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BACKGROUND: The development of atrial fibrillation (AF) is a complex multifactorial process. Over the past few decades, much has been learned about the pathophysiological processes that can lead to AF from a variety of specific disease models in animals. However, our ability to recognise these disease processes in AF patients is still limited, which has contributed to the limited progress in improving rhythm control in AF. AIMS/OBJECTIVES: We believe that a better understanding and detection of the individual pathophysiological mechanisms underlying AF is a prerequisite for developing patient-tailored therapies. The RACE V Tissue Bank Project will contribute to the unravelling of the main molecular mechanisms of AF by studying histology and genome-wide RNA expression profiles and combining this information with detailed phenotyping of patients undergoing cardiac surgery. METHODS: As more and more evidence suggests that AF may occur not only during the first days but also during the months and years after surgery, we will systematically study the incidence of AF during the first years after cardiac surgery in patients with or without a history of AF. Both the overall AF burden as well as the pattern of AF episodes will be studied. Lastly, we will study the association between the major molecular mechanisms and the clinical presentation of the patients, including the incidence and pattern of AF during the follow-up period. CONCLUSION: The RACE V Tissue Bank Project combines deep phenotyping of patients undergoing cardiac surgery, including rhythm follow-up, analysis of molecular mechanisms, histological analysis and genome-wide RNA sequencing. This approach will provide detailed insights into the main pathological alterations associated with AF in atrial tissue and thereby contribute to the development of individualised, mechanistically informed patient-tailored treatment for AF.

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Many cardiac pathologies involve changes in tissue structure. Conventional analysis of structural features is extremely time-consuming and subject to observer bias. The possibility to determine spatial interrelations between these features is often not fully exploited. We developed a staining protocol and an ImageJ-based tool (JavaCyte) for automated histological analysis of cardiac structure, including quantification of cardiomyocyte size, overall and endomysial fibrosis, spatial patterns of endomysial fibrosis, fibroblast density, capillary density and capillary size. This automated analysis was compared to manual quantification in several well-characterized goat models of atrial fibrillation (AF). In addition, we tested inter-observer variability in atrial biopsies from the CATCH-ME consortium atrial tissue bank, with patients stratified by their cardiovascular risk profile for structural remodeling. We were able to reproduce previous manually derived histological findings in goat models for AF and AV block (AVB) using JavaCyte. Furthermore, strong correlation was found between manual and automated observations for myocyte count (r = 0.94, p < 0.001), myocyte diameter (r = 0.97, p < 0.001), endomysial fibrosis (r = 0.98, p < 0.001) and capillary count (r = 0.95, p < 0.001) in human biopsies. No significant variation between observers was observed (ICC = 0.89, p < 0.001). We developed and validated an open-source tool for high-throughput, automated histological analysis of cardiac tissue properties. JavaCyte was as accurate as manual measurements, with less inter-observer variability and faster throughput.

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High density contact electrogram data of atrial fibrillation (AF) contain detailed information on recurring activation patterns and dominant signaling pathways. Current methods to analyze these patterns and pathways rely mainly on supervised atrial deflection annotation and wave reconstruction. In this study, we developed a new algorithm to automatically identify recurring patterns and dominant pathways without the need for annotation. A sparse multivariate autoregression model was estimated on short segments of synchronous unipolar electrograms to extract the dominant interactions between electrograms at different recording electrodes. Sparsity of the electrode interaction matrices at several time-lags was maximized by applying a distance-weighted basis pursuit algorithm. Dominant interactions were identified by computing the mean interaction matrix over a number of consecutive time segments. The algorithm was evaluated on high-density recordings with 234 electrodes and 2.4mm electrode spacing in the left and right atrial free wall of a goat model of AF. The method was able to identify relevant patterns of AF, including wave trains, repetitive breakthrough waves and rotating wave activity.

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The analysis of high-density activation maps of atrial fibrillation (AF) provides fundamental insights into the fibrillation wave propagation patterns and thus the mechanisms of AF. Current annotation of local activations in unipolar atrial electrograms and the construction of fibrillation waves require labor-intensive manual editing. To enhance the possibilities for spatiotemporal analysis of AF, we developed a rapid and fully automated procedure to accurately identify local, intrinsic atrial deflections and construct fibrillation waves based on these deflections. In this study, the automated procedure was validated using manually annotated electrograms and wave maps. We show that the novel procedure accurately detects intrinsic deflections (sensitivity=87%, positive predictive value=89%) and that reconstructed wave maps correlate well with manually edited wave maps in terms of number of waves (r=0.96), intra-wave conduction velocity (r=0.97), AF cycle length (r=0.97), and wave size (r=0.96) (p<0.01 in all cases). The automated procedure is therefore an adequate substitute for manual annotation.

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