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Physiologic Data-Driven Iterative Learning Control for Left Ventricular Assist Devices

ABSTRACT: Continuous flow ventricular assist devices (cfVADs) constitute a viable and increasingly used therapy for end-stage heart failure patients. However, they are still operating at a fixed-speed mode that precludes physiological cfVAD response and it is often related to adverse events of cfVAD therapy. To ameliorate this, various physiological controllers have been proposed, however, the majority of these controllers do not account for the lack of pulsatility in the cfVAD operation, which is supposed to be beneficial for the physiological function of the cardiovascular system. In this study, we present a physiological data-driven iterative learning controller (PDD-ILC) that accurately tracks predefined pump flow trajectories, aiming to achieve physiological, pulsatile, and treatment-driven response of cfVADs. The controller has been extensively tested in an in-silico environment under various physiological conditions, and compared with a physiologic pump flow proportional-integral-derivative controller (PF-PIDC) developed in this study as well as the constant speed (CS) control that is the current state of the art in clinical practice. Additionally, two treatment objectives were investigated to achieve pulsatility maximization and left ventricular stroke work (LVSW) minimization by implementing copulsation and counterpulsation pump modes, respectively. Under all experimental conditions, the PDD-ILC as well as the PF-PIDC demonstrated highly accurate tracking of the reference pump flow trajectories, outperforming existing model-based iterative learning control approaches. Additionally, the developed controllers achieved the predefined treatment objectives and resulted in improved hemodynamics and preload sensitivities compared to the CS support.

SUBMITTER: Magkoutas K

PROVIDER: S-EPMC9326058 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe assessment of hemodynamics in patients supported with left ventricular assist devices (LVADs) is often challenging. Physical examination maneuvers correlate poorly with true hemodynamics. We assessed the value of novel transthoracic echocardiography (TTE)-derived variables to reliably predict hemodynamics in patients supported with LVAD.Methods and resultsA total of 102 Doppler-TTE images of the LVAD outflow cannula were obtained during simultaneous invasive right heart catheterization (RHC) in 30 patients supported with continuous-flow LVADs (22 HMII, 8 HVAD) either during routine RHC or during invasive ramp testing. Properties of the Doppler signal though the outflow cannula were measured at each ramp stage (RS), including the systolic slope (SS), diastolic slope (DS), and velocity time integral (VTI). Hemodynamic variables were concurrently recorded, including Doppler opening pressure (MAP), heart rate (HR), right atrial pressure, pulmonary artery pressure, pulmonary capillary wedge pressure (PCWP), Fick cardiac output (CO) and systemic vascular resistance (SVR). Univariate and multivariate regression analyses were used to explore the dependence of PCWP, CO, and SVR on DS, SS, VTI, MAP, HR, and RS. Multivariate linear regression analysis revealed significant contributions of DS on PCWP (PCWPpred = 0.164DS + 4.959; R = 0.68). Receiver operating characteristic (ROC) curve analysis revealed that PCWPpred could predict an elevated PCWP ≥18 mm Hg with a sensitivity (Sn) of 94% and specificity (Sp) of 85% (area under the ROC curve 0.88). CO could be predicted by RS, VTI, and HR (COpred = 0.017VTI + 0.016HR + 0.12RS + 2.042; R = 0.61). COpred could predict CO ≤4.5 L/min with Sn 73% and Sp 79% (AUC 0.81). SVR could be predicted by MAP, VTI, and HR (SVRpred = 15.44MAP - 5.453VTI - 6.349HR + 856.15; R = 0.84) with Sn 84% and Sp 79% (AUC 0.91) to predict SVR ≥1200 dyn-s/cm5.ConclusionsDoppler-TTE variables derived from the LVAD outflow cannula can reliably predict PCWP, CO, and SVR in patients supported with LVADs and may mitigate the need for invasive testing.

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Physiologic Data-Driven Iterative Learning Control for Left Ventricular Assist Devices

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