Model Of Cardiovascular System Research Articles

In-silico Evaluation of the effects of blood flow rate change in ductus arteriosus on cardiac function and cerebral blood flow rate in term and preterm infants

The transition of cardiovascular system is vital for newborn infants. It is expected that ductus arteriosus which is vessel between aorta and pulmonary arteries closes in the first day of life. However, ductus arteriosus may not close within 24 h in preterm infants. In this study, a lumped parameter model was used to evaluate the effects of the change in blood flow rate through ductus arteriosus on cardiac function and cerebral blood flow rate in term and preterm neonatal infants. Mean arterial pressure increased from 41 mmHg to 53 mmHg at 24th hour after birth in term neonatal infant cardiovascular system model whereas it increased from 33 mmHg to 43 mmHg in preterm neonatal infant cardiovascular system model. Left cardiac output decreased from 911 mL/min to 721 mL/min and right cardiac output increased 551 mL/min to 706 mL/min in term neonatal infant cardiovascular system model. Left cardiac output decreased from 705 mL/min to 612 mL/min and right cardiac output increased 369 mL/min to 484 mL/min in preterm neonatal infant cardiovascular system model. Cerebral blood flow rate increased from 115 mL/min to 147.40 mL/min in term neonatal infant cardiovascular system model at 24th hour after birth and it increased 29.65 mL/min to 38.92 mL/min in preterm neonatal cardiovascular system model. The results showed that blood flow rate through ductus arteriosus alters cardiac function and cerebral blood flow rate in both term and preterm cardiovascular system models.

Evaluation of enhanced external counterpulsation for diabetic foot based on a patient-specific 0D-1D cardiovascular system model

Background and objectiveDiabetic foot (DF) complications often lead to severe vascular issues. This study investigated the effectiveness of enhanced external counterpulsation (EECP) and its derived innovative compression strategies in addressing poor perfusion in DF. Although developing non-invasive and efficient treatment methods for DF is critical, the hemodynamic alterations during EECP remain underexplored despite promising outcomes in microcirculation. This research sought to address this gap by developing a patient-specific 0D-1D model based on clinical ultrasound data to identify potentially superior compression strategies that could substantially enhance blood flow in patients with DF complications. MethodsData were gathered from 10 patients with DF utilizing ultrasound for blood flow rate and computed tomography angiography (CTA) to identify lower limb conditions. Clinical measurements during standard EECP, with varying cuff pressures, facilitated the creation of a patient-specific 0D-1D model through a two-step parameter estimation process. The accuracy of this model was verified via comparison with the clinical measurements. Four compression strategies were proposed and rigorously evaluated using this model: EECP-Simp-I (removing hip cuffs), EECP-Simp-II (further removing the cuffs around the lower leg), EECP-Impr-I (removing all cuffs around the affected side), and EECP-Impr-II (building a loop circulation from the healthy side to the affected side). ResultsThe predicted results under the rest and standard EECP states were generally closely aligned with clinical measurements. The patient-specific 0D-1D model demonstrated that EECP-Simp-I and EECP-Impr-I contributed similar enhancement to perfusion in the dorsal artery (DA) and were comparable to standard EECP, while EECP-Simp-II had the least effect and EECP-Impr-II displayed the most significant enhancement. Pressure at the aortic root (AO) remained consistent across strategies. ConclusionsEECP-Simp-I is recommended for patients with DF, emphasizing device simplification. However, EECP-Simp-II is discouraged as it significantly diminished blood perfusion in this study, except in cases of limb fragility. EECP-Impr-II showed superior enhancement of blood perfusion in DA to all other strategies but required a more complex EECP device. Despite increased AO pressure in all the proposed compression strategies, safety could be guaranteed as the pressue remained within a safe range.

