Providing therapies tailored to each patient is the vision of precision medicine, enabled by the increasing ability to capture extensive data about individual patients. In this position paper, we argue that the second enabling pillar towards this vision is the increasing power of computers and algorithms to learn, reason, and build the ‘digital twin’ of a patient. Computational models are boosting the capacity to draw diagnosis and prognosis, and future treatments will be tailored not only to current health status and data, but also to an accurate projection of the pathways to restore health by model predictions. The early steps of the digital twin in the area of cardiovascular medicine are reviewed in this article, together with a discussion of the challenges and opportunities ahead. We emphasize the synergies between mechanistic and statistical models in accelerating cardiovascular research and enabling the vision of precision medicine.

This study compared pressure fields by 4-dimensional (4D), velocity-encoded cine (VEC) cardiac magnetic resonance imaging (CMR) with pressures measured by the clinical gold standard catheterization. Thirteen patients (n = 7 male, n = 6 female) with coarctation were studied. The 4D-VEC-CMR pressure fields were computed by solving the Pressure-Poisson equation. The agreement between catheterization and CMR-based methods was determined at 5 different measurement sites along the aorta. For all sites, the correlation coefficients between measures varied between 0.86 and 0.97 (p < 0.001). The Bland-Altman test showed good agreement between peak systolic pressure gradients across the coarctation. The nonsignificant (p > 0.2) bias was +2.3 mm Hg (± 6.4 mm Hg, 2 SDs) for calibration with dynamic pressures and +1.5 mm Hg (± 4.6 mm Hg, 2 SDs) for calibration with static pressure. In a clinical setting of coarctation, pressure fields can be accurately computed from 4D-VEC-CMR-derived flows. In patients with coarctation, this noninvasive technique might evolve to an alternative to invasive catheterization.

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AimsModels of blood flow in the left ventricle (LV) and aorta are an important tool for analysing the interplay between LV deformation and flow patterns. Typically, image-based kinematic models describing endocardial motion are used as an input to blood flow simulations. While such models are suitable for analysing the hemodynamic status quo, they are limited in predicting the response to interventions that alter afterload conditions. Mechano-fluidic models using biophysically detailed electromechanical (EM) models have the potential to overcome this limitation, but are more costly to build and compute. We report our recent advancements in developing an automated workflow for the creation of such CFD ready kinematic models to serve as drivers of blood flow simulations.Methods and resultsEM models of the LV and aortic root were created for four pediatric patients treated for either aortic coarctation or aortic valve disease. Using MRI, ECG and invasive pressure recordings, anatomy as well as electrophysiological, mechanical and circulatory model components were personalized.ResultsThe implemented modeling pipeline was highly automated and allowed model construction and execution of simulations of a patient’s heartbeat within 1 day. All models reproduced clinical data with acceptable accuracy.ConclusionUsing the developed modeling workflow, the use of EM LV models as driver of fluid flow simulations is becoming feasible. While EM models are costly to construct, they constitute an important and nontrivial step towards fully coupled electro-mechano-fluidic (EMF) models and show promise as a tool for predicting the response to interventions which affect afterload conditions.

Intracardiac blood flow as characterized by measurements of KE is altered in patients with mitral regurgitation. Physiological flow conditions appear to not fully be restored with mitral valve surgery.