Dynamic Assessment of Baroreflex Control of Heart Rate During Induction of Propofol Anesthesia Using a Point Process Method

In this article, we present a point process method to assess dynamic baroreflex sensitivity (BRS) by estimating the baroreflex gain as focal component of a simplified closed-loop model of the cardiovascular system. Specifically, an inverse Gaussian probability distribution is used to model the heartbeat interval, whereas the instantaneous mean is identified by linear and bilinear bivariate regressions on both the previous R−R intervals (RR) and blood pressure (BP) beat-to-beat measures. The instantaneous baroreflex gain is estimated as the feedback branch of the loop with a point-process filter, while the RRBP feedforward transfer function representing heart contractility and vasculature effects is simultaneously estimated by a recursive least-squares filter. These two closed-loop gains provide a direct assessment of baroreflex control of heart rate (HR). In addition, the dynamic coherence, cross bispectrum, and their power ratio can also be estimated. All statistical indices provide a valuable quantitative assessment of the interaction between heartbeat dynamics and hemodynamics. To illustrate the application, we have applied the proposed point process model to experimental recordings from 11 healthy subjects in order to monitor cardiovascular regulation under propofol anesthesia. We present quantitative results during transient periods, as well as statistical analyses on steady-state epochs before and after propofol administration. Our findings validate the ability of the algorithm to provide a reliable and fast-tracking assessment of BRS, and show a clear overall reduction in baroreflex gain from the baseline period to the start of propofol anesthesia, confirming that instantaneous evaluation of arterial baroreflex control of HR may yield important implications in clinical practice, particularly during anesthesia and in postoperative care.National Institutes of Health (U.S.) (Grant R01-HL084502)National Institutes of Health (U.S.) (Grant K25-NS05758)National Institutes of Health (U.S.) (Grant DP2- OD006454)National Institutes of Health (U.S.) (Grant T32NS048005)National Institutes of Health (U.S.) (Grant T32NS048005)National Institutes of Health (U.S.) (Grant R01-DA015644)Massachusetts General Hospital (Clinical Research Center, UL1 Grant RR025758

Instantaneous monitoring of heart beat dynamics during anesthesia and sedation

Anesthesia-induced altered arousal depends on drugs having their effect in specific brain regions. These effects are also reflected in autonomic nervous system (ANS) outflow dynamics. To this extent, instantaneous monitoring of ANS outflow, based on neurophysiological and computational modeling, may provide a more accurate assessment of the action of anesthetic agents on the cardiovascular system. This will aid anesthesia care providers in maintaining homeostatic equilibrium and help to minimize drug administration while maintaining antinociceptive effects. In previous studies, we established a point process paradigm for analyzing heartbeat dynamics and have successfully applied these methods to a wide range of cardiovascular data and protocols. We recently devised a novel instantaneous nonlinear assessment of ANS outflow, also suitable and effective for real-time monitoring of the fast hemodynamic and autonomic effects during induction and emergence from anesthesia. Our goal is to demonstrate that our framework is suitable for instantaneous monitoring of the ANS response during administration of a broad range of anesthetic drugs. Specifically, we compare the hemodynamic and autonomic effects in study participants undergoing propofol (PROP) and dexmedetomidine (DMED) administration. Our methods provide an instantaneous characterization of autonomic state at different stages of sedation and anesthesia by tracking autonomic dynamics at very high time-resolution. Our results suggest that refined methods for analyzing linear and nonlinear heartbeat dynamics during administration of specific anesthetic drugs are able to overcome nonstationary limitations as well as reducing inter-subject variability, thus providing a potential real-time monitoring approach for patients receiving anesthesia

Predicting hypotension in perioperative and intensive care medicine

Blood pressure is the main determinant of organ perfusion. Hypotension is common in patients having surgery and in critically ill patients. The severity and duration of hypotension are associated with hypoperfusion and organ dysfunction. Hypotension is mostly treated reactively after low blood pressure values have already occurred. However, prediction of hypotension before it becomes clinically apparent would allow the clinician to treat hypotension pre-emptively, thereby reducing the severity and duration of hypotension. Hypotension cannowbepredictedminutes before it actually occurs from the blood pressure waveform using machine-learning algorithms that can be trained to detect subtle changes in cardiovascular dynamics preceding clinically apparent hypotension. However, analyzing the complex cardiovascular system is a challenge because cardiovascular physiology is highly interdependent, works within complicated networks, and is influenced by compensatory mechanisms. Improved hemodynamic data collection and integration will be a key to improve current models and develop new hypotension prediction models. (C) 2019 Elsevier Ltd. All rights reserved

Mechanism-based pharmacodynamic model for propofol haemodynamic effects in healthy volunteers☆

Background: The adverse haemodynamic effects of the intravenous anaesthetic propofol are well known, yet few empirical models have explored the dose-response relationship. Evidence suggests that hypotension during general anaesthesia is associated with postoperative mortality. We developed a mechanism-based model that quantitatively characterises the magnitude of propofol-induced haemodynamic effects during general anaesthesia. Methods: Mean arterial pressure (MAP), heart rate (HR) and pulse pressure (PP) measurements were available from 36 healthy volunteers who received propofol in a step-up and step-down fashion by target-controlled infusion using the Schnider pharmacokinetic model. A mechanistic pharmacodynamic model was explored based on the Snelder model. To benchmark the performance of this model, we developed empirical models for MAP, HR, and PP. Results: The mechanistic model consisted of three turnover equations representing total peripheral resistance (TPR), stroke volume (SV), and HR. Propofol-induced changes were implemented by E-max models on the zero-order production rates of the turnover equations for TPR and SV. The estimated 50% effective concentrations for propofol-induced changes in TPR and SV were 2.96 and 0.34 mu g ml(-1), respectively. The goodness-of-fit for the mechanism-based model was indistinguishable from the empirical models. Simulations showed that predictions from the mechanism-based model were similar to previously published MAP and HR observations. Conclusions: We developed a mechanism-based pharmacodynamic model for propofol-induced changes in MAP, TPR, SV, and HR as a potential approach for predicting haemodynamic alterations

