Atrial fibrosis heterogeneity is a risk for atrial fibrillation in pigs with ischaemic heart failure.

BACKGROUND: Atrial fibrillation (AF) is the most common arrhythmia and is associated with considerable morbidity and mortality. Ischaemic heart failure (IHF) remains one of the most common causes of AF in clinical practice. However, ischaemia-mediated mechanisms leading to AF are still incompletely understood, and thus, current treatment approaches are limited. To improve our understanding of the pathophysiology, we studied a porcine IHF model. METHODS: In pigs, IHF was induced by balloon occlusion of the left anterior descending artery for 90 min. After 30 days of reperfusion, invasive haemodynamic measurements and electrophysiological studies were performed. Masson trichrome and immunofluorescence staining were conducted to assess interstitial fibrosis and myofibroblast activation in different heart regions. RESULTS: After 30 days of reperfusion, heart failure with significantly reduced ejection fraction (left anterior obique 30°, 34.78 ± 3.29% [IHF] vs. 62.03 ± 2.36% [control], p < .001; anterior-posterior 0°, 29.16 ± 3.61% vs. 59.54 ± 1.09%, p < .01) was observed. These pigs showed a significantly higher susceptibility to AF (33.90% [IHF] vs. 12.98% [control], p < .05). Histological assessment revealed aggravated fibrosis in atrial appendages but not in atrial free walls in IHF pigs (11.13 ± 1.44% vs. 5.99 ± .86%, p < .01 [LAA], 8.28 ± .56% vs. 6.01 ± .35%, p < .01 [RAA]), which was paralleled by enhanced myofibroblast activation (12.09 ± .65% vs. 9.00 ± .94%, p < .05 [LAA], 14.37 ± .60% vs. 10.30 ± 1.41%, p < .05 [RAA]). Correlation analysis indicated that not fibrosis per se but its cross-regional heterogeneous distribution across the left atrium was associated with AF susceptibility (r = .6344, p < .01). CONCLUSION: Our results suggest that left atrial cross-regional fibrosis difference rather than overall fibrosis level is associated with IHF-related AF susceptibility, presumably by establishing local conduction disturbances and heterogeneity.

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice.

Arrhythmias are common, affecting millions of patients worldwide. Current treatment strategies are associated with significant side effects and remain ineffective in many patients. To improve patient care, novel and innovative therapeutic concepts causally targeting arrhythmia mechanisms are needed. To study the complex pathophysiology of arrhythmias, suitable animal models are necessary, and mice have been proven to be ideal model species to evaluate the genetic impact on arrhythmias, to investigate fundamental molecular and cellular mechanisms, and to identify potential therapeutic targets. Implantable telemetry devices are among the most powerful tools available to study electrophysiology in mice, allowing continuous ECG recording over a period of several months in freely moving, awake mice. However, due to the huge number of data points (>1 million QRS complexes per day), analysis of telemetry data remains challenging. This article describes a step-by-step approach to analyze ECGs and to detect arrhythmias in long-term telemetry recordings using the software, Ponemah, with its analysis modules, ECG Pro and Data Insights, developed by Data Sciences International (DSI). To analyze basic ECG parameters, such as heart rate, P wave duration, PR interval, QRS interval, or QT duration, an automated attribute analysis was performed using Ponemah to identify P, Q, and T waves within individually adjusted windows around detected R waves. Results were then manually reviewed, allowing adjustment of individual annotations. The output from the attribute-based analysis and the pattern recognition analysis was then used by the Data Insights module to detect arrhythmias. This module allows an automatic screening for individually defined arrhythmias within the recording, followed by a manual review of suspected arrhythmia episodes. The article briefly discusses challenges in recording and detecting ECG signals, suggests strategies to improve data quality, and provides representative recordings of arrhythmias detected in mice using the approach described above.