Immunohistochemical characteristics of local sites that trigger atrial arrhythmias in response to high-frequency stimulation.

AIMS: The response to high frequency stimulation (HFS) is used to locate putative sites of ganglionated plexuses (GPs), which are implicated in triggering atrial fibrillation (AF). To identify topological and immunohistochemical characteristics of presumed GP sites functionally identified by HFS. METHODS AND RESULTS: Sixty-three atrial sites were tested with HFS in four Langendorff-perfused porcine hearts. A 3.5 mm tip quadripolar ablation catheter was used to stimulate and deliver HFS to the left and right atrial epicardium, within the local atrial refractory period. Tissue samples from sites triggering atrial ectopy/AF (ET) sites and non-ET sites were stained with choline acetyltransferase (ChAT) and tyrosine hydroxylase (TH), for quantification of parasympathetic and sympathetic nerves, respectively. The average cross-sectional area (CSA) of nerves was also calculated. Histomorphometry of six ET sites (9.5%) identified by HFS evoking at least a single atrial ectopic was compared with non-ET sites. All ET sites contained ChAT-immunoreactive (ChAT-IR) and/or TH-immunoreactive nerves (TH-IR). Nerve density was greater in ET sites compared to non-ET sites (nerves/cm2: 162.3 ± 110.9 vs. 69.65 ± 72.48; P = 0.047). Overall, TH-IR nerves had a larger CSA than ChAT-IR nerves (µm2: 11 196 ± 35 141 vs. 2070 ± 5841; P < 0.0001), but in ET sites, TH-IR nerves were smaller than in non-ET sites (µm2: 6021 ± 14 586 vs. 25 254 ± 61 499; P < 0.001). CONCLUSIONS: ET sites identified by HFS contained a higher density of smaller nerves than non-ET sites. The majority of these nerves were within the atrial myocardium. This has important clinical implications for devising an effective therapeutic strategy for targeting autonomic triggers of AF.

Development of a pro-arrhythmic ex vivo intact human and porcine model: cardiac electrophysiological changes associated with cellular uncoupling.

We describe a human and large animal Langendorff experimental apparatus for live electrophysiological studies and measure the electrophysiological changes due to gap junction uncoupling in human and porcine hearts. The resultant ex vivo intact human and porcine model can bridge the translational gap between smaller simple laboratory models and clinical research. In particular, electrophysiological models would benefit from the greater myocardial mass of a large heart due to its effects on far-field signal, electrode contact issues and motion artefacts, consequently more closely mimicking the clinical setting. Porcine (n = 9) and human (n = 4) donor hearts were perfused on a custom-designed Langendorff apparatus. Epicardial electrograms were collected at 16 sites across the left atrium and left ventricle. A total of 1 mM of carbenoxolone was administered at 5 ml/min to induce cellular uncoupling, and then recordings were repeated at the same sites. Changes in electrogram characteristics were analysed. We demonstrate the viability of a controlled ex vivo model of intact porcine and human hearts for electrophysiology with pharmacological modulation. Carbenoxolone reduces cellular coupling and changes contact electrogram features. The time from stimulus artefact to (-dV/dt)max increased between baseline and carbenoxolone (47.9 ± 4.1-67.2 ± 2.7 ms) indicating conduction slowing. The features with the largest percentage change between baseline and carbenoxolone were fractionation + 185.3%, endpoint amplitude - 106.9%, S-endpoint gradient + 54.9%, S point - 39.4%, RS ratio + 38.6% and (-dV/dt)max - 20.9%. The physiological relevance of this methodological tool is that it provides a model to further investigate pharmacologically induced pro-arrhythmic substrates.

Considering discrepancy when calibrating a mechanistic electrophysiology model.

Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions. The field of cardiac simulation has begun to explore and adopt UQ methods to characterize uncertainty in model inputs and how that propagates through to outputs or predictions; examples of this can be seen in the papers of this issue. In this review and perspective piece, we draw attention to an important and under-addressed source of uncertainty in our predictions-that of uncertainty in the model structure or the equations themselves. The difference between imperfect models and reality is termed model discrepancy, and we are often uncertain as to the size and consequences of this discrepancy. Here, we provide two examples of the consequences of discrepancy when calibrating models at the ion channel and action potential scales. Furthermore, we attempt to account for this discrepancy when calibrating and validating an ion channel model using different methods, based on modelling the discrepancy using Gaussian processes and autoregressive-moving-average models, then highlight the advantages and shortcomings of each approach. Finally, suggestions and lines of enquiry for future work are provided. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.

Rethinking multiscale cardiac electrophysiology with machine learning and predictive modelling.

