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

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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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

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

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

Performance of lung recruitment model in healthy anesthetised pigs

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Improved pressure contour analysis for estimating cardiac stroke volume using pulse wave velocity measurement.

peer reviewedBACKGROUND: Pressure contour analysis is commonly used to estimate cardiac performance for patients suffering from cardiovascular dysfunction in the intensive care unit. However, the existing techniques for continuous estimation of stroke volume (SV) from pressure measurement can be unreliable during hemodynamic instability, which is inevitable for patients requiring significant treatment. For this reason, pressure contour methods must be improved to capture changes in vascular properties and thus provide accurate conversion from pressure to flow. METHODS: This paper presents a novel pressure contour method utilizing pulse wave velocity (PWV) measurement to capture vascular properties. A three-element Windkessel model combined with the reservoir-wave concept are used to decompose the pressure contour into components related to storage and flow. The model parameters are identified beat-to-beat from the water-hammer equation using measured PWV, wave component of the pressure, and an estimate of subject-specific aortic dimension. SV is then calculated by converting pressure to flow using identified model parameters. The accuracy of this novel method is investigated using data from porcine experiments (N = 4 Pietrain pigs, 20-24.5 kg), where hemodynamic properties were significantly altered using dobutamine, fluid administration, and mechanical ventilation. In the experiment, left ventricular volume was measured using admittance catheter, and aortic pressure waveforms were measured at two locations, the aortic arch and abdominal aorta. RESULTS: Bland-Altman analysis comparing gold-standard SV measured by the admittance catheter and estimated SV from the novel method showed average limits of agreement of +/-26% across significant hemodynamic alterations. This result shows the method is capable of estimating clinically acceptable absolute SV values according to Critchely and Critchely. CONCLUSION: The novel pressure contour method presented can accurately estimate and track SV even when hemodynamic properties are significantly altered. Integrating PWV measurements into pressure contour analysis improves identification of beat-to-beat changes in Windkessel model parameters, and thus, provides accurate estimate of blood flow from measured pressure contour. The method has great potential for overcoming weaknesses associated with current pressure contour methods for estimating SV

Impact of sensor and measurement timing errors on model-based insulin sensitivity

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Expiratory model-based method to monitor ARDS disease state

INTRODUCTION: Model-based methods can be used to characterise patient-specific condition and response to mechanical ventilation (MV) during treatment for acute respiratory distress syndrome (ARDS). Conventional metrics of respiratory mechanics are based on inspiration only, neglecting data from the expiration cycle. However, it is hypothesised that expiratory data can be used to determine an alternative metric, offering another means to track patient condition and guide positive end expiratory pressure (PEEP) selection. METHODS: Three fully sedated, oleic acid induced ARDS piglets underwent three experimental phases. Phase 1 was a healthy state recruitment manoeuvre. Phase 2 was a progression from a healthy state to an oleic acid induced ARDS state. Phase 3 was an ARDS state recruitment manoeuvre. The expiratory time-constant model parameter was determined for every breathing cycle for each subject. Trends were compared to estimates of lung elastance determined by means of an end-inspiratory pause method and an integral-based method. All experimental procedures, protocols and the use of data in this study were reviewed and approved by the Ethics Committee of the University of Liege Medical Faculty. RESULTS: The overall median absolute percentage fitting error for the expiratory time-constant model across all three phases was less than 10 %; for each subject, indicating the capability of the model to capture the mechanics of breathing during expiration. Provided the respiratory resistance was constant, the model was able to adequately identify trends and fundamental changes in respiratory mechanics. CONCLUSION: Overall, this is a proof of concept study that shows the potential of continuous monitoring of respiratory mechanics in clinical practice. Respiratory system mechanics vary with disease state development and in response to MV settings. Therefore, titrating PEEP to minimal elastance theoretically results in optimal PEEP selection. Trends matched clinical expectation demonstrating robustness and potential for guiding MV therapy. However, further research is required to confirm the use of such real-time methods in actual ARDS patients, both sedated and spontaneously breathing.Peer reviewe