ABSTRACT
OBJECTIVE: To determine whether ventilator-related fluctuations in cerebral blood volume (CBV) are associated with cerebral pressure passivity. STUDY DESIGN: In a prospective study of newborns undergoing positive-pressure ventilation, we calculated coherence between continuous mean arterial pressure (MAP) and cerebral near-infrared spectroscopy hemoglobin difference (HbD). Significant HbD-MAP coherence indicated cerebral pressure passivity. CBV changes were measured as the spectral power of total hemoglobin (SHbT) at the ventilator frequency. A regression model tested whether SHbT predicts cerebral pressure passivity and/or death/brain injury, controlling for birth gestational age and other factors. RESULTS: We studied 68 subjects with prematurity (n = 19), congenital heart disease (n = 11), and hypoxic-ischemic encephalopathy (n = 38). SHbT, sedative use, and pCO2 were positively associated, and circulating hemoglobin negatively associated, with cerebral pressure passivity (p < 0.001), which was positively associated with brain injury (p < 0.001). CONCLUSION: In sick newborns, ventilator-related CBV fluctuations may predispose to cerebral pressure passivity, which may predispose to an adverse neonatal outcome.
Subject(s)
Critical Illness , Infant, Premature , Blood Pressure , Cerebrovascular Circulation , Homeostasis , Humans , Infant , Infant, Newborn , Prospective StudiesABSTRACT
We studied the effect of EKG sampling rate on heart rate variability (HRV) analysis. We acquired EKG from four term hypoxic-ischemic encephalopathic infants undergoing therapeutic hypothermia. The EKG signal was acquired continuously for 4 days from the cardiorespiratory monitor through the analog port. The following HRV metrics were calculated: normalized low-frequency (nLF), normalized high-frequency (nHF), low-frequency (LF), high-frequency (HF), short-term detrended fluctuation analysis (DFA) exponent (αs), long-term DFA exponent (αL), root mean square (RMS) short (RMSS), and RMS long (RMSL). In addition, heart rate was used. These metrics were calculated for EKG acquired at 1 KHz (served as reference, EKGref) as well as from EKGs downsampled at 500 Hz (EKG500), 250 Hz (EKG250), and 125 Hz (EKG125). The bedside monitors were simultaneously sending the EKG to a data warehouse, storing the EKG (EKGDWH) at 250 Hz. All HRV metrics were also calculated for the EKGDWH. The comparison between HRV metrics calculated from EKGref and downsampled EKG (EKG500, EKG250, EKG125) was made with intraclass correlation coefficient (r). The comparisons of HRV metrics between EKG250 and EKGDWH were also made with ICC. Our results show that HRV calculated with EKGref and from downsampled EKG were highly correlated (r>0.8 for all comparisons, P<; 0.001). HRV metrics from EKG250 and EKGDWH were also significantly correlated (r=0.7, P<; 0.001) for all metrics except for HF (r=0.276). These data show that HF power is compromised in the EKGDWH signal and caution must be exercised in interpreting the HF power calculated from this EKG.
Subject(s)
Electrocardiography , Hypoxia-Ischemia, Brain , Algorithms , Heart Rate , Humans , Infant, NewbornABSTRACT
The objective of this article is to understand the complex role of the central autonomic nervous system in normal and complicated fetal-neonatal transition and how autonomic nervous system dysfunction can lead to brain injury. The central autonomic nervous system supports coordinated fetal transitional cardiovascular, respiratory, and endocrine responses to provide safe transition of the fetus at delivery. Fetal and maternal medical and environmental exposures can disrupt normal maturation of the autonomic nervous system in utero, cause dysfunction, and complicate fetal-neonatal transition. Brain injury may both be caused by autonomic nervous system failure and contribute directly to autonomic nervous system dysfunction in the fetus and newborn. The central autonomic nervous system has multiple roles in supporting transition of the fetus. Future studies should aim to improve real-time monitoring of fetal autonomic nervous system function and in supporting typical autonomic nervous system development even under complicated conditions.
Subject(s)
Autonomic Nervous System Diseases , Autonomic Nervous System , Central Nervous System , Fetal Diseases , Infant, Newborn, Diseases , Autonomic Nervous System/growth & development , Autonomic Nervous System/metabolism , Autonomic Nervous System/physiopathology , Autonomic Nervous System Diseases/diagnosis , Autonomic Nervous System Diseases/metabolism , Autonomic Nervous System Diseases/physiopathology , Central Nervous System/growth & development , Central Nervous System/metabolism , Central Nervous System/physiopathology , Fetal Diseases/diagnosis , Fetal Diseases/metabolism , Fetal Diseases/physiopathology , Humans , Infant, Newborn , Infant, Newborn, Diseases/diagnosis , Infant, Newborn, Diseases/metabolism , Infant, Newborn, Diseases/physiopathologyABSTRACT
BACKGROUND: Necrotizing enterocolitis (NEC) is a complication of prematurity with a high mortality rate. Currently, there are no reliable biomarkers capable of identifying infants at risk for developing NEC. We sought to determine the autonomic nervous system antecedents of NEC in premature infants, using heart rate variability (HRV). MATERIALS AND METHODS: HRV was quantified by retrieving archived electrocardiogram (EKG) data from 30 premature infants from 4 days prior, through 4 days after, the clinical NEC diagnosis. HRV metrics were compared with those on the diagnosis day using the receiver operating characteristic (ROC) analysis. RESULTS: HRV metrics showed a depression of autonomic tone that preceded the clinical NEC diagnosis by 2 days, and which recovered to baseline by 2 days after diagnosis (area under the curve [AUC] < 0.7). The pattern of HRV change was significantly associated with the clinical severity of NEC (stage II vs. stage III). CONCLUSION: Our studies suggest that readily accessible metrics of autonomic depression might expedite the diagnosis of NEC and its severity in a clinically meaningful manner. Clearly, these studies need to be extended prospectively to determine the diagnostic utility of this approach.
Subject(s)
Autonomic Nervous System/physiopathology , Enterocolitis, Necrotizing/diagnosis , Infant, Premature, Diseases/diagnosis , Biomarkers , Case-Control Studies , Electrocardiography , Female , Heart Rate , Humans , Infant, Newborn , Infant, Premature , Logistic Models , Male , ROC CurveABSTRACT
OBJECTIVES: To develop a computerized algorithm to quantify fetal heart rate (FHR) variability and compare it to perinatologists' interpretation of FHR variability. METHODS: FHR variability was calculated using data from 30 women who had a fetal scalp electrode placed for a clinical indication, and compared to the assessment of FHR variability from four perinatologists who interpreted paper tracings of the same data. Inter-rater reliability was calculated and receiver-operator curve analysis was done. RESULTS: Correlation between the computer algorithm's assessment of variability and the perinatologists' assessment (0.27-0.68) was similar to the inter-rater reliability between perinatologists (0.33-0.72). CONCLUSIONS: A computer-based algorithm can assess FHR variability as well as expert clinicians.