We review some of the latest approaches to analysing cardiac electrophysiology data using machine learning and predictive modelling. Cardiac arrhythmias, particularly atrial fibrillation, are a major global healthcare challenge. Treatment is often through catheter ablation, which involves the targeted localised destruction of regions of the myocardium responsible for initiating or perpetuating the arrhythmia. Ablation targets are either anatomically defined, or identified based on their functional properties as determined through the analysis of contact intracardiac electrograms acquired with increasing spatial density by modern electroanatomic mapping systems. While numerous quantitative approaches have been investigated over the past decades for identifying these critical curative sites, few have provided a reliable and reproducible advance in success rates. Machine learning techniques, including recent deep-learning approaches, offer a potential route to gaining new insight from this wealth of highly complex spatio-temporal information that existing methods struggle to analyse. Coupled with predictive modelling, these techniques offer exciting opportunities to advance the field and produce more accurate diagnoses and robust personalised treatment. We outline some of these methods and illustrate their use in making predictions from the contact electrogram and augmenting predictive modelling tools, both by more rapidly predicting future states of the system and by inferring the parameters of these models from experimental observations.

Analytical approaches for myocardial fibrillation signals.

Atrial and ventricular fibrillation are complex arrhythmias, and their underlying mechanisms remain widely debated and incompletely understood. This is partly because the electrical signals recorded during myocardial fibrillation are themselves complex and difficult to interpret with simple analytical tools. There are currently a number of analytical approaches to handle fibrillation data. Some of these techniques focus on mapping putative drivers of myocardial fibrillation, such as dominant frequency, organizational index, Shannon entropy and phase mapping. Other techniques focus on mapping the underlying myocardial substrate sustaining fibrillation, such as voltage mapping and complex fractionated electrogram mapping. In this review, we discuss these techniques, their application and their limitations, with reference to our experimental and clinical data. We also describe novel tools including a new algorithm to map microreentrant circuits sustaining fibrillation.

Characterisation of re-entrant circuit (or rotational activity) in vitro using the HL1-6 myocyte cell line.

Fibrillation is the most common arrhythmia observed in clinical practice. Understanding of the mechanisms underlying its initiation and maintenance remains incomplete. Functional re-entries are potential drivers of the arrhythmia. Two main concepts are still debated, the "leading circle" and the "spiral wave or rotor" theories. The homogeneous subclone of the HL1 atrial-derived cardiomyocyte cell line, HL1-6, spontaneously exhibits re-entry on a microscopic scale due to its slow conduction velocity and the presence of triggers, making it possible to examine re-entry at the cellular level. We therefore investigated the re-entry cores in cell monolayers through the use of fluorescence optical mapping at high spatiotemporal resolution in order to obtain insights into the mechanisms of re-entry. Re-entries in HL1-6 myocytes required at least two triggers and a minimum colony area to initiate (3.5 to 6.4 mm2). After electrical activity was completely stopped and re-started by varying the extracellular K+ concentration, re-entries never returned to the same location while 35% of triggers re-appeared at the same position. A conduction delay algorithm also allows visualisation of the core of the re-entries. This work has revealed that the core of re-entries is conduction blocks constituted by lines and/or groups of cells rather than the round area assumed by the other concepts of functional re-entry. This highlights the importance of experimentation at the microscopic level in the study of re-entry mechanisms.

Hemoglobin P(50) during a simulated ascent of Mt. Everest, Operation Everest II.

The amount of O(2) available to tissues is essentially the product of cardiac output, [Hb], and O(2) saturation. Saturation depends on P(O2) and the O(2)Hb dissociation curve. With altitude, increased [2,3-DPG] shifts the dissociation curve rightward, but hypocapnia and alkalosis move it leftward. We determined both standard and in vivo P(50) in 5 fit subjects decompressed over 42 days in an altitude chamber to the equivalent of the Mt. Everest summit (Operation Everest II). Arterial and venous blood was sampled at five "altitudes " (P(B) = 760, 429, 347, 282, 253 mmHg), and P(O2), P(CO2), pH, O(2) saturation, [Hb] and [2,3-DPG] were measured. As reported previously, 2,3-DPG levels increased from 1.7 (P(B) = 760) to 3.8 mmol/L (P(B) = 282). Standard P(50) also increased (from 28.2 mmHg at sea level to 33.1 on the summit, p<0.001). Alone, this would have lowered saturation by 12 percentage points at a summit arterial P(O2) of approximately 30 mmHg. However, in vivo P(50) remained between 26 and 27 mmHg throughout due to progressive hypocapnia and alkalosis. Calculations suggest that the increase in standard P(50) did not affect summit V(O2 MAX)), alveolar, arterial and venous P(O2)'s, but reduced arterial and venous O(2) saturations by 8.4 and 17.4 points, respectively, and increased O(2) extraction by 7.9 percentage points. Reduced saturation was balanced by increased extraction, resulting in no significant overall O(2) transport benefit, thus leaving unanswered the question of the purpose of increased [2,3-DPG] concentrations at altitude.