Heart wall tissue generates the mechanical force for blood flow, with overall cardiac function critically dependent on its mechanical properties. To improve insight into diseases leading to heart failure (HF), reliable and precise computational models are required. This work presents an overview of a 3D parametric computational model of the left ventricle and its potential use for predicting HF progression. The model is based on a finite element (FE) approach incorporating fluid-solid interaction via strong and loose coupling procedures. Active stresses are calculated using the Hunter excitation model, while passive stresses employ experiment-based material model. It allows for conventional 3D elements but also integrates a specific shell composite FE for cardiac mechanics. The model provides insight into hypertrophy development in realistic geometries, enabling the testing of medical hypotheses regarding its evolution in healthy and diseased hearts under various conditions. Hypertrophy impacts wall thickness, ventricular shape, displacement fields, and overall cardiac function. Using in silico simulations with virtual patients, we evaluate the influence of different drugs on cardiac output and ejection fraction. Drug effects are prescribed through boundary conditions for inlet/outlet flow, ECG signals, and calcium function governing muscle properties. The model can mimic conditions like hypertrophic and dilated cardiomyopathy, and various drug effects at the macroscopic level. When properly calibrated, the model can predict long-term ventricular expansion and remodeling. Its key capabilities for clinical translation include: predicting changes in ejection fraction (EF), providing clinicians with a predicted EF trajectory to identify patients at risk of worsening systolic function; Predicting diastolic dysfunction, offering a quantitative assessment to identify patients at risk of HF with preserved EF; and predicting ventricular remodeling, delivering visual and quantitative predictions to guide interventions against adverse remodeling. This model establishes a foundation for a computational decision-support system aimed at stratifying HF progression risk.