: 99%

Cardiovascular Fluctuations and Transfer Function Analysis in Stable Preterm Infants

Andriessen

2003

Pediatric Research

View full text Add to dashboard Cite

To examine the baroreceptor reflex function, a beat-to-beat analysis between systolic blood pressure (SBP) and R-R interval fluctuations was studied in 10 stable appropriate-for-gestational age preterm infants (range, 27.2-33.7 wk) in the first postnatal week during quiet sleep. Spectral power analysis, using fast Fourier transform, and transfer functions (gain and phase difference) between SBP and R-R fluctuations were estimated in a low-frequency band (LF, 0.03-0.2 Hz) and high-frequency band (HF defined as the frequency band between the 10th and 90th centiles of the individual respiratory frequency). The LF/HF ratio reflects the sympathovagal balance. The mean frequency (ϮSD) of LF peaks was centered at 0.07 Ϯ 0.02 Hz. The mean frequency (ϮSD) of the individual HF band was 0.82 Ϯ 0.21 Hz. The LF/HF ratio in the R-R interval series [median, 29; interquartile range (IQR), 16 -40] was higher than in the SBP series (median,8; IQR,(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14). The gain between R-R interval and SBP fluctuations (median, 4.2 ms/mm Hg; IQR, 2.4 -5.0) in the LF band was higher than in the HF band (median, 1.7 ms/mm Hg; IQR, 1.4 -3.0). SBP fluctuations lead R-R interval fluctuations in the LF band with a median phase difference of ϩ96° (IQR,. At LF the fluctuations in SBP precede changes in R-R interval with a time delay of 3.8 s. These observations indicate a dominant role of the sympathetic system in stable preterm infants in comparison with published adult values. Cross-spectral analysis allows a test for tracking the development of the sympathetic system in neonates. Abbreviations BP, blood pressure BR, baroreceptor reflex BW, birth weight GA, gestational age HF, high frequency (frequency between P-10 and P-90 of the individual respiratory frequency) HR, heart rate IQR, interquartile range (P-25-P-75) LF, low frequency (0.03-0.2 Hz) PCA, postconceptional age R-R, R-R interval length SBP, systolic blood pressureThe autonomic nervous system plays a key role in BP control by adapting HR and total peripheral resistance. Multiple physiologic mechanisms are responsible for fluctuations in HR and BP: respiration, BR, and thermoregulation. Power spectral analysis of the R-R interval series permits us to evaluate aspects of cardiovascular autonomic control (1). A HF spectral peak occurs at the respiratory frequency. A LF spectral peak, synchronous with Mayer waves in BP, occurs around 0.1 Hz and is attributed to the BR (2). Very low frequency oscillations (below 0.03 Hz) are ascribed to peripheral resistance fluctuations caused by thermoregulation. HF fluctuations are associated with the parasympathetic system, whereas LF fluctuations are related to sympathetic and parasympathetic activity (3). The LF/HF ratio reflects the sympathovagal balance (3).The BR can be considered a closed-loop circuit: BP fluctuations affect R-R interval fluctuations and vice versa (4). Spectral analysis technique and BR activation through postural change enable the assessment of BR gain/sensitivity in human adults (5, 6). Cardiovascular...

show abstract

Factors Affecting Heart Rate Variability in Preterm Infants

Cited by 15 publications

References 0 publications

Heart Rate Variability: Impact of Differences in Outlier Identification and Management Strategies on Common Measures in Three Clinical Populations

Heart Rate Variability: Impact of Differences in Outlier Identification and Management Strategies on Common Measures in Three Clinical Populations

Does heart rate variability reflect the systemic inflammatory response in a fetal sheep model of lipopolysaccharide-induced sepsis?

Cardiovascular Fluctuations and Transfer Function Analysis in Stable Preterm Infants

Contact Info

Product

Resources

About