The role of atrial pacing site in reducing filling pressures by accelerated pacing in virtual HFpEF patients

Abstract Background Patients with heart failure with preserved ejection fraction (HFpEF) and pacemakers have been shown to benefit from moderately accelerated pacing rates compared to the standard 60bpm. HFpEF patients often present with prolonged P-wave duration (PWD) and atrial fibrillation. Right atrial appendage pacing (RAAP) lengthens PWD, RA septal (RASP) has a neutral effect, while Bachmann’s bundle pacing (BBP) normalizes or shortens PWD compared to sinus rhythm. The mechanical and hemodynamic effects of atrial pacing site selection remain unknown. Aim To investigate the impact of RAAP, RASP and BBP on cardiac mechanics and hemodynamics in an established cohort of virtual HFpEF patients undergoing accelerated pacing. Method HFpEF with prolonged PWD was simulated using the validated CircAdapt computational model of the human cardiovascular system. First, left ventricular (LV) diastolic dysfunction was simulated by decreasing LV myocardial relaxation rate and compliance, such that mean left atrial (LA) pressure (mLAP) was 15mmHg. Second, PWD was prolonged by prescribing a gradual electrical activation delay from RA to LA of 140ms. We then simulated atrial pacing with three different lead positions (Fig.1, left) and gradually increased pacing rate with a constant 120-ms paced atrioventricular (AV) delay. The CircAdapt model computed regional LA strain and mLAP with RAAP, RASP, and BBP. Cardiac output (5L/min) and mean arterial pressure (92mmHg) were kept constant in all simulations. Results Simulated BBP demonstrated the most synchronous LA mechanical activation and function, characterized by uniform segmental LA strain patterns and highest LA reservoir strain (LASR) (Fig.1, middle, with colored arrows highlighting the mechanical dyssynchrony). Hemodynamically (Fig.1, right), BBP acutely decreased mLAP (-1.0mmHg) at the initial heart rate of 60bpm, while RAAP increased it (+0.3mmHg). As pacing rate accelerated, BBP reduces filling pressure (mLAP: -2.8mmHg), more than RASP (-1.8mmHg) or RAAP (-1.2mmHg) at 80bpm. At high pacing rates, RASP and RAAP adversely increase mLAP, due to the prolonged atrial contraction duration and shorter LV filling time. Conclusion Our simulations of different atrial pacing sites in virtual HFpEF patients suggest that BBP yields the most synchronous LA activation pattern and a more substantial reduction in filling pressures with accelerated atrial pacing compared to RASP. Reduction in mLAP with accelerated pacing was attenuated with RAAP. While our study concentrated on a specific paced AV interval, the favorable outcomes observed with BBP could be further improved by including different, rate-adaptive, AV protocols.Fig 1.

Mimicking blood and lymphatic vasculatures using microfluidic systems.

The role of the circulatory system, containing the blood and lymphatic vasculatures, within the body, has become increasingly focused on by researchers as dysfunction of either of the systems has been linked to serious complications and disease. Currently, in vivo models are unable to provide the sufficient monitoring and level of manipulation needed to characterize the fluidic dynamics of the microcirculation in blood and lymphatic vessels; thus in vitro models have been pursued as an alternative model. Microfluidic devices have the required properties to provide a physiologically relevant circulatory system model for research as well as the experimental tools to conduct more advanced research analyses of microcirculation flow. In this review paper, the physiological behavior of fluid flow and electrical communication within the endothelial cells of the systems are detailed and discussed to highlight their complexities. Cell co-culturing methods and other relevant organ-on-a-chip devices will be evaluated to demonstrate the feasibility and relevance of the in vitro microfluidic model. Microfluidic systems will be determined as a noteworthy model that can display physiologically relevant flow of the cardiovascular and lymphatic systems, which will enable researchers to investigate the systems' prevalence in diseases and identify potential therapeutics.

Preserving multi-dimensional information: A hypersphere method for parameter space analysis

