Key result
A patient-individual computational simulation model parametrized by routine clinical datasets allowed for the estimation of hemodynamic parameters and pressure-volume loop changes at different VAD speeds.
Why the study?
Can routinely obtained hemodynamic and echocardiographic data parametrize an in-silico model to predict ventricular function and pressure-volume loops following LVAD implantation?
Can routinely obtained hemodynamic and echocardiographic data parametrize an in-silico model to predict ventricular function and pressure-volume loops following LVAD implantation?
Routinely obtained clinical hemodynamic and echocardiographic data can be used to parametrize patient-specific in-silico models to predict ventricular function and hemodynamics during LVAD support.
Routine-data PV-loop modeling may predict LVAD complications; leaves open clinical utility pending prospective validation.
BACKGROUND: Left ventricular assist device (LVAD) therapy has revolutionized the way end stage heart failure is treated today. Analysis of LVAD interaction with the whole cardiovascular system and its biological feedback loops is often conducted by means of computer models. Generating real time pressure volume loops (PV-loops) in patients, not using conductance catheters but routine diagnostics to feed an in-silico model could help to predict postoperative complications. METHODS: Routinely obtained hemodynamic measurements to evaluate myocardial function prior to LVAD implantation like pressure readings in the aorta, the left atrium and the left ventricle and simultaneous three-dimensional (3D) echocardiography recordings were assessed to parametrize a reduced computational model of the cardiovascular system. An automatic parameter identification procedure has been developed. RESULTS: The results constitute a patient-individual computational simulation model. An exemplary in-silico study focusing on the effect of different ventricular assist device (VAD) speeds has been conducted. Results allow for estimation of the resulting hemodynamic parameters and changes of the PV-loops. CONCLUSION: The model improves understanding and prediction of the interaction between pump and ventricles. Future modifications in exporting and merging routinely assessed real time hemodynamic patient data are necessary to investigate various clinical and pathological conditions of LVAD recipients.
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Moza et al. (2017) studied End stage heart failure. In-silico circulatory simulation model was evaluated on Generation of a patient-individual computational simulation model. A patient-individual computational simulation model parametrized by routine clinical datasets allowed for the estimation of hemodynamic parameters and pressure-volume loop changes at different VAD speeds.
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