The long-term success rate of ablation therapy is still sub-optimal in patients with persistent atrial fibrillation (AF), mostly due to arrhythmia recurrence originating from arrhythmogenic sites outside the pulmonary veins. Computational mod- elling provides a framework to integrate and augment clinical data, potentially enabling the patient-specific identification of AF mechanisms and of the optimal ablation sites. We developed a technology to tailor ablations in anatomical andfunctional digital atrial twins of patients with persistent AF aiming to identify the most successful ablation strategy. Methods and resultsTwenty-nine patient-specific computational models integrating clinical information from tomographic imaging and elec-tro-anatomical activation time and voltage maps were generated. Areas sustaining AF were identified by a personalizedinduction protocol at multiple locations. State-of-the-art anatomical and substrate ablation strategies were comparedwith our proposed Personalized Ablation Lines (PersonAL) plan, which consists of iteratively targeting emergent highdominant frequency (HDF) regions, to identify the optimal ablation strategy. Localized ablations were connected tothe closest non-conductive barrier to prevent recurrence of AF or atrial tachycardia. The first application of the HDF strat-egy had a success of >98% and isolated only 5–6% of the left atrial myocardium. In contrast, conventional ablation strat-egies targeting anatomical or structural substrate resulted in isolation of up to 20% of left atrial myocardium. After asecond iteration of the HDF strategy, no further arrhythmia episode could be induced in any of the patient-specific models. Conclusion The novel PersonAL in silico technology allows to unveil all AF-perpetuating areas and personalize ablation by leveraging atrial digital twins.
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.
Aims Atrial cardiomyopathy (ACM) is associated with new-onset atrial fibrillation, arrhythmia recurrence after pulmonary vein isolation (PVI) and increased risk for stroke. At present, diagnosis of ACM is feasible by endocardial contact mapping of left atrial (LA) low-voltage substrate (LVS) or late gadolinium-enhanced magnetic resonance imaging, but their complexity limits a widespread use. The aim of this study was to assess non-invasive body surface electrocardiographic imaging (ECGI) as a novel clinical tool for diagnosis of ACM compared with endocardial mapping. Methods and results Thirty-nine consecutive patients (66 ± 9 years, 85% male) presenting for their first PVI for persistent atrial fibrillation underwent ECGI in sinus rhythm using a 252-electrode-array mapping system. Subsequently, high-density LA voltage and biatrial activation maps (mean 2090 ± 488 sites) were acquired in sinus rhythm prior to PVI. Freedom from arrhythmia recurrence was assessed within 12 months follow-up. Increased duration of total atrial conduction time (TACT) in ECGI was associated with both increased atrial activation time and extent of LA-LVS in endocardial contact mapping (r = 0.77 and r = 0.66, P < 0.0001 respectively). Atrial cardiomyopathy was found in 23 (59%) patients. A TACT value of 148 ms identified ACM with 91.3% sensitivity and 93.7% specificity. Arrhythmia recurrence occurred in 15 (38%) patients during a follow-up of 389 ± 55 days. Freedom from arrhythmia was significantly higher in patients with a TACT <148 ms compared with patients with a TACT ≥148 ms (82.4% vs. 45.5%, P = 0.019). Conclusion Analysis of TACT in non-invasive ECGI allows diagnosis of patients with ACM, which is associated with a significantly increased risk for arrhythmia recurrence following PVI.
