<p>Digital twins offer a promising approach to advancing healthcare by providing precise, noninvasive monitoring and early detection of diseases. In heart failure (HF), a leading cause of mortality worldwide, they can improve patient monitoring and clinical outcomes by simulating hemodynamic changes indicative of worsening HF. Current techniques are limited by their invasiveness and lack of scalability. We present a novel framework for HF digital twins that predicts patient-specific hemodynamic metrics in the pulmonary arteries using 3D computational fluid dynamics to address these limitations. We introduce a strategy to determine the minimal geometric complexity required for accurate pressure prediction and explore the effects of varying boundary conditions. By validating our digital twins against invasively-measured data, we demonstrate their potential to improve HF management by enabling continuous, noninvasive monitoring and early identification of worsening HF. This proof-of-concept study lays the groundwork for integrating digital twin technology into personalized HF care.</p>