Systematic Review and Meta-Analysis of the Effects of Age, Body Size and Exercise on Cardiovascular Parameters

Studies pertaining to the averages of cardiovascular parameters of the general population and their change with respect to a single variable have been conducted widely, but information about how multiple variables affect these cardiovascular parameters is still limited. This study builds multi-variable predictive models of cardiovascular parameters with respect to age, height, weight, body surface area, body mass index, and exercise intensity. The results of this study could help clinicians make better diagnoses and be useful for researchers by providing reference values for tuning computational models and boundary conditions

A protocol for automated a posteriori adaptive meshing with SimVascular: a test case

Objective Operational details regarding the use of the adaptive meshing (AM) algorithm available in the SimVascular package are scarce despite its application in several studies. Lacking these details, novice users of the AM algorithm may experience undesirable outcomes post-adaptation such as increases in mesh error metrics, unpredictable increases in mesh size, and losses in geometric fidelity. Here we present a test case using our proposed iterative protocol that will help prevent these undesirable outcomes and enhance the utility of the AM algorithm. We present three trials (conservative, moderate, and aggressive settings) applied to a scenario modelling a Fontan junction with a patient-specific geometry and physiologically realistic boundary conditions. Results In all three trials, an overall reduction in mesh error metrics is observed (range 47%–86%). The increase in the number of elements through each adaptation never exceeded the mesh size of the pre-adaptation mesh by one order of magnitude. In all three trials, the protocol resulted in consistent, repeatable improvements in mesh error metrics, no losses of geometric fidelity and steady increments in the number of elements in the mesh. Our proposed protocol prevented the aforementioned undesirable outcomes and can potentially save new users considerable effort and computing resources

Multiscale Modeling of Cardiovascular Flows

Simulations of blood flow in the cardiovascular system offer investigative and predictive capabilities to augment current clinical tools. Using image-based modeling, the Navier-Stokes equations can be solved to obtain detailed 3-dimensional hemodynamics in patient-specific anatomical models. Relevant parameters such as wall shear stress and particle residence times can then be calculated from the 3D results and correlated with clinical data for treatment planning and device evaluation. Reduced-order models such as open or closed loop 0D lumped-parameter models can simulate the dynamic behavior of the circulatory system using an analogy to electrical circuits. When coupled to 3D simulations as boundary conditions, they produce physiologically realistic pressure and flow conditions in the 3D domain. We describe fundamentals and current state of the art of patient-specific, multi-scale computational modeling approaches applied to cardiovascular disease. These tools enable investigations of hemodynamics reflecting individual patients physiology, and we provide several illustrative case studies. These methods can supplement current clinical measurement and imaging capabilities and provide predictions of patient outcomes for surgical planning and risk stratification

Factors Affecting Cardiovascular Physiology in Cardiothoracic Surgery: Implications for Lumped-Parameter Modeling

Cardiothoracic surgeries are complex procedures during which the patient cardiovascular physiology is constantly changing due to various factors. Physiological changes begin with the induction of anesthesia, whose effects remain active into the postoperative period. Depending on the surgery, patients may require the use of cardiopulmonary bypass and cardioplegia, both of which affect postoperative physiology such as cardiac index and vascular resistance. Complications may arise due to adverse reactions to the surgery, causing hemodynamic instability. In response, fluid resuscitation and/or vasoactive agents with varying effects may be used in the intraoperative or postoperative periods to improve patient hemodynamics. These factors have important implications for lumped-parameter computational models which aim to assist surgical planning and medical device evaluation. Patient-specific models are typically tuned based on patient clinical data which may be asynchronously acquired through invasive techniques such as catheterization, during which the patient may be under the effects of drugs such as anesthesia. Due to the limited clinical data available and the inability to foresee short-term physiological regulation, models often retain preoperative parameters for postoperative predictions; however, without accounting for the physiologic changes that may occur during surgical procedures, the accuracy of these predictive models remains limited. Understanding and incorporating the effects of these factors in cardiovascular models will improve the model fidelity and predictive capabilities