EEG-Based Automatic Classification of ‘Awake’ versus ‘Anesthetized’ State in General Anesthesia Using Granger Causality

BACKGROUND: General anesthesia is a reversible state of unconsciousness and depression of reflexes to afferent stimuli induced by administration of a "cocktail" of chemical agents. The multi-component nature of general anesthesia complicates the identification of the precise mechanisms by which anesthetics disrupt consciousness. Devices that monitor the depth of anesthesia are an important aide for the anesthetist. This paper investigates the use of effective connectivity measures from human electrical brain activity as a means of discriminating between 'awake' and 'anesthetized' state during induction and recovery of consciousness under general anesthesia. METHODOLOGY/PRINCIPAL FINDINGS: Granger Causality (GC), a linear measure of effective connectivity, is utilized in automated classification of 'awake' versus 'anesthetized' state using Linear Discriminant Analysis and Support Vector Machines (with linear and non-linear kernel). Based on our investigations, the most characteristic change of GC observed between the two states is the sharp increase of GC from frontal to posterior regions when the subject was anesthetized, and reversal at recovery of consciousness. Features derived from the GC estimates resulted in classification of 'awake' and 'anesthetized' states in 21 patients with maximum average accuracies of 0.98 and 0.95, during loss and recovery of consciousness respectively. The differences in linear and non-linear classification are not statistically significant, implying that GC features are linearly separable, eliminating the need for a complex and computationally expensive non-linear classifier. In addition, the observed GC patterns are particularly interesting in terms of a physiological interpretation of the disruption of consciousness by anesthetics. Bidirectional interaction or strong unidirectional interaction in the presence of a common input as captured by GC are most likely related to mechanisms of information flow in cortical circuits. CONCLUSIONS/SIGNIFICANCE: GC-based features could be utilized effectively in a device for monitoring depth of anesthesia during surgery

Anesthesia for cesarean section in the dog

The major goal in anesthesia for cesarean section (CS) is to minimize fetal effects of anesthetic drugs in order to minimize fetal respiratory, central nervous system and cardiovascular depression and deliver live, vigorous puppies. Of equal importance is to provide adequate analgesia to the dam and prevent anesthesia-related complications such as hypotension, hypoventilation, hypoxemia, hemorrhage and hypothermia, which will increase morbidity and mortality in both mother and puppies. The physiochemical properties which allow drugs to cross the blood-brain barrier also facilitate crossing of the placenta, therefore the assumption should be made (with very few exceptions) that anes- thetics, analgesics and sedatives/tranquilizers all cross the placenta. Prolonged labor prior to delivery causes maternal physiologic compromise, resulting in fetal depression due to decreased placental perfusion, hypoxemia and acidosis. Maternal and puppy mortality is significantly increased during emergent versus planned CS (1,2). Timing and preparation are extremely impor- tant for puppy survival for both elective and emergency CS, and a thorough understanding of the maternal phy- siologic changes and the potential impact of anesthetic drugs is essential to optimize outcomes for both mother and fetus (Figure 1)

Modelling, Optimisation and Explicit Model Predictive Control of Anaesthesia Drug Delivery Systems

The contributions of this thesis are organised in two parts. Part I presents a mathematical model for drug distribution and drug effect of volatile anaesthesia. Part II presents model predictive control strategies for depth of anaesthesia control based on the derived model. Closed-loop model predictive control strategies for anaesthesia are aiming to improve patient's safety and to fine-tune drug delivery, routinely performed by the anaesthetist. The framework presented in this thesis highlights the advantages of extensive modelling and model analysis, which are contributing to a detailed understanding of the system, when aiming for the optimal control of such system. As part of the presented framework, the model uncertainty originated from patient-variability is analysed and the designed control strategy is tested against the identified uncertainty. An individualised physiologically based model of drug distribution and uptake, pharmacokinetics, and drug effect, pharmacodynamics, of volatile anaesthesia is presented, where the pharmacokinetic model is adjusted to the weight, height, gender and age of the patient. The pharmacodynamic model links the hypnotic depth measured by the Bispectral index (BIS), to the arterial concentration by an artificial effect site compartment and the Hill equation. The individualised pharmacokinetic and pharmacodynamic variables and parameters are analysed with respect to their influence on the measurable outputs, the end-tidal concentration and the BIS. The validation of the model, performed with clinical data for isoflurane and desflurane based anaesthesia, shows a good prediction of the drug uptake, while the pharmacodynamic parameters are individually estimated for each patient. The derived control design consists of a linear multi-parametric model predictive controller and a state estimator. The non-measurable tissue and blood concentrations are estimated based on the end-tidal concentration of the volatile anaesthetic. The designed controller adapts to the individual patient's dynamics based on measured data. In an alternative approach, the individual patient's sensitivity is estimated on-line by solving a least squares parameter estimation problem.Open Acces