Cerebral oxygen saturation and autoregulation during hypotension in extremely preterm infants.

BACKGROUND: The impact of the permissive hypotension approach in clinically well infants on regional cerebral oxygen saturation (rScO2) and autoregulatory capacity (CAR) remains unknown. METHODS: Prospective cohort study of blinded rScO2 measurements within a randomized controlled trial of management of hypotension (HIP trial) in extremely preterm infants. rScO2, mean arterial blood pressure, duration of cerebral hypoxia, and transfer function (TF) gain inversely proportional to CAR, were compared between hypotensive infants randomized to receive dopamine or placebo and between hypotensive and non-hypotensive infants, and related to early intraventricular hemorrhage or death. RESULTS: In 89 potentially eligible HIP trial patients with rScO2 measurements, the duration of cerebral hypoxia was significantly higher in 36 hypotensive compared to 53 non-hypotensive infants. In 29/36 hypotensive infants (mean GA 25 weeks, 69% males) receiving the study drug, no significant difference in rScO2 was observed after dopamine (n = 13) compared to placebo (n = 16). Duration of cerebral hypoxia was associated with early intraventricular hemorrhage or death.  Calculated TF gain (n = 49/89) was significantly higher reflecting decreased CAR in 16 hypotensive compared to 33 non-hypotensive infants. CONCLUSIONS: Dopamine had no effect on rScO2 compared to placebo in hypotensive infants. Hypotension and cerebral hypoxia are associated with early intraventricular hemorrhage or death. IMPACT: Treatment of hypotension with dopamine in extremely preterm infants increases mean arterial blood pressure, but does not improve cerebral oxygenation. Hypotensive extremely preterm infants have increased duration of cerebral hypoxia and reduced cerebral autoregulatory capacity compared to non-hypotensive infants. Duration of cerebral hypoxia and hypotension are associated with early intraventricular hemorrhage or death in extremely preterm infants. Since systematic treatment of hypotension may not be associated with better outcomes, the diagnosis of cerebral hypoxia in hypotensive extremely preterm infants might guide treatment.

Cerebral Oxygenation and Activity During Surgical Repair of Neonates With Congenital Diaphragmatic Hernia: A Center Comparison Analysis.

Background and aim: Neonatal brain monitoring is increasingly used due to reports of brain injury perioperatively. Little is known about the effect of sedatives (midazolam) and anesthetics (sevoflurane) on cerebral oxygenation (rScO2) and cerebral activity. This study aims to determine these effects in the perioperative period. Methods: This is an observational, prospective study in two tertiary pediatric surgical centers. All neonates with a congenital diaphragmatic hernia received perioperative cerebral oxygenation and activity measurements. Patients were stratified based on intraoperatively administrated medication: the sevoflurane group (continuous sevoflurane, bolus fentanyl, bolus rocuronium) and the midazolam group (continuous midazolam, continuous fentanyl, and continuous vecuronium). Results: Intraoperatively, rScO2 was higher in the sevoflurane compared to the midazolam group (84%, IQR 77-95 vs. 65%, IQR 59-76, p = < 0.001), fractional tissue oxygen extraction was lower (14%, IQR 5-21 vs. 31%, IQR 29-40, p = < 0.001), the duration of hypoxia was shorter (2%, IQR 0.4-9.6 vs. 38.6%, IQR 4.9-70, p = 0.023), and cerebral activity decreased more: slow delta: 2.16 vs. 4.35 µV 2 (p = 0.0049), fast delta: 0.73 vs. 1.37 µV 2 (p = < 0.001). In the first 30 min of the surgical procedure, a 3-fold increase in fast delta (10.48-31.22 µV 2) and a 5-fold increase in gamma (1.42-7.58 µV 2) were observed in the midazolam group. Conclusion: Sevoflurane-based anesthesia resulted in increased cerebral oxygenation and decreased cerebral activity, suggesting adequate anesthesia. Midazolam-based anesthesia in neonates with a more severe CDH led to alarmingly low rScO2 values, below hypoxia threshold, and increased values of EEG power during the first 30 min of surgery. This might indicate conscious experience of pain. Integrating population-pharmacokinetic models and multimodal neuromonitoring are needed for personalized pharmacotherapy in these vulnerable patients. Trial Registration: https://www.trialregister.nl/trial/6972, identifier: NL6972.

