Development of suction detection algorithms for a left ventricular assist device from patient data

Real-time physiological environment emulation for the Istanbul heart ventricular assist device via acausal cardiovascular modeling

BACKGROUND AND OBJECTIVES

The cost and complexity associated with animal testing are significantly reduced by using mock circulatory loops prior. Novel mock circulatory loops allow us to test biomedical devices preclinically due to their flexibility, scalability, and cost-effectiveness. The presented work describes the development of a hardware-in-the-loop platform to emulate human physiology for the Istanbul Heart (iHeart-II) LVAD.

METHODS

A closed-loop system is developed whereby the effect of the LVAD on the heart and vice versa can be studied. An acausal model of the cardiovascular system is calibrated to emulate advanced-stage heart failure. A new prototype of the iHeart-II LVAD is connected between two air-actuated chambers emulating the left ventricle and aortic chambers with PID controllers tracking numerically modeled pressures from the in silico model. A lead-lag compensator is used to maintain fluid level. Controllers are tuned using nonlinear Hammerstein-Weiner models identified using open-loop data. The iHeart-II LVAD is operated at various speeds in its operational range, and the resulting hemodynamics are visualized in real time.

RESULTS

Hemodynamic variables, such as LVAD flow rate, aortic, left ventricular, and pulse pressure, demonstrate trends similar to clinical observations. The iHeart-II LVAD achieves hemodynamic normalization at ~3500 rpm for the emulated condition.

CONCLUSIONS

A novel evaluation methodology is adopted to study the performance of the iHeart LVAD under advanced-stage heart failure emulation. The models and controllers used in the platform are readily replicable to facilitate VAD research, pedagogy, design, and development.

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.

JM, Shickel B, Rashidi P, Bihorac A, Ahmed MM, Mardini MT. Artificial intelligence guidance of advanced heart failure therapies: A systematic scoping review

Introduction

Artificial intelligence can recognize complex patterns in large datasets. It is a promising technology to advance heart failure practice, as many decisions rely on expert opinions in the absence of high-quality data-driven evidence.

Methods

We searched Embase, Web of Science, and PubMed databases for articles containing "artificial intelligence," "machine learning," or "deep learning" and any of the phrases "heart transplantation," "ventricular assist device," or "cardiogenic shock" from inception until August 2022. We only included original research addressing post heart transplantation (HTx) or mechanical circulatory support (MCS) clinical care. Review and data extraction were performed in accordance with PRISMA-Scr guidelines.

Results

Of 584 unique publications detected, 31 met the inclusion criteria. The majority focused on outcome prediction post HTx (n = 13) and post durable MCS (n = 7), as well as post HTx and MCS management (n = 7, n = 3, respectively). One study addressed temporary mechanical circulatory support. Most studies advocated for rapid integration of AI into clinical practice, acknowledging potential improvements in management guidance and reliability of outcomes prediction. There was a notable paucity of external data validation and integration of multiple data modalities.

Conclusion

Our review showed mounting innovation in AI application in management of MCS and HTx, with the largest evidence showing improved mortality outcome prediction.

A Sensorless Modular Multiobjective Control Algorithm for Left Ventricular Assist Devices: A Clinical Pilot Study

Background

Contemporary Left Ventricular Assist Devices (LVADs) mainly operate at a constant speed, only insufficiently adapting to changes in patient demand. Automatic physiological speed control promises tighter integration of the LVAD into patient physiology, increasing the level of support during activity and decreasing support when it is excessive.

Methods

A sensorless modular control algorithm was developed for a centrifugal LVAD (HVAD, Medtronic plc, MN, USA). It consists of a heart rate-, a pulsatility-, a suction reaction—and a supervisor module. These modules were embedded into a safe testing environment and investigated in a single-center, blinded, crossover, clinical pilot trial (clinicaltrials.gov, NCT04786236). Patients completed a protocol consisting of orthostatic changes, Valsalva maneuver and submaximal bicycle ergometry in constant speed and physiological control mode in randomized sequence. Endpoints for the study were reduction of suction burden, adequate pump speed and flowrate adaptations of the control algorithm for each protocol item and no necessity for intervention via the hardware safety systems.

Results

A total of six patients (median age 53.5, 100% male) completed 13 tests in the intermediate care unit or in an outpatient setting, without necessity for intervention during control mode operation. Physiological control reduced speed and flowrate during patient rest, in sitting by a median of −75 [Interquartile Range (IQR): −137, 65] rpm and in supine position by −130 [−150, 30] rpm, thereby reducing suction burden in scenarios prone to overpumping in most tests [0 [−10, 2] Suction events/minute] in orthostatic upwards transitions and by −2 [−6, 0] Suction events/min in Valsalva maneuver. During submaximal ergometry speed was increased by 86 [31, 193] rpm compared to constant speed for a median flow increase of 0.2 [0.1, 0.8] L/min. In 3 tests speed could not be increased above constant set speed due to recurring suction and in 3 tests speed could be increased by up to 500 rpm with a pump flowrate increase of up to 0.9 L/min.

Conclusion

In this pilot study, safety, short-term efficacy, and physiological responsiveness of a sensorless automated speed control system for a centrifugal LVAD was established. Long term studies are needed to show improved clinical outcomes.

Clinical Trial Registration

ClinicalTrials.gov, identifier: NCT04786236.