Advances in Modeling Techniques for Ventricular Assist Devices: A Comprehensive Review and Future Directions

RESUMO

Ventricular Assist Devices (VADs), particularly rotary Left Ventricular Assist Devices (LVADs), are essential for patients with advanced heart failure who are ineligible for transplantation. Despite advances in cardiovascular modeling and control, clinical translation of proposed LVAD control methods remains limited. This review examines the evolution of LVAD modeling and control and argues that the gap results not from insufficiently sophisticated algorithms, but from mismatches between modeling assumptions, sensing limitations, and real-world cardiovascular variability. A systematized review of more than 100 peer-reviewed studies covers mathematical cardiovascular models, lumped-parameter and reduced-order representations, classical and advanced control strategies, and emerging data-driven and machine learning approaches. The literature is synthesized using a problem-driven framework linking modeling and control choices to clinical challenges, including physiological observability, parameter identifiability, patient variability, and prevention of adverse events such as ventricular suction and thrombosis. The analysis shows that high-fidelity models and intelligent controllers can perform well in simulation, but their dependence on unmeasurable states, extensive parameter tuning, and dense sensing limits clinical implementation. Simpler control approaches often provide greater robustness under clinical constraints. Adaptive and data-driven techniques may help bridge this gap, but only if they account for implantable sensing limitations and interpretability requirements. By identifying structural barriers to adoption, this review synthesizes LVAD modeling and control paradigms and outlines research directions for physiologically adaptive, clinically implementable, and patient-safe LVAD systems.