The effect of early procedural pain in preterm infants on the maturation of electroencephalogram and heart rate variability.

ABSTRACT: Preterm infants show a higher incidence of cognitive, social, and behavioral problems, even in the absence of major medical complications during their stay in the neonatal intensive care unit (NICU). Several authors suggest that early-life experience of stress and procedural pain could impact cerebral development and maturation resulting in an altered development of cognition, behavior, or motor patterns in later life. However, it remains very difficult to assess this impact of procedural pain on physiological development. This study describes the maturation of electroencephalogram (EEG) signals and heart rate variability in a prospective cohort of 92 preterm infants (<34 weeks gestational age) during their NICU stay. We took into account the number of noxious, ie, skin-breaking, procedures they were subjected in the first 5 days of life, which corresponded to a median age of 31 weeks and 4 days. Using physiological signal modelling, this study shows that a high exposure to early procedural pain, measured as skin-breaking procedures, increased the level of discontinuity in both EEG and heart rate variability in preterm infants. These findings have also been confirmed in a subset of the most vulnerable preterm infants with a gestational age lower than 29 weeks. We conclude that a high level of early pain exposure in the NICU increases the level of functional dysmaturity, which can ultimately impact preterm infants' future developmental outcome.

A Bradycardia-Based Stress Calculator for the Neonatal Intensive Care Unit: A Multisystem Approach.

Early life stress in the neonatal intensive care unit (NICU) can predispose premature infants to adverse health outcomes and neurodevelopment delays. Hands-on-care and procedural pain might induce apneas, hypoxic events, and sleep-wake disturbances, which can ultimately impact maturation, but a data-driven method based on physiological fingerprints to quantify early-life stress does not exist. This study aims to provide an automatic stress detector by investigating the relationship between bradycardias, hypoxic events and perinatal stress in NICU patients. EEG, ECG, and SpO 2 were recorded from 136 patients for at least 3 h in three different monitoring groups. In these subjects, the stress burden was assessed using the Leuven Pain Scale. Different subspace linear discriminant analysis models were designed to detect the presence or the absence of stress based on information in each bradycardic spell. The classification shows an area under the curve in the range [0.80-0.96] and a kappa score in the range [0.41-0.80]. The results suggest that stress seems to increase SpO 2 desaturations and EEG regularity as well as the interaction between the cardiovascular and neurological system. It might be possible that stress load enhances the reaction to respiratory abnormalities, which could ultimately impact the neurological and behavioral development.

Nonlinear Transfer Entropy to Assess the Neurovascular Coupling in Premature Neonates.

In the adult brain, it is well known that increases in local neural activity trigger changes in regional blood flow and, thus, changes in cerebral energy metabolism. This regulation mechanism is called neurovascular coupling (NVC). It is not yet clear to what extent this mechanism is present in the premature brain. In this study, we explore the use of transfer entropy (TE) in order to compute the nonlinear coupling between changes in brain function, assessed by means of EEG, and changes in brain oxygenation, assessed by means of near-infrared spectroscopy (NIRS). In a previous study, we measured the coupling between both variables using a linear model to compute TE. The results indicated that changes in brain oxygenation were likely to precede changes in EEG activity. However, using a nonlinear and nonparametric approach to compute TE, the results indicate an opposite directionality of this coupling. The source of the different results provided by the linear and nonlinear TE is unclear and needs further research. In this study, we present the results from a cohort of 21 premature neonates. Results indicate that TE values computed using the nonlinear approach are able to discriminate between neonates with brain abnormalities and healthy neonates, indicating a less functional NVC in neonates with brain abnormalities.

Assessment of dynamic cerebral autoregulation in humans: Is reproducibility dependent on blood pressure variability?