BackgroundPhysiological modelling often involves models described by large numbers of variables and significant volumes of clinical data. Mathematical interpretation of such models frequently necessitates analysing data points in high-dimensional spaces. Existing algorithms for analysing high-dimensional points either lose important dimensionality or do not describe the full position of points. Hence, there is a need for an algorithm which preserves this information. MethodsThe most-distant uncovered point (MDUP) hypersphere method is a binary classification approach which defines a collection of equidistant N-dimensional points as the union of hyperspheres. The method iteratively generates hyperspheres at the most distant point in the interest region not yet contained within any hypersphere, until the entire region of interest is defined by the union of all generated hyperspheres. This method is tested on a 7-dimensional space with up to 35.8 million points representing feasible and infeasible spaces of model parameters for a clinically validated cardiovascular system model. ResultsFor different numbers of input points, the MDUP hypersphere method tends to generate large spheres away from the boundary of feasible and infeasible points, but generates the greatest number of relatively much smaller spheres around the boundary of the region of interest to fill this space. Runtime scales quadratically, in part because the current MDUP implementation is not parallelised. ConclusionsThe MDUP hypersphere method can define points in a space of any dimension using only a collection of centre points and associated radii, making the results easily interpretable. It can identify large continuous regions, and in many cases capture the general structure of a region in only a relative few hyperspheres. The MDUP method also shows promise for initialising optimisation algorithm starting conditions within pre-defined feasible regions of model parameter spaces, which could improve model identifiability and the quality of optimisation results.

Interaction of a Ventricular Assist Device With Patient-Specific Cardiovascular Systems: In-Silico Study With Bidirectional Coupling.

Ventricular assist devices (VADs) are used to assist the heart function of patients with advanced heart failure. Computational fluid dynamics in VADs are widely applied in the development and optimization, for example, to evaluate blood damage. For these simulations, the pulsating operating conditions, in which the VAD operates, should be included accurately. Therefore, this study aims to evaluate the flow in a VAD by interacting with patient-specific cardiovascular systems of heart failure patients. A numeric method will be presented, which includes a patient-specific cardiovascular system model that is bidirectionally coupled with a three-dimensional (3D) flow simulation of the HeartMate 3. The cardiovascular system is represented by a lumped parameter model. Three heart failure patients are considered, based on clinical data from end-stage heart failure patients. Various parameters of the cardiovascular system and the VAD are analyzed, for example, flow rates, pressures, VAD heads, and efficiencies. A further important parameter is the blood damage potential of the VAD, which varies significantly among different patients. Moreover, the predicted blood damage fluctuates within a single heartbeat. The increase in blood damage is evaluated based on the operating conditions. Both, overload and especially partial load conditions during the pulsating operation result in elevated blood damage.

A real-time patient-specific treatment strategy for enhanced external counterpulsation.

Diastolic/systolic blood pressure ratio (D/S) ≥ 1.2 is the gold standard of enhanced external counterpulsation (EECP) treatment, but it does not show a clear clinical correspondence with the configuration of the EECP mode. As such, a single target results in different treatment effects in different individuals. The local haemodynamic effect (wall shear stress, WSS) of EECP on vascular endothelial cells is conducive to promote the growth of collateral circulation vessels and restore the blood supply distal to the stenosis lesion. Considering the haemodynamic effects of WSS on human arteries, this study developed a real-time patient-specific treatment strategy of EECP for patients with cardio-cerebrovascular diseases. Based on patient-specific haemodynamic data from 113 individuals, an optimization algorithm was developed to achieve the individualization of a 0D lumped-parameter model of the human circulatory system, thereby simulating the patient-specific global haemodynamic effects. 0D/3D coupled cardio-cerebrovascular models of two subjects were established to simulate the local WSS. We then established statistical models to evaluate clinically unmeasurable WSS based on measurable global haemodynamic indicators. With the aim of attaining appropriate area- and time-averaged WSS (ATAWSS, 4-7 Pa), as evaluated by global haemodynamic indicators, a closed-loop feedback tuning method was developed to provide patient-specific EECP treatment strategies. Results showed that for clinical data collected from 113 individuals, the individualized 0D model can accurately simulate patient-specific global haemodynamic effects (average error <5%). Based on two subjects, the statistical models can be used to evaluate local ATAWSS (error <6%) for coronary arteries and for cerebral arteries. An EECP mode planned by the patient-specific treatment strategy can promote an appropriate ATAWSS within a 16 s calculation time. The real-time patient-specific treatment strategy of EECP is expected to improve the long-term outcome for each patient and have potential clinical significance.

Performance and Reliable Operation of Physiological Controllers Under Various Cardiovascular Models: In Silico and In Vitro Study.

