Table 1.

Summary of notable results and challenges for computational modeling of AF.

Notable results

A wide range of action potential models has been published, each reproducing numerous experimental observations. Populations of models generated by varying model parameters can account for phenotypic diversity between patients and help to analyze differences between baseline models. Combined experimental and computational studies can inform on the therapeutic efficacy against AF targets, including state-dependent drug-channel interactions and drug combinations, in specific subgroups of patients. Whole-atria models have provided insight into the determinants of reentrant activity and drivers for AF. Patient-specific anatomy and fibrosis patterns have been used to retrospectively identify optimal patient-specific ablation strategies, with initial prospective applications being explored. Patient-level models can simulate the entire lifetime of a virtual cohort and inform on AF progression and cost-effectiveness of AF therapies.

Notable challenges

Each action potential model has different advantages and disadvantages, with numerous results being model specific. The etiology of AF is diverse, but currently available cardiomyocyte models only have limited options for tailoring models to specific clinical conditions. Only a handful of labs worldwide have the available expertise, computing power and required collaboration between clinicians, scientists and engineers to apply mechanistic whole-atria models in the clinical setting. The extent of personalization of whole-atria models, particularly with regard to electrophysiological properties, remains very limited. Current patient-level models do not incorporate fundamental mechanistic patterns of AF pathophysiology. Integration of mechanistic modeling with “big data” approaches might help to improve AF diagnosis and management.