We tested the influence of blood pressure variability on the reproducibility of dynamic cerebral autoregulation (DCA) estimates. Data were analyzed from the 2nd CARNet bootstrap initiative, where mean arterial blood pressure (MABP), cerebral blood flow velocity (CBFV) and end tidal CO2 were measured twice in 75 healthy subjects. DCA was analyzed by 14 different centers with a variety of different analysis methods. Intraclass Correlation (ICC) values increased significantly when subjects with low power spectral density MABP (PSD-MABP) values were removed from the analysis for all gain, phase and autoregulation index (ARI) parameters. Gain in the low frequency band (LF) had the highest ICC, followed by phase LF and gain in the very low frequency band. No significant differences were found between analysis methods for gain parameters, but for phase and ARI parameters, significant differences between the analysis methods were found. Alternatively, the Spearman-Brown prediction formula indicated that prolongation of the measurement duration up to 35 minutes may be needed to achieve good reproducibility for some DCA parameters. We conclude that poor DCA reproducibility (ICC<0.4) can improve to good (ICC > 0.6) values when cases with low PSD-MABP are removed, and probably also when measurement duration is increased.

Maturation of the Autonomic Nervous System in Premature Infants: Estimating Development Based on Heart-Rate Variability Analysis.

This study aims at investigating the development of premature infants' autonomic nervous system (ANS) based on a quantitative analysis of the heart-rate variability (HRV) with a variety of novel features. Additionally, the role of heart-rate drops, known as bradycardias, has been studied in relation to both clinical and novel sympathovagal indices. ECG data were measured for at least 3 h in 25 preterm infants (gestational age ≤32 weeks) for a total number of 74 recordings. The post-menstrual age (PMA) of each patient was estimated from the RR interval time-series by means of multivariate linear-mixed effects regression. The tachograms were segmented based on bradycardias in periods after, between and during bradycardias. For each of those epochs, a set of temporal, spectral and fractal indices were included in the regression model. The best performing model has R 2 = 0.75 and mean absolute error MAE = 1.56 weeks. Three main novelties can be reported. First, the obtained maturation models based on HRV have comparable performance to other development models. Second, the selected features for age estimation show a predominance of power and fractal features in the very-low- and low-frequency bands in explaining the infants' sympathovagal development from 27 PMA weeks until 40 PMA weeks. Third, bradycardias might disrupt the relationship between common temporal indices of the tachogram and the age of the infant and the interpretation of sympathovagal indices. This approach might provide a novel overview of post-natal autonomic maturation and an alternative development index to other electrophysiological data analysis.

Dynamic Cerebral Autoregulation Reproducibility Is Affected by Physiological Variability.

Parameters describing dynamic cerebral autoregulation (DCA) have limited reproducibility. In an international, multi-center study, we evaluated the influence of multiple analytical methods on the reproducibility of DCA. Fourteen participating centers analyzed repeated measurements from 75 healthy subjects, consisting of 5 min of spontaneous fluctuations in blood pressure and cerebral blood flow velocity signals, based on their usual methods of analysis. DCA methods were grouped into three broad categories, depending on output types: (1) transfer function analysis (TFA); (2) autoregulation index (ARI); and (3) correlation coefficient. Only TFA gain in the low frequency (LF) band showed good reproducibility in approximately half of the estimates of gain, defined as an intraclass correlation coefficient (ICC) of >0.6. None of the other DCA metrics had good reproducibility. For TFA-like and ARI-like methods, ICCs were lower than values obtained with surrogate data (p < 0.05). For TFA-like methods, ICCs were lower for the very LF band (gain 0.38 ± 0.057, phase 0.17 ± 0.13) than for LF band (gain 0.59 ± 0.078, phase 0.39 ± 0.11, p ≤ 0.001 for both gain and phase). For ARI-like methods, the mean ICC was 0.30 ± 0.12 and for the correlation methods 0.24 ± 0.23. Based on comparisons with ICC estimates obtained from surrogate data, we conclude that physiological variability or non-stationarity is likely to be the main reason for the poor reproducibility of DCA parameters.

Functional form estimation using oblique projection matrices for LS-SVM regression models.