The evaluation of control schemes for left ventricular assist devices (LVADs) requires the utilization of an appropriate model of the human cardiovascular system. Given that different patients and experimental data yield varying performance of the cardiovascular models (CVMs) and their respective parameters, it becomes crucial to assess the reliable operation of controllers. This study aims to assess the performance and reliability of various LVAD controllers using two state-of-the-art CVMs, with a specific focus on the impact of interpatient variability. Extreme test cases were employed for evaluation, incorporating both in silico and in vitro experiments. The differences observed in response between the studied CVMs can be attributed to variations in their structures and parameters. Specifically, the model with smaller compartments exhibits higher overload rates, whereas the other model demonstrates increased sensitivity to changes in preload and afterload, resulting in more frequent suction events (34.2% vs. 8.5% for constant speed mode). These findings along with the varying response of the tested controllers highlight the influence of the selected CVM emphasizing the need to test each LVAD controller with multiple CVMs or, at least, a range of parameter sets. This approach ensures sufficient evaluation of the controller's efficacy in addressing interpatient variability.

Towards practical implementation of molecular communication: A cost‐effective experimental platform based on the human circulatory system

AbstractRecently, there have been numerous studies exploring the field of molecular communication (MC) systems. However, due to the high cost and limited availability of advanced micro/nano‐scale equipment, most of these works remain purely theoretical, with only a few being examined through experimental platforms. Additionally, the absence of a suitable model for flow‐assisted MC‐based systems poses another significant challenge. This research focuses on the potential applications of MC technology within the human body. To address the limitations mentioned above, a closed‐loop experimental platform based on the human circulatory system is proposed. This platform offers a cost‐effective and accessible solution for studying MC systems. The implementation process involves a brief discussion about the circulatory system model. By varying flow rates and the quantity of released information particles, channel impulse responses are obtained. Based on the observed experimental data, the authors have successfully developed a new theoretical model that accurately fits the experimental data. The model demonstrates a strong level of agreement with the observed results. This model demonstrates its suitability for flow‐assisted MC‐based systems.

Non-invasive fractional flow reserve derived from reduced-order coronary model and machine learning prediction of stenosis flow resistance

Background and objectiveRecently, computational fluid dynamics enables the non-invasive calculation of fractional flow reserve (FFR) based on 3D coronary model, but it is time-consuming. Currently, machine learning technique has emerged as an efficient and reliable approach for prediction, which allows saving a lot of analysis time. This study aimed at developing a simplified FFR prediction model for rapid and accurate assessment of functional significance of stenosis. MethodsA reduced-order lumped parameter model (LPM) of coronary system and cardiovascular system was constructed for rapidly simulating coronary flow, in which a machine learning model was embedded for accurately predicting stenosis flow resistance at a given flow from anatomical features of stenosis. Importantly, the LPM was personalized in both structures and parameters according to coronary geometries from computed tomography angiography and physiological measurements such as blood pressure and cardiac output for personalized simulations of coronary pressure and flow. Coronary lesions with invasive FFR ≤ 0.80 were defined as hemodynamically significant. ResultsA total of 91 patients (93 lesions) who underwent invasive FFR were involved in FFR derived from machine learning (FFRML) calculation. Of the 93 lesions, 27 lesions (29.0%) showed lesion-specific ischemia. The average time of FFRML simulation was about 10 min. On a per-vessel basis, the FFRML and FFR were significantly correlated (r = 0.86, p < 0.001). The diagnostic accuracy, sensitivity, specificity, positive predictive value and negative predictive value were 91.4%, 92.6%, 90.9%, 80.6% and 96.8%, respectively. The area under the receiver-operating characteristic curve of FFRML was 0.984. ConclusionIn this selected cohort of patients, the FFRML improves the computational efficiency and ensures the accuracy. The favorable performance of FFRML approach greatly facilitates its potential application in detecting hemodynamically significant coronary stenosis in future routine clinical practice.

