One-dimensional time-dependent model of the cardiac pacemaker activity induced by the mechano-electric feedback in a thermo-electro-mechanical background

peer reviewedBut de l’étude : Dans un cœur sain, le mécanisme de feedback mécano-électrique (FME) agit comme un régulateur intrinsèque du myocarde, en atténuant les perturbations mécaniques, permettant une contraction cardiaque normale et une situation électromécanique saine. Cependant, dans certaines circonstances, le FME peut être un générateur d’arythmies cardiaques importantes en induisant localement des dépolarisations électriques dues à des déformations anormales du tissu myocardique, via des canaux mécano-sensibles activés par l’étirement des fibres musculaires cardiaques. Ces perturbations peuvent ensuite se propager à l’ensemble du cœur et mener à un dysfonctionnement global du myocarde. Dans cette étude, nous examinons qualitativement l’influence de la température sur l’activité électrique autonome induite par le FME. Méthode : Nous présentons un modèle unidimensionnel instationnaire contenant tous les éléments majeurs permettant de prendre en compte le couplage excitation-contraction, le FME et le couplage thermoélectrique. Résultats : Nos simulations numériques montrent qu’une activité électrique autonome peut être induite par les déformations mécaniques cardiaques mais seulement pour un intervalle donné de température. Par ailleurs, dans certains cas, l’activité électrique autonome est périodique tel un pacemaker. De plus, nous montrons que certaines propriétés des potentiels d’action, générés par le FME, sont significativement influencées par la température. En outre, lorsque l’activité électrique prend la forme d’un pacemaker, nous mettons en évidence que la période est fortement dépendante de la température. Conclusions : Notre modèle qualitatif montre que la température est un facteur influençant fortement le comportement électromécanique du cœur et plus particulièrement, l’activité électrique autonome induite par les déformations du tissu myocardique.Aim of the study: In a healthy heart, the mechano-electric feedback (MEF) process acts as an intrinsic regulatory mechanism of the myocardium which allows the normal cardiac contraction by damping mechanical perturbations in order to generate a new healthy electromechanical situation. However, under certain conditions, the MEF can be a generator of dramatic arrhythmias by inducing local electrical depolarizations as a result of abnormal cardiac tissue deformations, via stretch-activated channels (SACs). Then, these perturbations can propagate in the whole heart and lead to global cardiac dysfunctions. In the present study, we qualitatively investigate the influence of temperature on autonomous electrical activity generated by the MEF. Method: We introduce a one-dimensional time-dependent model containing all the key ingredients that allow accounting for the excitation-contraction coupling, the MEF and the thermoelectric coupling. Results: Our simulations show that an autonomous electrical activity can be induced by cardiac deformations, but only inside a certain temperature interval. In addition, in some cases, the autonomous electrical activity takes place in a periodic way like a pacemaker. We also highlight that some properties of action potentials, generated by the mechano-electric feedback, are significantly influenced by temperature. Moreover, in the situation where a pacemaker activity occurs, we also show that the period is heavily temperature-dependent. Conclusions: Our qualitative model shows that the temperature is a significant factor with regards to the electromechanical behavior of the heart and more specifically, with regards to the autonomous electrical activity induced by the cardiac tissue deformations

A graphical method for practical and informative identifiability analyses of physiological models: A case study of insulin kinetics and sensitivity

peer reviewedBackground: Derivative based a-priori structural identifiability analyses of mathematical models can offer valuable insight into the identifiability of model parameters. However, these analyses are only capable of a binary confirmation of the mathematical distinction of parameters and a positive outcome can begin to lose relevance when measurement error is introduced. This article presents an integral based method that allows the observation of the identifiability of models with two-parameters in the presence of assay error. Methods: The method measures the distinction of the integral formulations of the parameter coefficients at the proposed sampling times. It can thus predict the susceptibility of the parameters to the effects of measurement error. The method is tested in-silico with Monte Carlo analyses of a number of insulin sensitivity test applications. Results: The method successfully captured the analogous nature of identifiability observed in Monte Carlo analyses of a number of cases including protocol alterations, parameter changes and differences in participant behaviour. However, due to the numerical nature of the analyses, prediction was not perfect in all cases. Conclusions: Thus although the current method has valuable and significant capabilities in terms of study or test protocol design, additional developments would further strengthen the predictive capability of the method. Finally, the method captures the experimental reality that sampling error and timing can negate assumed parameter identifiability and that identifiability is a continuous rather than discrete phenomenon

Influence of thermoelectric coupling on pacemaker activity generated by mechano-electric feedback in a one-dimensional ring-shaped model of cardiac fiber

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Model-based computation of total stressed blood volume from a preload reduction manoeuvre

peer reviewedTotal stressed blood volume is an important parameter for both doctors and engineers. From a medical point of view, it has been associated with the success or failure of fluid therapy, a primary treatment to manage acute circulatory failure. From an engineering point of view, it dictates the cardiovascular system’s behavior in changing physiological situations. Current methods to determine this parameter involve repeated phases of circulatory arrests followed by fluid administration. In this work, a more straightforward method is developed using data from a preload reduction manoeuvre. A simple six-chamber cardiovascular system model is used and its parameters are adjusted to pig experimental data. The parameter adjustment process has three steps: (1) compute nominal values for all model parameters; (2) determine the five most sensitive parameters; and (3) adjust only these five parameters. Stressed blood volume was selected by the algorithm, which emphasizes the importance of this parameter. The model was able to track experimental trends with a maximal root mean squared error of 29.2%. Computed stressed blood volume equals 486 ± 117 ml or 15.7 ± 3.6 ml/kg, which matches previous independent experiments on pigs, dogs and humans. The method proposed in this work thus provides a simple way to compute total stressed blood volume from usual hemodynamic data