Kernel regression models have been used as non-parametric methods for fitting experimental data. However, due to their non-parametric nature, they belong to the so-called "black box" models, indicating that the relation between the input variables and the output, depending on the kernel selection, is unknown. In this paper we propose a new methodology to retrieve the relation between each input regressor variable and the output in a least squares support vector machine (LS-SVM) regression model. The method is based on oblique subspace projectors (ObSP), which allows to decouple the influence of input regressors on the output by including the undesired variables in the null space of the projection matrix. Such functional relations are represented by the nonlinear transformation of the input regressors, and their subspaces are estimated using appropriate kernel evaluations. We exploit the properties of ObSP in order to decompose the output of the obtained regression model as a sum of the partial nonlinear contributions and interaction effects of the input variables, we called this methodology Nonlinear ObSP (NObSP). We compare the performance of the proposed algorithm with the component selection and smooth operator (COSSO) for smoothing spline ANOVA models. We use as benchmark 2 toy examples and a real life regression model using the concrete strength dataset from the UCI machine learning repository. We showed that NObSP is able to outperform COSSO, producing stable estimations of the functional relations between the input regressors and the output, without the use of prior-knowledge. This methodology can be used in order to understand the functional relations between the inputs and the output in a regression model, retrieving the physical interpretation of the regression models.

Measurement of Neurovascular Coupling in Neonates.

Neurovascular coupling refers to the mechanism that links the transient neural activity to the subsequent change in cerebral blood flow, which is regulated by both chemical signals and mechanical effects. Recent studies suggest that neurovascular coupling in neonates and preterm born infants is different compared to adults. The hemodynamic response after a stimulus is later and less pronounced and the stimulus might even result in a negative (hypoxic) signal. In addition, studies both in animals and neonates confirm the presence of a short hypoxic period after a stimulus in preterm infants. In clinical practice, different methodologies exist to study neurovascular coupling. The combination of functional magnetic resonance imaging or functional near-infrared spectroscopy (brain hemodynamics) with EEG (brain function) is most commonly used in neonates. Especially near-infrared spectroscopy is of interest, since it is a non-invasive method that can be integrated easily in clinical care and is able to provide results concerning longer periods of time. Therefore, near-infrared spectroscopy can be used to develop a continuous non-invasive measurement system, that could be used to study neonates in different clinical settings, or neonates with different pathologies. The main challenge for the development of a continuous marker for neurovascular coupling is how the coupling between the signals can be described. In practice, a wide range of signal interaction measures exist. Moreover, biomedical signals often operate on different time scales. In a more general setting, other variables also have to be taken into account, such as oxygen saturation, carbon dioxide and blood pressure in order to describe neurovascular coupling in a concise manner. Recently, new mathematical techniques were developed to give an answer to these questions. This review discusses these recent developments.

Neonatal Seizure Detection Using Deep Convolutional Neural Networks.

Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published studies describing features and seizure classifiers, the features were hand-engineered, which may not be optimal. The main goal of the present paper is using deep convolutional neural networks (CNNs) and random forest to automatically optimize feature selection and classification. The input of the proposed classifier is raw multi-channel EEG and the output is the class label: seizure/nonseizure. By training this network, the required features are optimized, while fitting a nonlinear classifier on the features. After training the network with EEG recordings of 26 neonates, five end layers performing the classification were replaced with a random forest classifier in order to improve the performance. This resulted in a false alarm rate of 0.9 per hour and seizure detection rate of 77% using a test set of EEG recordings of 22 neonates that also included dubious seizures. The newly proposed CNN classifier outperformed three data-driven feature-based approaches and performed similar to a previously developed heuristic method.

Reproducibility of dynamic cerebral autoregulation parameters: a multi-centre, multi-method study.