Impact of hypertension on mean transvalvular gradients in aortic stenosis Lessons learned from in silico modelling

Abstract Introduction The influence of hypertension on the diagnostic assessment of aortic stenosis (AS) severity is unclear, yet clinically relevant. To clarify the effect of hypertension on transvalvular gradients, requires a better understanding of the impact that blood pressure change has on mean flow rate. Also, the effect of various degrees of AS severity, the valve geometry and intrinsic left ventricular contractile function (elastance) on this interaction, needs to be clarified. Existing clinical research on this topic is limited by not incorporating the full complement of factors that influences the directionality and magnitude of mean transvalvular gradient change for any given change in blood pressure. Purpose To use validated flow dynamics modelling grounded in fundamental physics principles to create a digital platform where various parameters can be manipulated to quantify the effects of bloodpressure change on mean transvalvular gradients in various pathological states. Methods A validated, zero-dimensional electro-hydraulic analogue computer model of the human cardiovascular circulatory system was generated. It was used to assess the impact of blood pressure changes on left ventricular pressure and transvalvular gradients at various flow rates, left ventricular elastances, a range of aortic valve areas and for different aortic valve morphologies. Results Mean transvalvular gradients are flow dependent. The impact of hypertension on mean flow rate (MFR), and hence on mean gradient (MG), is influenced by the aortic valve area as well as the underlying left ventricular elastance (Figure 1). Subsequently, the relationship between MFR and MG could be modelled (Figure 2). Generally, for a given change in systemic arterial pressure, the impact on MG will be the most marked for lower flow rate states such as is expected in more severe degrees of AS and for worse intrinsic left ventricular (LV) contractility. Furthermore, for a constant hemodynamic valve area, the hydraulic valve orifice area was found to be smaller in rheumatic AS compared to degenerative AS. This results in less susceptibility to blood pressure induced changes in mean transvalvular gradient. Similarly, the size of the aortic root was found to be inversely related to the susceptibility to induce a change in mean gradient for a change in blood pressure. Conclusion The interaction between hypertension and mean gradients in AS is complex. The current work places previous recommendations in perspective by quantifying the magnitude of the effect that a change in blood pressure has on mean gradient in various pathophysiological states. By doing so it provides more granularity into this multifaceted interaction compared to previously oversimplified deductions from clinical studies. The findings of this study creates a basic science framework based on first principles that dictates the scope of parameters that should be considered in future clinical research on the topic.Modelling 1Modelling 2

Nonlinear Closed-Loop Predictive Control of Heart Rate and Blood Pressure Using Vagus Nerve Stimulation: An In Silico Study.

We propose a nonlinear model-based control technique for regulating the heart rate and blood pressure using vagus nerve neuromodulation. The closed-loop framework is based on an in silico model of the rat cardiovascular system for the simulation of the hemodynamic response to multi-location vagal nerve stimulation. The in silico model is derived by compartmentalizing the various physiological components involved in the closed-loop cardiovascular system with intrinsic baroreflex regulation to virtually generate nominal and hypertension-related heart dynamics of rats in rest and exercise states. The controller, using a reduced cycle-averaged model, monitors the outputs from the in silico model, estimates the current state of the reduced model, and computes the optimum stimulation locations and the corresponding parameters using a nonlinear model predictive control algorithm. The results demonstrate that the proposed control strategy is robust with respect to its ability to handle setpoint tracking and disturbance rejection in different simulation scenarios.

Data-driven discovery of sparse dynamical model of cardiovascular system for model predictive control

Cardiovascular diseases remain the leading cause of death globally. In recent years, vagal nerve stimulation (VNS) has shown promising results in the treatment of a number of cardiovascular diseases. In this approach, mild electrical pulses are sent to the brain via the vagus nerve. This open-loop neurostimulation, however, leads to various side effects due to physiological and inter-patient variability and therefore a closed-loop delivery strategy of electrical pulses that accounts for this variability is desired. In this context, we envision data-driven sparse dynamical model parameterized by patient-specific data as appropriate for use in closed loop controller design. In this work, we build a dynamical model for mean arterial pressure and heart rate using the method sparse identification of nonlinear dynamics (SINDy). As a proxy for real datasets or measurements from a patient, we simulate a mechanistic model from the literature and then discover a data-driven model for predicting mean arterial pressure and heart rate in response to neural stimulus. This discovered model is then used to design a controller to be implemented in closed-loop via model predictive control. We observe that this data-driven model is interpretable, consistent with experiments, provides insights on the sensitivity of different stimulation locations and simplifies the formulation of the optimal control problem. Noting the set-point tracking performance of this closed-loop model-based controller that uses this discovered model, we conclude that the model is adequate in capturing the dynamics of a highly nonlinear cardiovascular system for the purpose of optimal predictive controller design.

