Stationary and Recurrent Properties of Atrial Fibrillation Conduction Patterns in Goat

Introduction. Electrical mapping of the atria is used to assess the substrate of atrial fibrillation (AF). Targeted ablation of the AF substrate assumes spatiotemporal stationarity. In this study we analyzed long AF recordings of AF using high-density contact mapping.Methods. In 12 goats with stable AF 10 successive 60s files were recorded, within a single AF episode. AF cycle length, fractionation index (FI), lateral dissociation, conduction velocity, breakthroughs and preferentiality of conduction (Prefi were assessed to construct AF-property maps. The Pearson correlation coefficient (PCC) between AF-property maps of consecutive recordings was calculated. Recurrence plots and recurrence quantification analysis were used to identify recurrent patterns.Results Spatiotemporal stationarity for the 6 properties were high, PCC ranged from 0.66 +/- 0.11 for Pref to 0.98 +/- 0.01 for FI. The PCC is not affected by the time delay between files. Yet, highly dynamic patterns were found. Recurrence plots revealed few (1.6 +/- 0.7) recurrent patterns in individual animals.Conclusions AF properties were stationary in stable AF. This cannot be attributed to stable recurrent conduction patterns. during This suggests that spatial properties of the atrium determine AF properties

JavaCyte, a novel open-source tool for automated quantification of key hallmarks of cardiac structural remodeling

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

An angiopoietin 2, FGF23, and BMP10 biomarker signature differentiates atrial fibrillation from other concomitant cardiovascular conditions

Early detection of atrial fibrillation (AF) enables initiation of anticoagulation and early rhythm control therapy to reduce stroke, cardiovascular death, and heart failure. In a cross-sectional, observational study, we aimed to identify a combination of circulating biomolecules reflecting different biological processes to detect prevalent AF in patients with cardiovascular conditions presenting to hospital. Twelve biomarkers identified by reviewing literature and patents were quantified on a high-precision, high-throughput platform in 1485 consecutive patients with cardiovascular conditions (median age 69 years [Q1, Q3 60, 78]; 60% male). Patients had either known AF (45%) or AF ruled out by 7-day ECG-monitoring. Logistic regression with backward elimination and a neural network approach considering 7 key clinical characteristics and 12 biomarker concentrations were applied to a randomly sampled discovery cohort (n=933) and validated in the remaining patients (n=552). In addition to age, sex, and body mass index (BMI), BMP10, ANGPT2, and FGF23 identified patients with prevalent AF (AUC 0.743 [95% CI 0.712, 0.775]). These circulating biomolecules represent distinct pathways associated with atrial cardiomyopathy and AF. Neural networks identified the same variables as the regression-based approach. The validation using regression yielded an AUC of 0.719 (95% CI 0.677, 0.762), corroborated using deep neural networks (AUC 0.784 [95% CI 0.745, 0.822]). Age, sex, BMI and three circulating biomolecules (BMP10, ANGPT2, FGF23) are associated with prevalent AF in unselected patients presenting to hospital. Findings should be externally validated. Results suggest that age and different disease processes approximated by these three biomolecules contribute to AF in patients. Our findings have the potential to improve screening programs for AF after external validation

An angiopoietin 2, FGF23, and BMP10 biomarker signature differentiates atrial fibrillation from other concomitant cardiovascular conditions

Abstract Early detection of atrial fibrillation (AF) enables initiation of anticoagulation and early rhythm control therapy to reduce stroke, cardiovascular death, and heart failure. In a cross-sectional, observational study, we aimed to identify a combination of circulating biomolecules reflecting different biological processes to detect prevalent AF in patients with cardiovascular conditions presenting to hospital. Twelve biomarkers identified by reviewing literature and patents were quantified on a high-precision, high-throughput platform in 1485 consecutive patients with cardiovascular conditions (median age 69 years [Q1, Q3 60, 78]; 60% male). Patients had either known AF (45%) or AF ruled out by 7-day ECG-monitoring. Logistic regression with backward elimination and a neural network approach considering 7 key clinical characteristics and 12 biomarker concentrations were applied to a randomly sampled discovery cohort (n = 933) and validated in the remaining patients (n = 552). In addition to age, sex, and body mass index (BMI), BMP10, ANGPT2, and FGF23 identified patients with prevalent AF (AUC 0.743 [95% CI 0.712, 0.775]). These circulating biomolecules represent distinct pathways associated with atrial cardiomyopathy and AF. Neural networks identified the same variables as the regression-based approach. The validation using regression yielded an AUC of 0.719 (95% CI 0.677, 0.762), corroborated using deep neural networks (AUC 0.784 [95% CI 0.745, 0.822]). Age, sex, BMI and three circulating biomolecules (BMP10, ANGPT2, FGF23) are associated with prevalent AF in unselected patients presenting to hospital. Findings should be externally validated. Results suggest that age and different disease processes approximated by these three biomolecules contribute to AF in patients. Our findings have the potential to improve screening programs for AF after external validation