OBJECTIVE: Different methods to calculate dynamic cerebral autoregulation (dCA) parameters are available. However, most of these methods demonstrate poor reproducibility that limit their reliability for clinical use. Inter-centre differences in study protocols, modelling approaches and default parameter settings have all led to a lack of standardisation and comparability between studies. We evaluated reproducibility of dCA parameters by assessing systematic errors in surrogate data resulting from different modelling techniques. APPROACH: Fourteen centres analysed 22 datasets consisting of two repeated physiological blood pressure measurements with surrogate cerebral blood flow velocity signals, generated using Tiecks curves (autoregulation index, ARI 0-9) and added noise. For reproducibility, dCA methods were grouped in three broad categories: 1. Transfer function analysis (TFA)-like output; 2. ARI-like output; 3. Correlation coefficient-like output. For all methods, reproducibility was determined by one-way intraclass correlation coefficient analysis (ICC). MAIN RESULTS: For TFA-like methods the mean (SD; [range]) ICC gain was 0.71 (0.10; [0.49-0.86]) and 0.80 (0.17; [0.36-0.94]) for VLF and LF (p = 0.003) respectively. For phase, ICC values were 0.53 (0.21; [0.09-0.80]) for VLF, and 0.92 (0.13; [0.44-1.00]) for LF (p < 0.001). Finally, ICC for ARI-like methods was equal to 0.84 (0.19; [0.41-0.94]), and for correlation-like methods, ICC was 0.21 (0.21; [0.056-0.35]). SIGNIFICANCE: When applied to realistic surrogate data, free from the additional exogenous influences of physiological variability on cerebral blood flow, most methods of dCA modelling showed ICC values considerably higher than what has been reported for physiological data. This finding suggests that the poor reproducibility reported by previous studies may be mainly due to the inherent physiological variability of cerebral blood flow regulatory mechanisms rather than related to (stationary) random noise and the signal analysis methods.

Cerebral autoregulation and activity after propofol for endotracheal intubation in preterm neonates.

BACKGROUND: Despite increasing use of propofol in neonates, observations on cerebral effects are limited. AIM: To investigate cerebral autoregulation (CAR) and activity after propofol for endotracheal intubation in preterm neonates. METHODS: Twenty-two neonates received propofol before intubation as part of a published dose-finding study. Mean arterial blood pressure (MABP), near-infrared spectroscopy-derived cerebral oxygenation (rScO2), and amplitude-integrated electroencephalography (aEEG) were analyzed until 180 min after propofol. CAR was expressed as transfer function (TF) gain, indicating % change in rScO2 per 1 mmHg change in MABP. Values exceeding mean TF gain + 2 standard deviations (SD) defined impaired CAR. RESULTS: After intubation with a median propofol dose of 1 (0.5-4.5) mg/kg, rScO2 remained stable during decreasing MABP. Mean (±SD) TF gain was 0.8 (±0.3)%/mmHg. Impaired CAR was identified in 1 and 5 patient(s) during drug-related hypotension and normal to raised MABP, respectively. Suppressed aEEG was observed up to 60 min after propofol. CONCLUSIONS: Drug-related hypotension and decreased cerebral activity after intubation with low propofol doses in preterm neonates were observed, without evidence of cerebral ischemic hypoxia. CAR remained intact during drug-related hypotension in 95.5% of patients. Cerebral monitoring including CAR clarifies the cerebral impact of MABP fluctuations.

Impact of Changes in Systemic Physiology on fNIRS/NIRS Signals: Analysis Based on Oblique Subspace Projections Decomposition.

Measurements of cerebral and muscle oxygenation (StO2) and perfusion ([tHb]) with functional near-infrared spectroscopy (fNIRS) and near infrared spectroscopy (NIRS), respectively, can be influenced by changes in systemic physiology. The aim of our study was to apply the oblique subspace projections signal decomposition (OSPSD) to find the contribution from systemic physiology, i.e. heart rate (HR), electrocardiography (ECG)-derived respiration (EDR) and partial pressure of carbon dioxide (pCO2) to StO2 and [tHb] signals measured on the prefrontal cortex (PFC) and calf muscle. OSPSD was applied to two datasets (n1 = 42, n2 = 79 measurements) from two fNIRS/NIRS speech studies. We found that (i) all StO2 and [tHb] signals contained components related to changes in systemic physiology, (ii) the contribution from systemic physiology varied strongly between subjects, and (iii) changes in systemic physiology generally influenced fNIRS signals on the left and right PFC to a similar degree.

Quiet sleep detection in preterm infants using deep convolutional neural networks.

