Abstract:

Aims Atrial flutter (AFlut) is a common re-entrant atrial tachycardia driven by self-sustainable mechanisms that cause excitations to propagate along pathways different from sinus rhythm. Intra-cardiac electrophysiological mapping and catheter ablation are often performed without detailed prior knowledge of the mechanism perpetuating AFlut, likely prolonging the procedure time of these invasive interventions. We sought to discriminate the AFlut location [cavotricuspid isthmus-dependent (CTI), peri-mitral, and other left atrium (LA) AFlut classes] with a machine learning-based algorithm using only the non-invasive signals from the 12-lead electrocardiogram (ECG). Methods and results Hybrid 12-lead ECG dataset of 1769 signals was used (1424 in silico ECGs, and 345 clinical ECGs from 115 patients—three different ECG segments over time were extracted from each patient corresponding to single AFlut cycles). Seventy-seven features were extracted. A decision tree classifier with a hold-out classification approach was trained, validated, and tested on the dataset randomly split after selecting the most informative features. The clinical test set comprised 38 patients (114 clinical ECGs). The classifier yielded 76.3% accuracy on the clinical test set with a sensitivity of 89.7%, 75.0%, and 64.1% and a positive predictive value of 71.4%, 75.0%, and 86.2% for CTI, peri-mitral, and other LA class, respectively. Considering majority vote of the three segments taken from each patient, the CTI class was correctly classified at 92%. Conclusion Our results show that a machine learning classifier relying only on non-invasive signals can potentially identify the location of AFlut mechanisms. This method could aid in planning and tailoring patient-specific AFlut treatments.

Abstract:

Atrial fibrillation (AF) is one of the leading health challenges posing a significant burden not only to patients but also to the health care systems. While pulmonary vein isolation (PVI) is an effective therapy for paroxysmal AF patients, the success rate drops for patients with persistent AF. This is thought to be due to patients exhibiting atrial cardiomyopathy (ACM), specifically structural remodelling in the atria occurring during the progression of AF. Therefore, persistent AF patients exhibit additional pathological substrate in the atria, which maintains the arrhythmia. Unfortunately, the current approaches performing PVI plus additionally targeting the pathological substrate are still sub-optimal, with only 50-70\% of patients having long-term freedom from AF after catheter ablation. Hence, the optimal ablation strategy remains an open question demanding further research to identify promising ablation targets. Two approaches that have gained attention over the recent years are electro-anatomical mapping specifically targeting low voltage areas and areas showing contrast in late gadolinium-enhanced magnetic resonance imaging (LGE-MRI). However, both are hindered by the lack of consensus regarding a precise method to identify the pathological substrate. Identification via low voltage mapping is limited due to a lack of understanding of the impact of catheter characteristics that influence the voltage aside from the pathological substrate. Additionally, voltage mapping can be performed during sinus rhythm (SR) or AF. Mapping in the latter case is beneficial as it reduces the need for potentially multiple cardioversions. However, there is no precise statistical evaluation for the cut-off values applied to determine low voltage areas. The advantage of using LGE-MRI instead is that it is a less invasive diagnostic method. However, the spatial resolution of LGE-MRI is limited. Moreover, the degree of accordance between MRI and voltage mapping to detect fibrosis remains disputed. The overall goal of this thesis is to compare mapping modalities to address the fore-mentioned limitations. Therefore, providing more robust and accurate methods to identify pathological substrate areas known for the maintenance of atrial fibrillation.In the first project, 28 persistent AF patients undergoing electro-anatomical mapping were studied. Statistical analysis was then applied, comparing each patient's bipolar and unipolar voltage maps. Specifically, the extent of agreement between methods was identified, finding the optimal unipolar thresholds to locate pathological substrate as determined by the bipolar voltage map. Additionally, the impact of the inter-electrode distances and regional discrepancies on the comparability was explored. For the second part of the project, simulations modelling electrodes of different sizes on a 2D patch and a lasso catheter in a 3D left atrial geometry were performed. This work identified that while the catheter characteristics influence the bipolar voltage values, they do not play a significant role in altering the location of the low voltage areas. The identified unipolar thresholds, which relate the bipolar and unipolar map, can help determine the extent of pathological substrate in an area. Additionally, it was found that larger electrodes deliver smaller voltages, providing techniques to compare results across studies and centres. In the second project, a patient cohort where patients underwent electro-anatomical mapping while in SR and AF was used. The two rhythms could then be compared in each patient, and AF global and regional thresholds relating the rhythms could be identified. Additionally, the effects of inducing AF in patients could be explored and the benefits of different voltage calculation methods analysed. Low voltage thresholds that can better relate mapping in AF with SR were proposed. It was identified that using the regional thresholds proposed in this work could help prevent a false representation of the extent of pathological substrate within an area. Furthermore, using the maximum voltage value in a signal will lead to higher concordance between methods and using a variability measure (sample entropy) can help identify complex propagation patterns distorting the signals in AF. Finally, the last project studied 36 patients who underwent both LGE-MRI and electro-anatomical mapping. Using this cohort, the concordance between different LGE-MRI mapping modalities and voltage and conduction velocity mapping could be investigated. Additionally, a new LGE-MRI analysis method could be developed to improve the agreement between the modalities. Spatial histograms showing typical low voltage and slow conduction regions were created in this work to help clinicians identify important regions to map during a procedure. Moreover, important discrepancies were found between methods, specifically on the posterior wall, which needs further investigation. Lastly, a new LGE-MRI thresholding method was developed, which could be used to identify patients with ACM. Therefore, providing a non-invasive approach which can help to determine whether additional mapping is needed in patients besides performing PVI. The work presented in this thesis provides the clinical community with a deeper understanding of how the different methods to identify pathological substrate compare. Additionally, providing techniques to relate the methods, account for variability between centres and potentially reduce procedure times. Moreover, it was identified that perhaps one-size-fits-all ablation strategies is limited. Thus, this thesis supports the implementation of more personalised ablation approaches.