Parameter Identification Methods in a Model of the Cardiovascular System

To be clinically relevant, mathematical models have to be patient-specific, meaning that their parameters have to be identified from patient data. To achieve real time monitoring, it is important to select the best parameter identification method, in terms of speed, efficiency and reliability. This work presents a comparison of seven parameter identification methods applied to a lumped-parameter cardiovascular system model. The seven methods are tested using in silico and experimental reference data. To do so, precise formulae for initial parameter values first had to be developed. The test results indicate that the trust-region reflective method seems to be the best method for the present model. This method (and the proportional method) are able to perform parameter identification in two to three minutes, and will thus benefit cardiac and vascular monitoring applications

Activité électrique autonome induite par les déformations du tissu cardiaque dans un environnement thermo-électro-mécanique

peer reviewedIn a healthy heart, the mechano-electric feedback (MEF) process acts as an intrinsic regulatory mechanism of the myocardium which allows the normal cardiac contraction by damping mechanical perturbations in order to generate a new healthy electromechanical situation. However, under certain conditions, the MEF can be a generator of dramatic arrhythmias by inducing local electrical depolarizations as a result of abnormal cardiac tissue deformations, via stretch-activated channels (SACs). Then, these perturbations can propagate in the whole heart and lead to global cardiac dysfunctions. In the present study, we examine the spatio-temporal behavior of the autonomous electrical activity induced by the MEF when the heart is subject to temperature variations. For instance, such a situation can occur during a therapeutic hypothermia. This technique is usually used to prevent neuronal injuries after a cardiac resuscitation. From this perspective, we introduce a one-dimensional time-dependent model containing all the key ingredients that allow accounting for excitation-contraction coupling, MEF and thermoelectric coupling. Our simulations show that an autonomous electrical activity can be induced by cardiac deformations, but only inside a certain temperature interval. In addition, in some cases, the autonomous electrical activity takes place in a periodic way like a pacemaker. We also highlight that some properties of the action potentials that are generated by the MEF, are significantly influenced by temperature. Moreover, in the situation where a pacemaker activity occurs, we also show that the period is heavily temperature-dependent

Model-based optimal PEEP in mechanically ventilated ARDS patients in the Intensive Care Unit

Background: The optimal level of positive end-expiratory pressure (PEEP) is still widely debated in treating acute respiratory distress syndrome (ARDS) patients. Current methods of selecting PEEP only provide a range of values and do not provide unique patient-specific solutions. Model-based methods offer a novel way of using non-invasive pressure-volume (PV) measurements to estimate patient recruitability. This paper examines the clinical viability of such models in pilot clinical trials to assist therapy, optimise patient-specific PEEP, assess the disease state and response over time. Methods: Ten patients with acute lung injury or ARDS underwent incremental PEEP recruitment manoeuvres. PV data was measured at increments of 5 cmH(2)O and fitted to the recruitment model. Inspiratory and expiratory breath holds were performed to measure airway resistance and auto-PEEP. Three model-based metrics are used to optimise PEEP based on opening pressures, closing pressures and net recruitment. ARDS status was assessed by model parameters capturing recruitment and compliance. Results: Median model fitting error across all patients for inflation and deflation was 2.8% and 1.02% respectively with all patients experiencing auto-PEEP. In all three metrics' cases, model-based optimal PEEP was higher than clinically selected PEEP. Two patients underwent multiple recruitment manoeuvres over time and model metrics reflected and tracked the state or their ARDS. Conclusions: For ARDS patients, the model-based method presented in this paper provides a unique, non-invasive method to select optimal patient-specific PEEP. In addition, the model has the capability to assess disease state over time using these same models and methods

Model-based diagnosis of acute pulmonary embolism - results from a porcine model

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Effects of Neurally Adjusted Ventilatory Assist (NAVA) levels in non-invasive ventilated patients: titrating NAVA levels with electric diaphragmatic activity and tidal volume matching

BACKGROUND: Neurally adjusted ventilatory assist (NAVA) delivers pressure in proportion to diaphragm electrical activity (Eadi). However, each patient responds differently to NAVA levels. This study aims to examine the matching between tidal volume (Vt) and patients' inspiratory demand (Eadi), and to investigate patient-specific response to various NAVA levels in non-invasively ventilated patients. METHODS: 12 patients were ventilated non-invasively with NAVA using three different NAVA levels. NAVA100 was set according to the manufacturer's recommendation to have similar peak airway pressure as during pressure support. NAVA level was then adjusted ±50% (NAVA50, NAVA150). Airway pressure, flow and Eadi were recorded for 15 minutes at each NAVA level. The matching of Vt and integral of Eadi (ʃEadi) were assessed at the different NAVA levels. A metric, Range90, was defined as the 5-95% range of Vt/ʃEadi ratio to assess matching for each NAVA level. Smaller Range90 values indicated better matching of supply to demand. RESULTS: Patients ventilated at NAVA50 had the lowest Range90 with median 25.6 uVs/ml [Interquartile range (IQR): 15.4-70.4], suggesting that, globally, NAVA50 provided better matching between ʃEadi and Vt than NAVA100 and NAVA150. However, on a per-patient basis, 4 patients had the lowest Range90 values in NAVA100, 1 patient at NAVA150 and 7 patients at NAVA50. Robust coefficient of variation for ʃEadi and Vt were not different between NAVA levels. CONCLUSIONS: The patient-specific matching between ʃEadi and Vt was variable, indicating that to obtain the best possible matching, NAVA level setting should be patient specific. The Range90 concept presented to evaluate Vt/ʃEadi is a physiologic metric that could help in individual titration of NAVA level.Peer reviewe