OBJECTIVE: Neonates spend most of their time asleep. Sleep of preterm infants evolves rapidly throughout maturation and plays an important role in brain development. Since visual labelling of the sleep stages is a time consuming task, automated analysis of electroencephalography (EEG) to identify sleep stages is of great interest to clinicians. This automated sleep scoring can aid in optimizing neonatal care and assessing brain maturation. APPROACH: In this study, we designed and implemented an 18-layer convolutional neural network to discriminate quiet sleep from non-quiet sleep in preterm infants. The network is trained on 54 recordings from 13 preterm neonates and the performance is assessed on 43 recordings from 13 independent patients. All neonates had a normal neurodevelopmental outcome and the EEGs were recorded between 27 and 42 weeks postmenstrual age. MAIN RESULTS: The proposed network achieved an area under the mean and median ROC curve equal to 92% and 98%, respectively. SIGNIFICANCE: Our findings suggest that CNN is a suitable and fast approach to classify neonatal sleep stages in preterm infants.

Measuring Near-Infrared Spectroscopy Derived Cerebral Autoregulation in Neonates: From Research Tool Toward Bedside Multimodal Monitoring.

Introduction: Cerebral autoregulation (CAR), the ability of the human body to maintain cerebral blood flow (CBF) in a wide range of perfusion pressures, can be calculated by describing the relation between arterial blood pressure (ABP) and cerebral oxygen saturation measured by near-infrared spectroscopy (NIRS). In literature, disturbed CAR is described in different patient groups, using multiple measurement techniques and mathematical models. Furthermore, it is unclear to what extent cerebral pathology and outcome can be explained by impaired CAR. Aim and methods: In order to summarize CAR studies using NIRS in neonates, a systematic review was performed in the PUBMED and EMBASE database. To provide a general overview of the clinical framework used to study CAR, the different preprocessing methods and mathematical models are described and explained. Furthermore, patient characteristics, definition of impaired CAR and the outcome according to this definition is described organized for the different patient groups. Results: Forty-six articles were included in this review. Four patient groups were established: preterm infants during the transitional period, neonates receiving specific medication/treatment, neonates with congenital heart disease and neonates with hypoxic-ischemic encephalopathy (HIE) treated with therapeutic hypothermia. Correlation, coherence and transfer function (TF) gain are the mathematical models most frequently used to describe CAR. The definition of impaired CAR is depending on the mathematical model used. The incidence of intraventricular hemorrhage in preterm infants is the outcome variable most frequently correlated with impaired CAR. Hypotension, disease severity, dopamine treatment, injury on magnetic resonance imaging (MRI) and long term outcome are associated with impaired CAR. Prospective interventional studies are lacking in all research areas. Discussion and conclusion: NIRS derived CAR measurement is an important research tool to improve knowledge about central hemodynamic fluctuations during the transitional period, cerebral pharmacodynamics of frequently used medication (sedatives-inotropes) and cerebral effects of specific therapies in neonatology. Uniformity regarding measurement techniques and mathematical models is needed. Multimodal monitoring databases of neonatal intensive care patients of multiple centers, together with identical outcome parameters are needed to compare different techniques and make progress in this field. Real-time bedside monitoring of CAR, together with conventional monitoring, seems a promising technique to improve individual patient care.

Relation Between EEG Activity and Brain Oxygenation in Preterm Neonates.

This study investigates the relationship between brain oxygenation, assessed by means of near infrared spectroscopy (NIRS), and brain function, assessed by means of electroencephalography (EEG). Using NIRS signals measuring the regional cerebral oxygen saturation (rScO2) and computing the fractional tissue oxygen extraction (FTOE), we compared how these variables relate to different features extracted from the EEG, such as the inter-burst interval (IBI) duration and amplitude, the amplitude of the EEG, and the amplitude of the burst. A cohort of 22 neonates undergoing sedation by propofol was studied and a regression of the NIRS-derived values to the different EEG features was made. We found that higher values of FTOE were related to higher values of EEG amplitude. These results might be of used in the monitoring of proper brain function in neonates.

Decomposition of Near-Infrared Spectroscopy Signals Using Oblique Subspace Projections: Applications in Brain Hemodynamic Monitoring.

