The clinical management of patients with repaired tetralogy of Fallot (rTOF) remains challenging. Around 10% of adult patients die prematurely within ~30 years after the initial surgery, and around 50% require at least one reintervention throughout their lives. Pulmonary valve replacment (PVR) is typically needed in adolescence to restore the function of the pulmonary valve and avoid severe biventricular complications, and the timing is critical for success. Standard clinical methods evaluate ventricular function with imaging biomarkers like level of RV dilatation, pulmonary valve regurgitation fraction, and ventricular ejection fraction. However, these biomarkers often fail, as 40% of patients do not respond positively to PVR. There is a critical need to better understand the factors driving ventricular remodeling and increase specificity of current diagnostic tools. Computational modeling can provide augmented mechanics- and structure-based assessment of ventricular function. The rationale for this project is to employ computational modeling to study the mechanisms of biventricular failure in patients with rTOF by combining biomechanical and statistical shape modeling techniques on a 200+ cohort. The central hypothesis is that variation in ventricular shape correlates with a variation in ventricular contractility and such a relationship has prognostic significance. During K99 phase, in AIM 1, Dr. Gusseva will examine the relationship between left ventricular (LV) shape and LV function in 200 patients with rTOF prior to PVR. Biomechanical models will provide LV contractility markers, and statistical shape models will quantify LV shape variation. Linear regression analysis will identify which LV shape modes best explain variation in LV contractility, incorporating gender, age, and clinical outcomes variables into the analysis. During R00 phase, in AIM 2A, Dr Gusseva will build a prospective clinical data cohort of 100 patients with rTOF undergoing PVR at their institution. Invasive pressure data and cardiac magnetic resonance imaging (MRI) will be acquired prior to and immediately post-PVR to build RV biomechanical models. These models will provide RV contractility pre- and post-PVR for 100 patients and used to analyze combined and independent effects of pulmonary regurgitation and stenosis on RV function (AIM 2B). Finally, in AIM 2C, Dr. Gusseva will examine multifactorial relationships between biventricular shape and function: 1) RV shape vs. RV function, 2) RV shape vs. LV function, 3) RV function vs. LV shape. The effect of gender, age, and clinical outcome will be evaluated. This work has the potential to augment our understanding of the factors driving biventricular heart failure in patients with rTOF. In addition to that, the proposed methodology can be applied to other congenital and acquired heart disease. Dr. Gusseva aims to become an independent investigator in the field of computational modeling and translational sciences. Her K99 career development plan includes training in biophysical model formulation, data science, machine learning, and clinical patient-oriented research skills, with guidance and hand-on training from multidisciplinary mentorship team of renowned experts.