Patients with congenitally corrected transposition of the great arteries (ccTGA) can be treated with a double switch operation (DSO) to restore the normal anatomical connection of the left ventricle (LV) to the systemic circulation and the right ventricle (RV) to the pulmonary circulation. The subpulmonary LV progressively deconditions over time due to its connection to the low pressure pulmonary circulation and needs to be retrained using a surgical pulmonary artery band (PAB) for 6-12 months prior to the DSO. The subsequent clinical follow-up, consisting of invasive cardiac pressure and non-invasive imaging data, evaluates LV preparedness for the DSO. Evaluation using standard clinical techniques has led to unacceptable LV failure rates of ∼15 % after DSO. We propose a computational modeling framework to (1) reconstruct LV and RV pressure-volume (PV) loops from non-simultaneously acquired imaging and pressure data and gather model-derived mechanical indicators of ventricular function; and (2) perform in silico DSO to predict the functional response of the LV when connected to the high-pressure systemic circulation. Clinical datasets of six patients with ccTGA after PAB, consisting of cardiac magnetic resonance imaging (MRI) and right and left heart catheterization, were used to build patient-specific models of LV and RV - MbaselineLV and MbaselineRV. For in silico DSO the models of MbaselineLV and MbaselineRV were used while imposing the afterload of systemic and pulmonary circulations, respectively. Model-derived contractility and Pressure-Volume Area (PVA) - i.e., the sum of stroke work and potential energy - were computed for both ventricles at baseline and after in silico DSO. In silico DSO suggests that three patients would require a substantial augmentation of LV contractility between 54 % and 80 % and an increase in PVA between 38 % and 79 % with respect to the baseline values to accommodate the increased afterload of the systemic circulation. On the contrary, the baseline functional state of the remaining three patients is predicted to be adequate to sustain cardiac output after the DSO. This work demonstrates the vast variation of LV function among patients with ccTGA and emphasizes the importance of a biventricular approach to assess patients' readiness for DSO. Model-derived predictions have the potential to provide additional insights into planning of complex surgical interventions.
Keywords: Biomechanical modeling; Cardiac magnetic resonance imaging; Complex congenital heart disease; Pressure-volume loops.
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