Clinical data is comprised by a large number of synchronously collected biomedical signals that are measured at different locations. Deciphering the interrelationships of these signals can yield important information about their dependence providing some useful clinical diagnostic data. For instance, by computing the coupling between Near-Infrared Spectroscopy signals (NIRS) and systemic variables the status of the hemodynamic regulation mechanisms can be assessed. In this paper we introduce an algorithm for the decomposition of NIRS signals into additive components. The algorithm, SIgnal DEcomposition base on Obliques Subspace Projections (SIDE-ObSP), assumes that the measured NIRS signal is a linear combination of the systemic measurements, following the linear regression model y = Ax + ϵ. SIDE-ObSP decomposes the output such that, each component in the decomposition represents the sole linear influence of one corresponding regressor variable. This decomposition scheme aims at providing a better understanding of the relation between NIRS and systemic variables, and to provide a framework for the clinical interpretation of regression algorithms, thereby, facilitating their introduction into clinical practice. SIDE-ObSP combines oblique subspace projections (ObSP) with the structure of a mean average system in order to define adequate signal subspaces. To guarantee smoothness in the estimated regression parameters, as observed in normal physiological processes, we impose a Tikhonov regularization using a matrix differential operator. We evaluate the performance of SIDE-ObSP by using a synthetic dataset, and present two case studies in the field of cerebral hemodynamics monitoring using NIRS. In addition, we compare the performance of this method with other system identification techniques. In the first case study data from 20 neonates during the first 3 days of life was used, here SIDE-ObSP decoupled the influence of changes in arterial oxygen saturation from the NIRS measurements, facilitating the use of NIRS as a surrogate measure for cerebral blood flow (CBF). The second case study used data from a 3-years old infant under Extra Corporeal Membrane Oxygenation (ECMO), here SIDE-ObSP decomposed cerebral/peripheral tissue oxygenation, as a sum of the partial contributions from different systemic variables, facilitating the comparison between the effects of each systemic variable on the cerebral/peripheral hemodynamics.

Propofol Dose-Finding to Reach Optimal Effect for (Semi-)Elective Intubation in Neonates.

OBJECTIVE: To define the effective dose for 50% of patients (ED50) of propofol for successful intubation and to determine the rate of successful extubation in those patients with planned intubation, surfactant administration, and immediate extubation (INSURE procedure). In addition, pharmacodynamic effects were assessed. STUDY DESIGN: Neonates (n = 50) treated with propofol for (semi-)elective endotracheal intubation were stratified in 8 strata by postmenstrual and postnatal age. The first patient in each stratum received an intravenous bolus of 1 mg/kg propofol. Dosing for the next patient was determined using the up-and-down method. A propofol ED50 dose was calculated in each stratum with an effective sample size of at least 6, via the Dixon-Masey method, with simultaneous assessment of clinical scores and continuous vital sign monitoring. RESULTS: Propofol ED50 values for preterm neonates <10 days of age varied between 0.713 and 1.350 mg/kg. Clinical recovery was not attained at the end of the 21-minute scoring period. Mean arterial blood pressure showed a median decrease between 28.5% and 39.1% from baseline with a brief decrease in peripheral and regional cerebral oxygen saturation. Variability in mean arterial blood pressure area under the curve could not be explained by weight or age. CONCLUSIONS: Low propofol doses were sufficient to sedate neonates for intubation. Clinical recovery was accompanied by permissive hypotension (no clinical shock and no treatment). The propofol ED50 doses can be administered at induction, with subsequent up-titration if needed, while monitoring blood pressure. They can be used for further dosing optimalization and validation studies. TRIAL REGISTRATION: ClinicalTrials.gov: NCT01621373; EudraCT: 2012-002648-26.

Changes in Oxygenation Levels Precede Changes in Amplitude of the EEG in Premature Infants.

Brain function is supported by an appropriate balance between the metabolic demand and the supply of nutrients and oxygen. However, the physiological principles behind the regulation of brain metabolism and demand in premature infants are unknown. Some studies found that changes in hemodynamic variables in this population precede changes in EEG activity; however, these studies only used descriptive statistics. This paper describes the relationship between changes in cerebral oxygenation, assessed by means of near-infrared spectroscopy (NIRS), and changes in EEG, using mathematical methods taken from information dynamics. In a cohort of 35 neonates subjected to sedation by propofol, we quantified the direction of information transfer between brain oxygenation and EEG. The results obtained indicate that, as reported in other studies, changes in NIRS are likely to precede changes in EEG activity.