FRACTIONAL ORDER MODEL FOR CARDIOVASCULAR SYSTEM USING HEURISTIC OPTIMIZATION APPROACH

Cardiovascular diseases like atherosclerosis, peripheral arterial disease, coronary heart disease, etc. are dangerous and hence early detection of such diseases is important in the field of medical sciences. The existing methods used for diagnosing diseases are time-consuming and highly expensive. On the other hand, experimentation with Cardiovascular System (CVS) for the analysis and decision making for treatment is unsafe and hence it is important to develop an accurate CVS model. It is observed from the existing integer order cardiovascular models that the viscoelastic property of four chambers is not taken into consideration. To accommodate these properties, this paper proposes a fractional order model by introducing fractionality to the dynamical equation of CVS. Further, an optimization method is presented to obtain the fractionality of different chambers by minimizing the integral square error between clinical data of healthy human and model output using Cuckoo search, Firefly and Accelerated particle swarm algorithms. The results indicate that fractional models are better than integer order CVS models and specifically, the Firefly algorithm provides a better fractional model than the models obtained from other algorithms. Further, the best model obtained using firefly algorithm is considered for simulating the diseases (i) increased arterial stiffness and (ii) atherosclerosis.

Mechanistic insights on age-related changes in heart-aorta-brain hemodynamic coupling using a pulse wave model of the entire circulatory system.

Age-related changes in aortic biomechanics can impact the brain by reducing blood flow and increasing pulsatile energy transmission. Clinical studies have shown that impaired cardiac function in patients with heart failure is associated with cognitive impairment. Although previous studies have attempted to elucidate the complex relationship between age-associated aortic stiffening and pulsatility transmission to the cerebral network, they have not adequately addressed the effect of interactions between aortic stiffness and left ventricle (LV) contractility (neither on energy transmission nor on brain perfusion). In this study, we use a well-established and validated one-dimensional blood flow and pulse wave computational model of the circulatory system to address how age-related changes in cardiac function and vasculature affect the underlying mechanisms involved in the LV-aorta-brain hemodynamic coupling. Our results reveal how LV contractility affects pulsatile energy transmission to the brain, even with preserved cardiac output. Our model demonstrates the existence of an optimal heart rate (near the normal human heart rate) that minimizes pulsatile energy transmission to the brain at different contractility levels. Our findings further suggest that the reduction in cerebral blood flow at low levels of LV contractility is more prominent in the setting of age-related aortic stiffening. Maintaining optimal blood flow to the brain requires either an increase in contractility or an increase in heart rate. The former consistently leads to higher pulsatile power transmission, and the latter can either increase or decrease subsequent pulsatile power transmission to the brain.NEW & NOTEWORTHY We investigated the impact of major aging mechanisms of the arterial system and cardiac function on brain hemodynamics. Our findings suggest that aging has a significant impact on heart-aorta-brain coupling through changes in both arterial stiffening and left ventricle (LV) contractility. Understanding the underlying physical mechanisms involved here can potentially be a key step for developing more effective therapeutic strategies that can mitigate the contributions of abnormal LV-arterial coupling toward neurodegenerative diseases and dementia.

