Abstract

Single ventricle heart defects involve pathologies in which the heart has only one functional pumping chamber. In these conditions, treatment consists of three staged procedures. At stage 1 pulmonary flow is provided through an artificial shunt from the systemic circulation. Representative hemodynamics models able to explore different virtual surgical options can be built based on pre-operative imaging and patient data. In this context, the specification of boundary conditions is necessary to compute pressure and flow in the entire domain. However, these boundary conditions are rarely the measured variables. Moreover, to take into account the rest of the circulation outside of the three-dimensional modeled domain, a number of reduced order models exist. A simplified method is presented to iteratively, but automatically, tune reduced model parameters from hemodynamic data clinically measured before stage 2 surgery. Patient-specific local hemodynamics around the distal systemic-to-pulmonary shunt anastomosis and the connected pulmonary arteries are also analyzed. Multi-scale models of pre-stage 2 single ventricle patients are developed, including a 3D model of shunt-pulmonary connection and a number of pulmonary arteries. For each pulmonary outlet a total downstream resistance is identified, consistent with measured flow split and pressures. Target pressures such as minimum, maximum or average over one or both lungs are considered, depending on the clinical measurement. When possible, both steady and pulsatile identifications are performed. The methodology is demonstrated with six patient-specific models: the clinical target data are well-matched, except for one case where clinical data were subsequently found inconsistent. Inhomogeneous pressure, swirling blood flow patterns and very high wall shear stress 3D maps highlight similarities and differences among patients. Steady and pulsatile tuning results are similar. This work demonstrates (1) how to use routine clinical data to define boundary conditions for patient-specific 3D models in pre-stage 2 single ventricle circulations and (2) how simulations can help to check the coherence of clinical data, or provide insights to clinicians that are otherwise difficult to measure, such as in the presence of kinks. Finally, the choice of steady vs. pulsatile tuning, limitations and possible extensions of this work are discussed.

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