For heart failure patients, left ventricular assist devices (LVADs) keep blood flowing when the main pumping chamber fails. Yet supporting the left side can overload the right, triggering lower survival rates or longer ICU stays.
Lik Chuan Lee, professor in the Department of Mechanical Engineering at Michigan State, uses detailed computer models to study how LVAD settings change the way both sides of the heart work together. Together with collaborators at Corewell Health and Assistant Professor Lei Fan at MSU, his Computational Biomechanics Lab built a virtual heart that incorporates a clinically used HeartMate 3 LVAD, then calibrated it based on a real heart failure patient and validated it against animal experiments. The model lets them “turn the dial” on pump speed and heart muscle properties and watch how the right ventricle responds.
Simulations reveal a ceiling on pump speed: higher speed weakens right-ventricle function, especially in hearts with thinner interventricular walls or weaker musculature.
By showing how structure, muscle strength, and LVAD speed interact, the model helps clinicians spot high-risk patients and plan their care. In time, similar models could be tailored to guide safer pump speeds, drug choices, and plans for extra support or closer monitoring before surgery.
Reducing right-heart failure would cut complications and hospital time, helping LVAD patients live longer, more stable lives while they wait for a transplant or rely on LVADs long term.
Discover more about Lee’s research:
- An in silico study of the effects of left ventricular assist device on right ventricular function and inter-ventricular interaction [Article]
- Comparison of Left Ventricular Function Derived from Subject-Specific Inverse Finite Element Modeling Based on 3D ECHO and Magnetic Resonance Images [Article]
- Factors Causing Interventricular Interactions In The Heart Implanted With Left Ventricular Assist Device [Article]
- In-silico assessment of the effects of right ventricular assist device on pulmonary arterial hypertension using an image based biventricular modeling framework [Article]
- Research website [Website]
- Google Scholar page [Website]
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