Each heartbeat is initiated by cyclic spontaneous depolarization of cardiomyocytes in the sinus node forming the primary natural pacemaker. In patients with end-stage renal disease undergoing hemodialysis, it was recently shown that the heart rate drops to very low values before they suffer from sudden cardiac death with an unexplained high incidence. We hypothesize that the electrolyte changes commonly occurring in these patients affect sinus node beating rate and could be responsible for severe bradycardia. To test this hypothesis, we extended the Fabbri et al. computational model of human sinus node cells to account for the dynamic intracellular balance of ion concentrations. Using this model, we systematically tested the effect of altered extracellular potassium, calcium, and sodium concentrations. Although sodium changes had negligible (0.15 bpm/mM) and potassium changes mild effects (8 bpm/mM), calcium changes markedly affected the beating rate (46 bpm/mM ionized calcium without autonomic control). This pronounced bradycardic effect of hypocalcemia was mediated primarily by I attenuation due to reduced driving force, particularly during late depolarization. This, in turn, caused secondary reduction of calcium concentration in the intracellular compartments and subsequent attenuation of inward I and reduction of intracellular sodium. Our in silico findings are complemented and substantiated by an empirical database study comprising 22,501 pairs of blood samples and in vivo heart rate measurements in hemodialysis patients and healthy individuals. A reduction of extracellular calcium was correlated with a decrease of heartrate by 9.9 bpm/mM total serum calcium (p < 0.001) with intact autonomic control in the cross-sectional population. In conclusion, we present mechanistic in silico and empirical in vivo data supporting the so far neglected but experimentally testable and potentially important mechanism of hypocalcemia-induced bradycardia and asystole, potentially responsible for the highly increased and so far unexplained risk of sudden cardiac death in the hemodialysis patient population.
The risk of sudden cardiac death (SCD) is increased 14-fold in chronic hemodialysis (HD) patients compared to patients with normal kidney function suffering from cardiovascular diseases. This high rate is not explained by traditional cardiovascular risk factors. Recently, severe bradycardia has been implicated in SCD in HD patients. Mathematical modelling suggests an electrophysiological link between low serum calcium (Ca) levels and bradycardia. Therefore, we analyzed the correlation between heart rate (HR) and Ca as well as potassium (K).
Catheter ablation targeting low voltage areas (LVA) is commonly being used to treat atrial fibrillation (AF) in pa- tients with persistent AF. However, it is not always certain that the areas marked as low voltage (LV) are correct. This can be related to how the voltage is calculated. There- fore, this paper focuses on comparing different calculation methods, specifically, with regards to spatial distribution. Two voltage maps obtained in AF were used, removing points which did not meet the required specifications. The peaks for the remaining points, in regions of the left atrium, were then found and the voltage was calculated based on taking the peak to peak (p2p) for different beats. For around 30% of the points on the map, the voltage only changed by 0.1mV when taking one beat versus all beats. However, for some individual points, the difference was substantial, around 0.8mV, depending on the beat cho- sen. Additionally, the inter-method variability increased by around 0.1mV when considering all methods compared to only methods calculated using more than one point. It was found that taking a method which considers all p2p values would be a more appropriate method for cal- culating the voltage. Thus, providing a technique, which could improve the accuracy of identifying LVA in an AF map.
Clinical and computational studies highlighted the role of atrial anatomy for atrial fibrillation vulnerability. However, personalized computational models are often generated from electroanatomical maps, which might lack important anatomical structures like the appendages, or from imaging data which are potentially affected by segmentation uncertainty. A bi-atrial statistical shape model (SSM) covering relevant structures for electrophysiological simulations was shown to cover atrial shape variability. We hypothesized that it could, therefore, also be used to infer the shape of missing structures and deliver ready-to-use models to assess atrial fibrillation vulnerability in silico. We implemented a highly automatized pipeline to generate a personalized computational model by fitting the SSM to the clinically acquired geometries. We applied our framework to a geometry coming from an electroanatomical map and one derived from magnetic resonance images (MRI). Only landmarks belonging to the left atrium and no information from the right atrium were used in the fitting process. The left atrium surface-to-surface distance between electroanatomical map and a fitted instance of the SSM was 2.26+-1.95 mm. The distance between MRI segmentation and SSM was 2.07+-1.56 mm and 3.59+-2.84 mm in the left and right atrium, respectively. Our semi-automatic pipeline provides ready-to-use personalized computational models representing the original anatomy well by fitting a SSM. We were able to infer the shape of the right atrium even in the case of using information only from the left atrium.
D. Nairn. Multi-Modality Correspondence to Enhance Arrhythmogenic Atrial Substrate Identification: Guiding Persistent Atrial Fibrillation Ablation Therapy. Karlsruher Institut für Technologie (KIT). Dissertation. 2022
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.