RESUMEN
BACKGROUND AND AIMS: The effective refractory period (ERP) is one of the main electrophysiological properties governing arrhythmia, yet ERP personalisation is rarely performed when creating patient-specific computer models of the atria to inform clinical decision-making. This study evaluates the impact of integrating clinical ERP measurements into personalised in silico models on arrhythmia vulnerability. METHODS: Clinical ERP measurements were obtained in seven patients from multiple locations in the atria. Atrial geometries from the electroanatomical mapping system were used to generate personalised anatomical atrial models. The Courtemanche cellular model was adjusted to reproduce patientspecific ERP. Four modelling approaches were compared: homogeneous (A), heterogeneous (B), regional (C), and continuous (D) ERP distributions. Non-personalised approaches (A, B) were based on literature data, while personalised approaches (C, D) were based on patient measurements. Modelling effects were assessed on arrhythmia vulnerability and tachycardia cycle length, with sensitivity analysis on ERP measurement uncertainty. RESULTS: 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. Mean tachycardia cycle length was 167.1±12.6 ms, 158.4±27.5 ms, 265.2±39.9 ms, and 285.9±77.3 ms for scenarios A to D, respectively. Incorporating perturbations to the measured ERP in the range of 2, 5, 10, 20, and 50ms changed the vulnerability of the model to 5.8±2.7%, 6.1±3.5%, 6.9±3.7%, 5.2±3.5%, 9.7±10.0% respectively. CONCLUSION: Increased ERP dispersion had a greater effect on reentry dynamics than on vulnerability. Inducibility was higher in personalised scenarios compared to scenarios with uniformly reduced ERP; however, this effect was reversed when incorporating fibrosis informed by low voltage areas.ERP measurement uncertainty up to 20 ms slightly influenced vulnerability. Electrophysiological personalisation of atrial in silico models appears essential and requires confirmation in larger cohorts.