The International Journal of Artificial Organs

Ventricular assist devices (VADs) are essential for end-stage heart failure patients, but their design must balance hydraulic efficiency and hemocompatibility to minimize blood damage. This study presents a multi-objective optimization framework integrating computational fluid dynamics (CFD), Random Forest Regression (RFR), and Bayesian optimization to improve VAD rotor hemocompatibility. Seven key design parameters (inlet/outlet blade angles, blade count, rotational speed, clearance gap, blade thickness, and rotor length) were optimized using a D-optimal design of experiments. The RFR surrogate model demonstrated superior performance in handling the complex parameter interactions, achieving high predictive accuracy (R² > 0.84 for all hemocompatibility metrics). CFD simulations employing a Carreau-Yasuda blood model and rigorous mesh independence analysis evaluated shear stress distributions, exposure times, hemolysis index (HI), and platelet activation state (PAS). The optimized design achieved 97.24% of blood flow with shear stress <50 Pa, a HI of 0.01%, and PAS of 1 × 10⁻⁶%, representing significant improvements over baseline configurations. While this computational study provides comprehensive parametric insights, future experimental validation is recommended to confirm these findings under physiological conditions. The proposed framework offers a systematic approach for developing high-performance VADs with enhanced hemocompatibility.

When the cardiac function is minimal, LVAD patients' intraventricular blood flow occurs entirely through the LVAD, the aortic valve is continuously closed, this condition significantly alters intraventricular haemodynamics and increases the risk of intraventricular thrombosis. The aim of this study was to analyse intraventricular blood flow patterns in continuous aortic closure, during axial flow pump support. In vitro flow visualization experiments inside a 3D-printed silicone LV model were conducted to emulate the intraventricular hemodynamic parameters inside the LV during LAP31 axial blood pump support, including the distribution of velocity, vorticity and the standard deviation of velocity. The results showed that the intraventricular blood flows directly from the mitral valve to the blood pump inlet at the three instants of the cardiac cycle when the aortic valve is continuously closed, and the standard deviation of the blood flow velocity in the examined cross-section was uniform. Meanwhile, the strong vorticity was observed in the ventricular wall near the blood pump inlet and below the aorta throughout the cardiac cycle, and the location of the strong vorticity region does not change over time. This phenomenon causes intraventricular blood stagnation in the left ventricular outflow tract and the ventricular wall near the blood pump inlet, which increases the risk of thrombus formation in the heart.

Electrocardiogram (ECG) signal classification plays a critical role in diagnosing various cardiac conditions by identifying irregularities in heart rhythms. Despite advancements in the field, existing methodologies often rely on basic techniques that inadequately filter noise, leading to degraded performance and misinterpretation of vital features. This study presents the Spectral-Optimized Cardiac Framework (SOCF) approach to enhance the accuracy of ECG classification through advanced noise filtering, comprehensive feature extraction, efficient feature selection and integration of hybrid modelling techniques. The proposed methodology introduces the ChebWave Mean Refinement Filter (CWMRF) for effective noise reduction and to enhance signal clarity while preserving essential characteristics. In feature extraction, the Spectral Essence Extractor (SEE) captures both basic and high order features, providing deeper insights into ECG signals. Additionally, the Deep Blue Particle Optimizer (DBPO) efficiently identify relevant features while mitigating the risk of overfitting. Furthermore, the hybrid architecture of Convolutional neural network (CNN) and long short-term memory (LSTM) enable the model to effectively capture both spatial and temporal dependencies, thereby improving classification accuracy. To optimize performance, the Aquila Optimizer enhances the convergence speed and model efficiency by employing diverse search strategies inspired by the hunting behavior of Aquila bird. By integrating these advanced techniques, the SOCF achieved impressive results on the MIT-BIH dataset and PTB dataset with an accuracy of 99.6% and 99.68%, precision of 99.4% and 99.44%, recall of 99.5% and 99.51%, and F1 score of 99.2% and 99.49%, which significantly improves the robustness and reliability of ECG signal classification, ultimately providing more accurate clinical insights and better patient outcomes.

Severe rhabdomyolysis, characterized by extensive muscle breakdown and release of myoglobin and creatine kinase (CK), is a life-threatening condition often complicated by acute kidney injury (AKI) and multi-organ failure (MOF). Even when conventional treatments such as fluid resuscitation and renal replacement therapy (RRT) are timely applied, severe cases remain challenging to manage. Among therapies available in this setting, hemadsorption with CytoSorb has the potential not only to treat rhabdomyolysis through removal of circulating molecules but also to limit or even prevent rhabdomyolysis-related renal failure and MOF. In this case series we present a preliminary experience with a novel use CytoSorb hemoadsorption therapy, which encompassed the use of two CytoSorb cartridges running in parallel, to enhance myoglobin and cytokine clearance. Clinical data from the three patients with severe rhabdomyolysis treated with dual concomitant CytoSorb treatments highlighted marked improvements in CK, renal, hepatic, and inflammatory markers, with possible remarkable impact in containing rhabdomyolysis-related organ failure and death. Further investigation is warranted to establish the opportunity of tailored protocols for critically ill patients.