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Objective. Significant aortic regurgitation is a common complication following left ventricular assist device (LVAD) intervention, and existing studies have not attempted to monitor regurgitation signals and undertake preventive measures during full support. Regurgitation is an adverse event that can lead to inadequate left ventricular unloading, insufficient peripheral perfusion, and repeated episodes of heart failure. Moreover, regurgitation occurring during full support due to pump position offset cannot be directly controlled through control algorithms. Therefore, accurate estimation of regurgitation during percutaneous left ventricular assist device (PLVAD) full support is critical for clinical management and patient safety. Approach. An estimation system based on the regurgitation model is built in this paper, and the unscented Kalman filter estimator (UKF) is introduced as an estimation approach. Three offset degrees and three heart failure states are considered in the investigation. Using the mock circulatory loop experimental platform, compare the regurgitation estimated by the UKF algorithm with the actual measured regurgitation; the errors are analyzed using standard confidence intervals of ±2 SDs, and the effectiveness of the mentioned algorithms is thus assessed. The generalization ability of the proposed algorithm is verified by setting different heart failure conditions and different rotational speeds. The root mean square error and correlation coefficient between the estimated and actual values are quantified and the statistical significance of accuracy differences in estimation is illustrated using one-way analysis of variance (One-Way ANOVA), which in turn assessed the accuracy and stability of the UKF algorithm. Main results. The research findings demonstrate that the regurgitation estimation system based on the regurgitation model and UKF can relatively accurately estimate the regurgitation status of patients during PLVAD full support, but the effect of myocardial contractility on the estimation accuracy still needs to be taken into account. Significance. The proposed estimation method in this study provides essential reference information for clinical practitioners, enabling them to promptly manage potential complications arising from regurgitation. By sensitively detecting LVAD adverse events, valuable insights into the performance and reliability of the LVAD device can be obtained, offering crucial feedback and data support for device improvement and optimization., (© 2024 Institute of Physics and Engineering in Medicine.)

Objective. A percutaneous left ventricular assist device (PLVAD) can be used as a bridge to heart transplantation or as a temporary support for end-stage heart failure. Transvalvularly placed PLVADs may result in aortic regurgitation due to unstable pump position during fully supported operation, which may diminish the pumping effect of forward flow and predispose to complications. Therefore, accurate characterization of aortic regurgitation is essential for proper modeling of heart-pump interactions and validation of control strategies. Approach. In the present study, an improved aortic valve model was used to analyze the severity of regurgitation produced by different pump position offsets. The link between pump position offset degree and regurgitation is validated in the fixed speed mode, and the influence of pump speed on regurgitation is verified in the variable speed mode, using the mock circulatory loop (MCL) experimental platform. Main results. The greater the pump offset and the more severe the regurgitation, the more carefully the pump speed needs to be managed. To avoid over-pumping, the recommended pump speed in this study should not exceed 30 000 rpm. Significance. The modeling approach provide in this study not only makes it easier to comprehend the impact of regurgitation events on the entire interactive system during mechanical assistance, but it also aids in providing timely alerts and suitable management measures., (© 2023 Institute of Physics and Engineering in Medicine.)