Potential of Medical Management to Mitigate Suction Events in Ventricular Assist Device Patients

Rocchi, Maria; Fresiello, Libera; Jacobs, Steven; Dauwe, Dieter; Droogné, Walter; Meyns, Bart

doi:10.1097/mat.0000000000001573

Cited by 3 publications

(6 citation statements)

References 29 publications

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“…The aim of this study was to validate the suction module connected to a hybrid cardiovascular simulator present at KU Leuven. ( Rocchi et al, 2021a ; Rocchi et al, 2021b ). For this purpose, suction beats and non-suction beats were simulated with a HVAD for different pathophysiological conditions.…”

Section: Discussionmentioning

confidence: 99%

“…According to our results, left ventricular systolic and diastolic functions do play a great role in determining the phenomenology of suction, but we do not exclude the influence that other factors such as right heart failure, tachycardia, or hypotension might have on the LVAD signals. ( Rocchi et al, 2021b ). This could be a reason why our simulated cohort, focused on ventricular parameters only, represents just a subgroup of the LVAD signals variability observed in the clinic.…”

Section: Discussionmentioning

confidence: 99%

“…The work was conducted using the suction module ( Rocchi et al, 2021a ; Rocchi et al, 2021b ) connected to a previously validated high fidelity cardiovascular hybrid simulator. ( Fresiello et al, 2014 ; Fresiello et al, 2022 ).…”

Section: Methods and Methodsmentioning

confidence: 99%

“…This is the hemodynamic condition for suction to be triggered in case of ideal positioning of the inflow cannula as previously investigated in vivo by Salamonsen et al ( Salamonsen et al, 2015 ) Additional technical specifications of the suction module and the hybrid simulator were described previously. ( Fresiello et al, 2014 ; Rocchi et al, 2021a ; Rocchi et al, 2021b ).…”

Section: Methods and Methodsmentioning

confidence: 99%

“…Indeed, the suction module is implemented in a high fidelity simulator of the entire closed loop cardiovascular system. ( Rocchi et al, 2021a ; Rocchi et al, 2021b ). The cardiovascular simulator can represent different (patho-)physiologies by tuning cardiac parameters (e.g., heart rate, left/right ventricular systolic and diastolic status), vascular parameters (e.g., circulating blood volume, systemic vascular resistance) and LVAD speed level.…”

Section: Introductionmentioning

confidence: 99%

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

Rocchi¹,

Gross²,

Moscato³

et al. 2023

Front. Physiol.

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methods and Methodsmentioning

confidence: 99%

Section: Methods and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Rocchi¹,

Gross²,

Moscato³

et al. 2023

Front. Physiol.

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Activation of a Soft Robotic Left Ventricular Phantom Embedded in a Closed-Loop Cardiovascular Simulator: A Computational and Experimental Analysis

Demeersseman,

Rocchi,

Fehervary

et al. 2024

Cardiovasc Eng Tech

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Physiological control for left ventricular assist devices based on deep reinforcement learning

Fernández‐Zapico,

Peirelinck,

Deconinck

et al. 2024

Artificial Organs

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BackgroundThe improvement of controllers of left ventricular assist device (LVAD) technology supporting heart failure (HF) patients has enormous impact, given the high prevalence and mortality of HF in the population. The use of reinforcement learning for control applications in LVAD remains minimally explored. This work introduces a preload‐based deep reinforcement learning control for LVAD based on the proximal policy optimization algorithm.MethodsThe deep reinforcement learning control is built upon data derived from a deterministic high‐fidelity cardiorespiratory simulator exposed to variations of total blood volume, heart rate, systemic vascular resistance, pulmonary vascular resistance, right ventricular end‐systolic elastance, and left ventricular end‐systolic elastance, to replicate realistic inter‐ and intra‐patient variability of patients with a severe HF supported by LVAD. The deep reinforcement learning control obtained in this work is trained to avoid ventricular suction and allow aortic valve opening by using left ventricular pressure signals: end‐diastolic pressure, maximum pressure in the left ventricle (LV), and maximum pressure in the aorta.ResultsThe results show controller obtained in this work, compared to the constant speed LVAD alternative, assures a more stable end‐diastolic volume (EDV), with a standard deviation of 5 mL and 9 mL, respectively, and a higher degree of aortic flow, with an average flow of 1.1 L/min and 0.9 L/min, respectively.ConclusionThis work implements a deep reinforcement learning controller in a high‐fidelity cardiorespiratory simulator, resulting in increases of flow through the aortic valve and increases of EDV stability, when compared to a constant speed LVAD strategy.

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Potential of Medical Management to Mitigate Suction Events in Ventricular Assist Device Patients

Cited by 3 publications

References 29 publications

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

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

Activation of a Soft Robotic Left Ventricular Phantom Embedded in a Closed-Loop Cardiovascular Simulator: A Computational and Experimental Analysis

Physiological control for left ventricular assist devices based on deep reinforcement learning

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