Michel Haïssaguerre

Chapter 112 – Catheter Ablation of Atrial Fibrillation: Triggers and Substrate

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia in humans, occurring in 2% of the unselected adult population, increasing with each decade of life to a prevalence of 5.9% in the population older than 65 years. It results in an increased mortality rate and significant morbidity rate related to thromboembolic stroke, loss of atrial systole, and tachycardia-mediated atrial and ventricular cardiomyopathy. 1 Management strategies have been two tiered, either using palliative measures of rate control coupled with anticoagulation or the restoration and maintenance of sinus rhythm. Emerging evidence suggests that the pharmacologic maintenance of sinus rhythm has limited efficacy and value, at least in older patients and in those with persistent AF or structural heart disease. 2 3 4 Consequently, there is heightened interest in the nonpharmacologic, catheter-based approaches to maintain sinus rhythm that may prevent the significant adverse consequences associated with this arrhythmia. This chapter focuses on the current mapping techniques and the advantages and limitations of catheter ablation techniques targeting the triggers and substrate of AF.

Confidence-Based Training for Clinical Data Uncertainty in Image-Based Prediction of Cardiac Ablation Targets

Ventricular radio-frequency ablation (RFA) can have a critical impact on preventing sudden cardiac arrest but is challenging due to a highly complex arrhythmogenic substrate. This work aims to identify local image characteristics capable of predicting the presence of local abnormal ventricular activities (LAVA). This can allow, pre-operatively and non-invasively, to improve and accelerate the procedure. To achieve this, intensity and texture-based local image features are computed and random forests are used for classification. However using machine-learning approaches on such complex multimodal data can prove difficult due to the inherent errors in the training set. In this manuscript we present a detailed analysis of these error sources due in particular to catheter motion and the data fusion process. We derived a principled analysis of confidence impact on classification. Moreover, we demonstrate how formal integration of these uncertainties in the training process improves the algorithm’s performance, opening up possibilities for non-invasive image-based prediction of RFA targets.

Early Repolarization Syndrome: Epidemiology, Genetics, and Risk Stratification

Early repolarization (ER) involving the inferolateral leads, previously considered a benign electrocardiographic (ECG) phenomenon, has recently been associated with sudden cardiac death. Although the mechanisms underlying early repolarization are unknown, ER is the latest of the primary electrical cardiac disease discovered to have significantly high prevalence in SCD cases and contribute to increased risk of death from cardioarrhythmic cause. Population-based studies have demonstrated an association between early repolarization and sudden death, primarily when the ECG demonstrates ≥0.2 mV of ST elevation. Risk stratification, especially for asymptomatic patient, remains challenging. However, clinical features as personal history of syncope, familial history of sudden cardiac death and characteristics of the J-wave (>0.2 mV, wide distribution, horizontal/descending ST segment) should be considered at higher risk.