Initial clinical validation of a hybrid in silico—in vitro cardiorespiratory simulator for comprehensive testing of mechanical circulatory support systems

Understanding the complexity of cardiogenic shock management: the added value of advanced computational modeling

PURPOSE OF REVIEW

The purpose of this review is to explain the value of computational physiological modeling for in-depth understanding of the complex derangements of cardiopulmonary pathophysiology during cardiogenic shock, particularly when treated with temporary mechanical circulatory support (tMCS) devices.

RECENT FINDINGS

Computational physiological models have evolved in recent years and can provide a high degree of clinical realism in the simulation of cardiogenic shock and related conservative and interventional therapies. These models feature a large spectrum of practically relevant hemodynamic and respiratory parameters tunable to patient-specific disease states as well as adjustable to medical therapies and support device settings. Current applications work in real-time and can operate on an ordinary computer, laptop or mobile device.

SUMMARY

The use of computational physiological models is increasingly appreciated for educational purposes as they help to understand the complexity of cardiogenic shock, especially when sophisticated management of tMCS is involved in addition to multimodal critical care support. Practical implementation of computational models as clinical decision support tools at the bedside is at the horizon but awaits rigorous clinical validation.

A patient-specific echogenic soft robotic left ventricle embedded into a closed-loop cardiovascular simulator for advanced device testing

Cardiovascular medical devices undergo a large number of pre- and post-market tests before their approval for clinical practice use. Sophisticated cardiovascular simulators can significantly expedite the evaluation process by providing a safe and controlled environment and representing clinically relevant case scenarios. The complex nature of the cardiovascular system affected by severe pathologies and the inherently intricate patient-device interaction creates a need for high-fidelity test benches able to reproduce intra- and inter-patient variability of disease states. Therefore, we propose an innovative cardiovascular simulator that combines in silico and in vitro modeling techniques with a soft robotic left ventricle. The simulator leverages patient-specific and echogenic soft robotic phantoms used to recreate the intracardiac pressure and volume waveforms, combined with an in silico lumped parameter model of the remaining cardiovascular system. Three different patient-specific profiles were recreated, to assess the capability of the simulator to represent a variety of working conditions and mechanical properties of the left ventricle. The simulator is shown to provide a realistic physiological and anatomical representation thanks to the use of soft robotics combined with in silico modeling. This tool proves valuable for optimizing and validating medical devices and delineating specific indications and boundary conditions.

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.

An in vitro model to study suction events by a ventricular assist device: validation with clinical data

Introduction: Ventricular assist devices (LVADs) are a valuable therapy for end-stage heart failure patients. However, some adverse events still persist, such as suction that can trigger thrombus formation and cardiac rhythm disorders. The aim of this study is to validate a suction module (SM) as a test bench for LVAD suction detection and speed control algorithms. Methods: The SM consists of a latex tube, mimicking the ventricular apex, connected to a LVAD. The SM was implemented into a hybrid in vitro-in silico cardiovascular simulator. Suction was induced simulating hypovolemia in a profile of a dilated cardiomyopathy and of a restrictive cardiomyopathy for pump speeds ranging between 2,500 and 3,200 rpm. Clinical data collected in 38 LVAD patients were used for the validation. Clinical and simulated LVAD flow waveforms were visually compared. For a more quantitative validation, a binary classifier was used to classify simulated suction and non-suction beats. The obtained classification was then compared to that generated by the simulator to evaluate the specificity and sensitivity of the simulator. Finally, a statistical analysis was run on specific suction features (e.g., minimum impeller speed pulsatility, minimum slope of the estimated flow, and timing of the maximum slope of the estimated flow). Results: The simulator could reproduce most of the pump waveforms observed in vivo. The simulator showed a sensitivity and specificity and of 90.0% and 97.5%, respectively. Simulated suction features were in the interquartile range of clinical ones. Conclusions: The SM can be used to investigate suction in different pathophysiological conditions and to support the development of LVAD physiological controllers.