Low Variability of Blood Pressure Predicts Abnormal Electroencephalogram in Infants with Hypoxic Ischemic Encephalopathy.

OBJECTIVE: This study aimed to evaluate the role of an objective physiologic biomarker, arterial blood pressure variability, for the early identification of adverse short-term electroencephalogram (EEG) outcomes in infants with hypoxic-ischemic encephalopathy (HIE). STUDY DESIGN: In this multicenter observational study, we analyzed blood pressure of infants meeting these criteria: (1) neonatal encephalopathy determined by modified Sarnat exam, (2) continuous mean arterial blood pressure (MABP) data between 18 and 27 hours after birth, and (3) continuous EEG performed for at least 48 hours. Adverse outcome was defined as moderate-severe grade EEG at 48 hours. Standardized signal preprocessing was used; the power spectral density was computed without interpolation. Multivariate binary logistic regression was used to identify which MABP time and frequency domain metrics provided improved predictive power for adverse outcomes compared with standard clinical predictors (5-minute Apgar score and cord pH) using receiver operator characteristic analysis. RESULTS: Ninety-one infants met inclusion criteria. The mean gestational age was 38.4 ± 1.8 weeks, the mean birth weight was 3,260 ± 591 g, 52/91 (57%) of infants were males, the mean cord pH was 6.95 ± 0.21, and 10/91 (11%) of infants died. At 48 hours, 58% of infants had normal or mildly abnormal EEG background and 42% had moderate or severe EEG backgrounds. Clinical predictor variables (10-minute Apgar score, Sarnat stage, and cord pH) were modestly predictive of 48 hours EEG outcome with area under curve (AUC) of 0.66 to 0.68. A composite model of clinical and optimal time- and frequency-domain blood pressure variability had a substantially improved AUC of 0.86. CONCLUSION: Time- and frequency-domain blood pressure variability biomarkers offer a substantial improvement in prediction of later adverse EEG outcomes over perinatal clinical variables in a two-center cohort of infants with HIE. KEY POINTS: · Early outcome prediction in HIE is suboptimal.. · Patterns in blood pressure physiology may be predictive of short-term outcomes.. · Early time- and frequency-domain measures of blood pressure variability predict short-term EEG outcomes in HIE infants better than perinatal factors alone..

Blood pressure extremes and severe IVH in preterm infants.

BACKGROUND: The optimal upper and lower limits of blood pressure in preterm infants are not known. Exceeding these thresholds may contribute to intraventricular hemorrhage (IVH). METHODS: Preterm infants born ≤30 weeks GA were identified. Infants had continuous measurement of mean arterial blood pressure (MABP) for 7 days and cranial ultrasound imaging. IVH was classified as severe IVH (grade 3/4), no severe IVH (no IVH; grade 1/2), or no IVH. Mean ± SEM MABP values from hours 1-168 were calculated and sorted into bins 2 mm Hg wide. The normalized proportion of each recording spent in each bin was then calculated. Candidate limits were identified by comparison of MABP distribution in those with severe IVH vs. those without severe IVH. RESULTS: Eighty-five million measurements were made from 157 infants. Mean EGA was 25.2 weeks; mean BW was 749 g; 65/157 female; inotrope use in 59/157; grade 3/4 IVH in 29/157. Infants with severe IVH spent significantly more time with extreme MABP measurements (<23 mm Hg or >46 mm Hg) compared to those without severe IVH (12% vs. 8% of recording, p = 0.02). CONCLUSIONS: Infants who developed severe IVH had substantially more unstable MABP and spent a significantly greater period of time with MABP outside of the optimal range.

Cardiovascular oscillations at the bedside: early diagnosis of neonatal sepsis using heart rate characteristics monitoring.

We have applied principles of statistical signal processing and nonlinear dynamics to analyze heart rate time series from premature newborn infants in order to assist in the early diagnosis of sepsis, a common and potentially deadly bacterial infection of the bloodstream. We began with the observation of reduced variability and transient decelerations in heart rate interval time series for hours up to days prior to clinical signs of illness. We find that measurements of standard deviation, sample asymmetry and sample entropy are highly related to imminent clinical illness. We developed multivariable statistical predictive models, and an interface to display the real-time results to clinicians. Using this approach, we have observed numerous cases in which incipient neonatal sepsis was diagnosed and treated without any clinical illness at all. This review focuses on the mathematical and statistical time series approaches used to detect these abnormal heart rate characteristics and present predictive monitoring information to the clinician.

Periodic heart rate decelerations in premature infants.

The pacemaking system of the heart is complex; a healthy heart constantly integrates and responds to extracardiac signals, resulting in highly complex heart rate patterns with a great deal of variability. In the laboratory and in some pathological or age-related states, however, dynamics can show reduced complexity that is more readily described and modeled. Reduced heart rate complexity has both clinical and dynamical significance - it may provide warning of impending illness or clues about the dynamics of the heart's pacemaking system. In this paper, we describe simple and interesting heart rate dynamics that we have observed in premature human infants - reversible transitions to large-amplitude periodic oscillations - and we show that the appearance and disappearance of these periodic oscillations can be described by a simple mathematical model, a Hopf bifurcation.

fMRI evidence that the neural basis of response inhibition is task-dependent.

Event-related fMRI was used to investigate the hypothesis that neural activity involved in response inhibition depends upon the nature of the response being inhibited. Two different Go/No-go tasks were compared-one with a high working memory load and one with low. The 'simple' Go/No-go task with low working memory load required subjects to push a button in response to green spaceships but not red spaceships. A 'counting' Go/No-go task (high working memory load) required subjects to respond to green spaceships as well as to those red spaceships preceded by an even number of green spaceships. In both tasks, stimuli were presented every 1.5 s with a 5:1 ratio of green-to-red spaceships. fMRI group data for each task were analyzed using random effects models to determine signal change patterns associated with Go events and No-go events (corrected P< or =0.05). For both tasks, Go responses were associated with signal change in the left primary sensorimotor cortex, supplementary motor area (SMA) proper, and anterior cerebellum (right>left). For the simple task, No-go events were associated with activation in the pre-SMA; the working memory-loaded 'counting' task elicited additional No-go activation in the right dorsolateral prefrontal cortex. The findings suggest that neural contributions to response inhibition may be task dependent; the pre-SMA appears necessary for inhibition of unwanted movements, while the dorsolateral prefrontal cortex is recruited for tasks involving increased working memory load.