Spatial Dynamics Of Vertical And Horizontal Intergovernmental Collaboration

Although researchers have made progress in understanding motivations behind local government collaboration, there is little research that explores the spatial dynamics of such interactions. Does the idea of collaboration travel horizontally, passed from neighbor to neighbor, or is vertical leadership from state, county, or regional actors more important in influencing local governments’ decisions to share resources and functions? What factors influence local governments’ choices to collaborate with their neighbors versus a regional entity, county, or state government? In this article, we investigate the importance of vertical and horizontal influences when local governments decide to collaborate around land use planning. Using data from a survey of Michigan local government officials, we take a spatial statistical approach to answering this question. We find widespread evidence of collaboration at multiple scales, and observe patterns of both horizontal and vertical influence. We also find that contextual factors help to explain these patterns of collaboration.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/112248/1/juaf12139.pd

Longer and better lives for patients with atrial fibrillation:the 9th AFNET/EHRA consensus conference

Aims: Recent trial data demonstrate beneficial effects of active rhythm management in patients with atrial fibrillation (AF) and support the concept that a low arrhythmia burden is associated with a low risk of AF-related complications. The aim of this document is to summarize the key outcomes of the 9th AFNET/EHRA Consensus Conference of the Atrial Fibrillation NETwork (AFNET) and the European Heart Rhythm Association (EHRA). Methods and results: Eighty-three international experts met in Münster for 2 days in September 2023. Key findings are as follows: (i) Active rhythm management should be part of the default initial treatment for all suitable patients with AF. (ii) Patients with device-detected AF have a low burden of AF and a low risk of stroke. Anticoagulation prevents some strokes and also increases major but non-lethal bleeding. (iii) More research is needed to improve stroke risk prediction in patients with AF, especially in those with a low AF burden. Biomolecules, genetics, and imaging can support this. (iv) The presence of AF should trigger systematic workup and comprehensive treatment of concomitant cardiovascular conditions. (v) Machine learning algorithms have been used to improve detection or likely development of AF. Cooperation between clinicians and data scientists is needed to leverage the potential of data science applications for patients with AF. Conclusions: Patients with AF and a low arrhythmia burden have a lower risk of stroke and other cardiovascular events than those with a high arrhythmia burden. Combining active rhythm control, anticoagulation, rate control, and therapy of concomitant cardiovascular conditions can improve the lives of patients with AF

Longer and better lives for patients with atrial fibrillation: the 9th AFNET/EHRA consensus conference.

AIMS: Recent trial data demonstrate beneficial effects of active rhythm management in patients with atrial fibrillation (AF) and support the concept that a low arrhythmia burden is associated with a low risk of AF-related complications. The aim of this document is to summarize the key outcomes of the 9th AFNET/EHRA Consensus Conference of the Atrial Fibrillation NETwork (AFNET) and the European Heart Rhythm Association (EHRA). METHODS AND RESULTS: Eighty-three international experts met in Münster for 2 days in September 2023. Key findings are as follows: (i) Active rhythm management should be part of the default initial treatment for all suitable patients with AF. (ii) Patients with device-detected AF have a low burden of AF and a low risk of stroke. Anticoagulation prevents some strokes and also increases major but non-lethal bleeding. (iii) More research is needed to improve stroke risk prediction in patients with AF, especially in those with a low AF burden. Biomolecules, genetics, and imaging can support this. (iv) The presence of AF should trigger systematic workup and comprehensive treatment of concomitant cardiovascular conditions. (v) Machine learning algorithms have been used to improve detection or likely development of AF. Cooperation between clinicians and data scientists is needed to leverage the potential of data science applications for patients with AF. CONCLUSIONS: Patients with AF and a low arrhythmia burden have a lower risk of stroke and other cardiovascular events than those with a high arrhythmia burden. Combining active rhythm control, anticoagulation, rate control, and therapy of concomitant cardiovascular conditions can improve the lives of patients with AF

Sparse estimation: applications in atrial fibrillation

Finding important links between large numbers of factors is a topical issue. In this study, a mathematical method was developed allowing dominant links between a certain outcome and many possible predictive factors, or key interactions in a large complex network to be discovered by using relatively few data. This can be achieved by assuming that among the many possible links only a few are actually meaningful. This method has been successfully applied to, among other things, discovering key factors playing a role in the effectiveness of treatment strategies for atrial fibrillation, a type of cardiac arrhythmia

Measuring clinical pathway adherence

AbstractAs clinical pathway adoption continues worldwide, it is necessary to establish adherence measurement methods in order to understand the difficulties and results of implementation. Adherence measurement literature mostly provides binary measurements of adherence to guidelines regarding individual medical activities over patient groups. The resulting measurements are of limited value in view of the pathways actually followed by individual patients. We develop and test dynamic programming formulations for adherence measurement in clinical pathways – based on partially ordered data in medical records and pathway definitions. With these new methods at hand, we analyze clinical pathway adherence at the Cardiovascular Center of Maastricht University Medical Center