No abstract

Age, history of AF, and SHD are the most powerful predictors of atrial fibrosis, as detected by MRI, in a general cardiology population. Atrial fibrosis predominates in the posterior LA wall.

OBJECTIVES This study sought to assess the relationship between fibrosis and re-entrant activity in persistent atrial fibrillation (AF). BACKGROUND The mechanisms involved in sustaining re-entrant activity during AF are poorly understood. METHODS Forty-one patients with persistent AF (age 56 ± 12 years; 6 women) were evaluated. High-resolution electrocardiographic imaging (ECGI) was performed during AF by using a 252-chest electrode array, and phase mapping was applied to locate re-entrant activity. Sites of high re-entrant activity were defined as re-entrant regions. Late gadolinium-enhanced (LGE) cardiac magnetic resonance (CMR) was performed at 1.25 × 1.25 × 2.5 mm resolution to characterize atrial fibrosis and measure atrial volumes. The relationship between LGE burden and the number of re-entrant regions was analyzed. Local LGE density was computed and characterized at re-entrant sites. All patients underwent catheter ablation targeting re-entrant regions, the procedural endpoint being AF termination. Clinical, CMR, and ECGI predictors of acute procedural success were then analyzed. RESULTS Left atrial (LA) LGE burden was 22.1 ± 5.9% of the wall, and LA volume was 74 ± 21 ml/m2. The number of re-entrant regions was 4.3 ± 1.7 per patient. LA LGE imaging was significantly associated with the number of re-entrant regions (R = 0.52, p = 0.001), LA volume (R = 0.62, p < 0.0001), and AF duration (R = 0.54, p = 0.0007). Regional analysis demonstrated a clustering of re-entrant activity at LGE borders. Areas with high re-entrant activity showed higher local LGE density as compared with the remaining atrial areas (p < 0.0001). Failure to achieve AF termination during ablation was associated with higher LA LGE burden (p < 0.001), higher number of re-entrant regions (p < 0.001), and longer AF duration (p = 0.008). CONCLUSIONS The number of re-entrant regions during AF relates to the extent of LGE on CMR, with the location of these regions clustering to LGE areas. These characteristics affect procedural outcomes of ablation.

In order to translate the important progress in cardiac electrophysiology modelling of the last decades into clinical applications, there is a requirement to make macroscopic models that can be used for the planning and performance of the clinical procedures. This requires model personalization, i.e. estimation of patient-specific model parameters and computations compatible with clinical constraints. Simplified macroscopic models can allow a rapid estimation of the tissue conductivity, but are often unreliable to predict arrhythmias. Conversely, complex biophysical models are more complete and have mechanisms of arrhythmogenesis and arrhythmia sustainibility, but are computationally expensive and their predictions at the organ scale still have to be validated. We present a coupled personalization framework that combines the power of the two kinds of models while keeping the computational complexity tractable. A simple eikonal model is used to estimate the conductivity parameters, which are then used to set the parameters of a biophysical model, the Mitchell -Schaeffer (MS) model. Additional parameters related to action potential duration restitution curves for the tissue are further estimated for the MS model. This framework is applied to a clinical dataset derived from a hybrid X-ray/magnetic resonance imaging and non-contact mapping procedure on a patient with heart failure. This personalized MS model is then used to perform an in silico simulation of a ventricular tachycardia (VT) stimulation protocol to predict the induction of VT. This proof of concept opens up possibilities of using VT induction modelling in order to both assess the risk of VT for a given patient and also to plan a potential subsequent radio-frequency ablation strategy to treat VT.

. Among a total of 13 060 electrograms reviewed in the whole study population, 538 LAVA were detected and analyzed. LAVA were located within the WT <5 mm (469/538 [87%]) or at its border (100% within 23 mm). Very late LAVA (>100 ms after QRS complex) were almost exclusively detected within the thinnest area (93% in the WT<3 mm). Conclusions-Regional