Pengembangan Prototipe Alat Peraga Styrofoam Sirkulasi Peredaran Darah Manusia pada Pembelajaran IPA SMP

One of the obstacles for students when studying the human circulatory system is the difficulty of students in distinguishing the large circulatory system and the small circulatory system. The purpose of developing the prototype of props is to make students better understand in real and detailed terms about the blood circulation process in the human body, so far, students have only used images as visual media, and students can be motivated and learning is not spared, so that student learning outcomes can improve. The purpose of making a circulatory system model of children's learning motivation is to provide a concrete picture of information and new ideas and unite the material that the teacher will convey to the knowledge that will be given to the child, so that the material presented by the teacher can be well received by students. Props are made using styrofoam material, which is designed in such an attractive way to be representative of human blood circulation. This research is part of design-research with small-scale class trials. A small-scale trial of a prop prototype involved seven junior high school students in Bojonegoro Regency, East Java. After being given an explanation and participants tried the prop prototype, they were given a questionnaire to measure students' interest and motivation for the prototype props that had been developed previously. The result obtained from this study is that students have new experiences by learning to use props. Participants were very interested and motivated in using prototypes of human circulatory circulation styrofoam props. The results of small-scale trials indicate that the props prototypes that have been developed are suitable for use in large-scale trials. Keywords: Teaching aids, blood circulation, science learning

A new synergistic model for simulating exercise incorporating control mechanisms at cellular and organ scales

The physiological response of the cardio-vascular system (CVS) to physical activity is of great importance to those working in sporting research and has profound consequences for the health and well-being of people. Coronary vasodilation and the physiological mechanisms involved in exercise have frequently been the focus of numerical models for simulating exercise. This is partly achieved using the time-varying-elastance (TVE) theory, which prescribes the pressure-volume relationship of the ventricle as a periodic function of time, tuned using empirical data. The empirical foundations of the TVE method however, and its suitability for CVS modelling are frequently questioned. To overcome this challenge, we adopt a different synergistic approach in which a model for the microscale heart muscle (myofibers) activity is embedded within a macro organ-scale CVS model. We developed such a synergistic model by including the coronary flow and various control mechanisms at the circulation level through feedback and feedforward means, and at the microscale (contractile) through the regulation of ATP availability and myofiber force depending on exercise intensity or heart rate. The coronary flow produced by the model displays the well-known 2-phase character of the flow, which is preserved under exercise. The model is tested by simulating reactive hyperemia, which is a transient occlusion of the coronary flow, successfully reproducing the additional coronary flow following the block removal. On-transient exercise results reveal a rise in both cardiac output and mean ventricle pressure as expected. The stroke volume increases initially, but then declines during the latter period of HR rise, corresponding with one of the main physiological responses to exercise. The pressure-volume loop expands during exercise, as systolic pressure rises. The Myocardial oxygen demand increases during exercise and the coronary blood supply increases in response, causing an excess of oxygen supply to the heart. Off-transient exercise recovery is largely a reverse of this response, although the behaviour is slightly more varied, with sudden spikes in coronary resistance. Different levels of fitness and exercise intensity are tested and reveal that the stroke volume rises until a level of myocardial oxygen demand is reached at which point it declines. This level of demand is independent of fitness or exercise intensity. An advantage of our model is demonstrated in the correspondence between the micro and organ scale mechanics so that cellular pathologies can be traced from exercise performance with relatively little computational or experimental expense.

P13: “Linearizing the Heart”: Application of an Autoencoder Network to Identify Koopman Embeddings in Hemodynamic Signals

System linearization helps researchers to understand and predict the behavior of complex systems like the cardiovascular system. This understanding is essential for developing medical devices or physiological cardiac control algorithms. Aim of this study was to use a neural network approach to obtain a linearized model of the cardiovascular system dynamics. An autoencoder network was used to learn Koopman embeddings from a dataset of simulated heartbeats obtained from a highly nonlinear lumped-parameter model of the human cardiovascular system. The dataset contained 18 hemodynamic signals of 800 separate heartbeats generated using varying initial conditions and included volumes, pressures, and flows of the heart chambers and the systemic circulation. All data were z-normalized, and the network was trained using the Adam optimizer. The decoder and encoder comprised 5 layers, both with progressively increasing/decreasing numbers of nodes (27-211). The latent space had the same number of dimensions as the input comprising 18 hemodynamic signals. An auxiliary network with 4 layers (28 nodes each) operating on the latent space was used to identify the Koopman operator. The results showed that, although the eigenvalues of the Koopman operator were allowed to vary, they remained about constant for all heartbeats, indicating the convergence towards a linear representation of the system’s dynamics. The overall average prediction error quantified as relative RMSE for 100 unseen heartbeats, given only the initial condition, was 5.4%, showing a good capture of the system’s dynamics. The results are consistent with previous work on Koopman linearization, adding to the growing body of evidence supporting the utility of this approach. Using autoencoders to learn Koopman embeddings from simulated heartbeats and thus identify a linear representation of the cardiovascular system’s dynamics is novel. This promising approach could be particularly useful for the development of physiological cardiac control algorithms.