A Protocol for Coupling Volumetrically Dynamic In vitro Experiments to Numerical Physiology Simulation for A Hybrid Cardiovascular Model

Objective: The Physiology Simulation Coupled Experiment (PSCOPE) is a hybrid modeling framework that enables a physical fluid experiment to operate in the context of a closed-loop computational simulation of cardiovascular physiology. Previous PSCOPE methods coupled rigid experiments to a lumped parameter network (LPN) of physiology but are incompatible with volumetrically dynamic experiments where fluid volume varies periodically. We address this limitation by introducing a method capable of coupling rigid, multi-branch, and volumetrically dynamic in-vitro experiments to an LPN. Methods: Our proposed method utilizes an iterative weighted-averaging algorithm to identify the unique solution waveforms for a given PSCOPE model. We confirm the accuracy of these PSCOPE solutions by integrating mathematical surrogates of in vitro experiments directly into the LPN to derive reference solutions, which serve as the gold standard to validate the solutions obtained from using our proposed method to couple the same mathematical surrogates to the LPN. Finally, we illustrate a practical application of our PSCOPE method by coupling an in-vitro renal circulation experiment to the LPN. Results: Compared to the reference solution, the normalized root mean square error of the flow and pressure waveforms were 0.001%∼0.55%, demonstrating the accuracy of the coupling method. Conclusion: We successfully coupled the in-vitro experiment to the LPN, demonstrating the real-world performance within the constraints of sensor and actuation limitations in the physical experiment. Significance: This study introduces a PSCOPE method that can be used to investigate medical devices and anatomies that exhibit periodic volume changes, expanding the utility of the hybrid framework

Development of a Physical Windkessel Module to Re-Create \u3ci\u3eIn Vivo\u3c/i\u3e Vascular Flow Impedance for \u3ci\u3eIn Vitro\u3c/i\u3e Experiments

To create and characterize a physical Windkessel module that can provide realistic and predictable vascular impedances for in vitro flow experiments used for computational fluid dynamics validation, and other investigations of the cardiovascular system and medical devices. We developed practical design and manufacturing methods for constructing flow resistance and capacitance units. Using these units we assembled a Windkessel impedance module and defined its corresponding analytical model incorporating an inductance to account for fluid momentum. We tested various resistance units and Windkessel modules using a flow system, and compared experimental measurements to analytical predictions of pressure, flow, and impedance. The resistance modules exhibited stable resistance values over wide ranges of flow rates. The resistance value variations of any particular resistor are typically within 5% across the range of flow that it is expected to accommodate under physiologic flow conditions. In the Windkessel impedance modules, the measured flow and pressure waveforms agreed very favorably with the analytical calculations for four different flow conditions used to test each module. The shapes and magnitudes of the impedance modulus and phase agree well between experiment and theoretical values, and also with those measured in vivo in previous studies. The Windkessel impedance module we developed can be used as a practical tool to provide realistic vascular impedance for in vitro cardiovascular studies. Upon proper characterization of the impedance module, its analytical model can accurately predict its measured behavior under different flow conditions

Cardiovascular Biomechanical Models

Computational and in vitro methods offer powerful means to model the cardiovascular system and investigate biomechanics related to blood flow, cardiovascular tissue, and medical devices. These models can be constructed to directly describe human anatomy and physiology, and can be more highly controlled compared to animal models. Low-order models composed of lumped-parameter elements and simplified descriptions of cardiac function can capture the global physiology, while high-order models exhibiting detailed 3D anatomy and dynamics can provide highly realistic replication of biomechanical interactions in a small region of the circulation. Multiscale models offer the freedom to capture biomechanics in different regions at the desired level of details. In this chapter we describe the fundamentals and the current state-of-the-art of model construction both in the computational and in vitro approaches. These models have been applied to understand the physiologic impacts of medical device implantations, predict surgical outcomes, and investigate hemodynamics in vascular diseases; we present several illustrative case studies here. Finally we examine the pros and cons of each type of models and discuss the considerations in proper model selection for a research study

