Impact of Effective Refractory Period Personalization on Arrhythmia Vulnerability in Patient-Specific Atrial Computer Models

Background and AimsThe effective refractory period is one of the main electrophysiological properties governing arrhythmia maintenance, yet effective refractory period personalisation is rarely performed when creating patient-specific computer models of the atria to inform clinical decision-making. The aim of this study is to evaluate the impact of incorporating clinical effective refractory period measurements when creating in silico personalised models on arrhythmia vulnerability. MethodsClinical effective refractory period measurements were obtained in seven patients from multiple locations in the atria. The atrial geometries from the electroanatomical mapping system were used to generate personalised anatomical atrial models. To reproduce patient-specific refractory period measurements, the Courtemanche cellular model was gradually reparameterised from control conditions to a setup representing atrial fibrillation-induced remodelling. Four different modelling approaches were compared: homogeneous (A), heterogeneous (B), regional (C), and continuous (D) distribution of effective refractory period. The first two configurations were non-personalised based on literature data, the latter two were personalised based on patient measurements. We evaluated the effect of each modelling approach by quantifying arrhythmia vulnerability and tachycardia cycle length. We performed a sensitivity analysis to assess the influence of effective refractory period measurement uncertainty on arrhythmia vulnerability. ResultsThe mean vulnerability was 3.4{+/-}4.0%, 7.7{+/-}3.4%, 9.0{+/-}5.1%, 7.0{+/-}3.6% for scenarios A to D, respectively. The mean tachycardia cycle length was 167.1{+/-}12.6ms, 158.4{+/-}27.5ms, 265.2{+/-}39.9ms, and 285.9{+/-}77.3ms for scenarios A to D, respectively. Incorporating perturbations to the measured effective refractory period in the range of 2, 5, 10 and 20ms, had an impact on the vulnerability of the model of 5.8{+/-}2.7%, 6.1{+/-}3.5%, 6.9{+/-}3.7%, 5.2{+/-}3.5%, respectively. ConclusionIncreased dispersion of the effective refractory period had a greater effect on reentry dynamics than on mean vulnerability values. The incorporation of personalised effective refractory period in the form of gradients had a greater impact on vulnerability than had a homogeneously reduced effective refractory period. Effective refractory period measurement uncertainty up to 20ms slightly influences arrhythmia vulnerability. Electrophysiological personalisation of atrial in silico models appears essential and warrants confirmation in larger cohorts.