A Physiological Control for Artificial Heart Pump based on Adaptive Regulation of Heart Rate and the improved BAS Algorithm Optimization

In order to solve the problem that the traditional artificial heart pump has poor dynamic bionic performance and cannot respond to blood perfusion in real time according to the patient’s state, this paper proposes a new physiological control method based on adaptive adjustment of heart rate. Firstly, a coupled model of the cardiovascular circulatory system and artificial heart pump was established, and the correctness of the model is verified by hemodynamic simulation. Secondly, according to the model-free adaptive control theory, the artificial heart pump physiological control strategy for adaptive regulation of heart rate is designed, including pseudo partial derivative estimation algorithm and adaptive control law. Then, the parameters of the adaptive controller are optimized by the improved beetle antennae search algorithm. Finally, the auxiliary effect and dynamic performance of the adaptive control strategy are studied by numerical methods. The results show that the physiological control method based on adaptive regulation of heart rate realizes the adaptive modulation of heart rate and blood flow, which can meet the needs of blood perfusion in different states and help to promote the recovery of damaged heart.

P80: Adding Intra-Aortic Balloon Pump Counter-Pulsation To Vasodilator Therapy Results In Better Organ Perfusion In Patients With Biventricular Heart Failure Awaiting A Heart Transplant

Background: Significant right ventricular dysfunction with elevated central venous pressure is associated with poor survival in heart failure patients with reduced ejection fraction (HFrEF). Heart transplant is often the only option for this group of patients, as they are not candidates for left ventricular assist device therapy. Medical management of HFrEF involves lowering the systemic afterload with vasodilators. While this therapy is very effective in isolated left ventricular dysfunction, in patients with biventricular dysfunction and high venous pressure, vasodilation can lead to low organ perfusion pressure, resulting in organ dysfunction. Organ perfusion pressure is calculated as the difference between mean arterial pressure and central venous pressure. We hypothesized that combining intra-aortic balloon pump (IABP) counter-pulsation with vasodilator therapy might be a good clinical option for stabilizing these patients while awaiting a heart transplant. Methods: To understand the hemodynamics of vasodilator and IABP therapy in biventricular heart failure, we used a closed-loop multiscale model of the entire cardiovascular system, including a one-dimensional model of the arterial tree coupled with a lumped model of the heart and IABP counter-pulsation. In addition, a baroreceptor model was also used to capture the physiological changes secondary to ventricular dysfunction realistically. Results: Our mathematical model accurately captures the features of the aortic pressure waveform with IABP support (Figure 1) and the hemodynamic consequences of the onset of right ventricular dysfunction (Table 1). Biventricular dysfunction leads to lower cardiac output and organ perfusion pressure when compared with isolated left ventricular dysfunction. Vasodilation during biventricular dysfunction increases the cardiac output but reduces the organ perfusion pressure further. IABP counter-pulsation increases cardiac output along with higher mean arterial pressure and hence higher organ perfusion pressure. The best results are seen when vasodilators are used in conjunction with IABP, where a “sweet spot” of maximal increase in cardiac output with the best possible organ perfusion pressure is obtained, and this combined additive effect of IABP counter-pulsation and vasodilation is also reflected across the renal vasculature (Table 1). Conclusion: Vasodilation with high venous pressure can lead to low organ perfusion pressure and consequent organ dysfunction. Fine-tuning IABP counter-pulsation and vasodilator seems to confer a substantial hemodynamic benefit in this setting.Figure 1. Simulated aortic pressure waveform with IABP support