A Hybrid Experimental‐Computational Modeling Framework For Cardiovascular Device Testing

Significant advances in biomedical science often leverage powerful computational and experimental modeling platforms. We present a framework named physiology simulation coupled experiment (“PSCOPE”) that can capitalize on the strengths of both types of platforms in a single hybrid model. PSCOPE uses an iterative method to couple an in vitro mock circuit to a lumped-parameter numerical simulation of physiology, obtaining closed-loop feedback between the two. We first compared the results of Fontan graft obstruction scenarios modeled using both PSCOPE and an established multiscale computational fluid dynamics method; the normalized root-mean-square error values of important physiologic parameters were between 0.1% and 2.1%, confirming the fidelity of the PSCOPE framework. Next, we demonstrate an example application of PSCOPE to model a scenario beyond the current capabilities of multiscale computational methods—the implantation of a Jarvik 2000 blood pump for cavopulmonary support in the single-ventricle circulation; we found that the commercial Jarvik 2000 controller can be modified to produce a suitable rotor speed for augmenting cardiac output by approximately 20% while maintaining blood pressures within safe ranges. The unified modeling framework enables a testing environment which simultaneously operates a medical device and performs computational simulations of the resulting physiology, providing a tool for physically testing medical devices with simulated physiologic feedback

An algorithm for coupling multibranch in vitro experiment to numerical physiology simulation for a hybrid cardiovascular model

The hybrid cardiovascular modeling approach integrates an in vitro experiment with a computational lumped‐parameter simulation, enabling direct physical testing of medical devices in the context of closed‐loop physiology. The interface between the in vitro and computational domains is essential for properly capturing the dynamic interactions of the two. To this end, we developed an iterative algorithm capable of coupling an in vitro experiment containing multiple branches to a lumped‐parameter physiology simulation. This algorithm identifies the unique flow waveform solution for each branch of the experiment using an iterative Broyden\u27s approach. For the purpose of algorithm testing, we first used mathematical surrogates to represent the in vitro experiments and demonstrated five scenarios where the in vitro surrogates are coupled to the computational physiology of a Fontan patient. This testing approach allows validation of the coupling result accuracy as the mathematical surrogates can be directly integrated into the computational simulation to obtain the “true solution” of the coupled system. Our algorithm successfully identified the solution flow waveforms in all test scenarios with results matching the true solutions with high accuracy. In all test cases, the number of iterations to achieve the desired convergence criteria was less than 130. To emulate realistic in vitro experiments in which noise contaminates the measurements, we perturbed the surrogate models by adding random noise. The convergence tolerance achievable with the coupling algorithm remained below the magnitudes of the added noise in all cases. Finally, we used this algorithm to couple a physical experiment to the computational physiology model to demonstrate its real‐world applicability

A Real-Time Programmable Pulsatile Flow Pump for In Vitro Cardiovascular Experimentation

Benchtop in vitro experiments are valuable tools for investigating the cardiovascular system and testing medical devices. Accurate reproduction of the physiologic flow waveforms at various anatomic locations is an important component of these experimental methods. This study discusses the design, construction, and testing of a low-cost and fully programmable pulsatile flow pump capable of continuously producing unlimited cycles of physiologic waveforms. It consists of a gear pump actuated by an AC servomotor and a feedback algorithm to achieve highly accurate reproduction of flow waveforms for flow rates up to 300 ml/s across a range of loading conditions. The iterative feedback algorithm uses the flow error values in one iteration to modify the motor control waveform for the next iteration to better match the desired flow. Within four to seven iterations of feedback, the pump replicated desired physiologic flow waveforms to within 2% normalized RMS error (for flow rates above 20 mL/s) under varying downstream impedances. This pump device is significantly more affordable (∼10% of the cost) than current commercial options. More importantly, the pump can be controlled via common scientific software and thus easily implemented into large automation frameworks