Clinical, biomarker, and genetic predictors of specific types of atrial fibrillation in a community-based cohort: data of the PREVEND study

Hobbelt, Anne H; Siland, Joylene E.; Geelhoed, Bastiaan; Harst, Pim van der; Hillege, Hans L.; Gelder, Isabelle C. Van; Rienstra, Michiel

doi:10.1093/europace/euw016

Cited by 20 publications

(22 citation statements)

References 27 publications

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“…Treatment burden in AF patients is largely unknown. In a single-centre prospective study, AF patient-perceived total treatment burden was higher than in patients with other chronic conditions (27.6% vs. 24.3%, P =0.011), and 1 in 5 AF patients reported a high treatment burden that could question the sustainability of their treatment. Notably, AF patients attributed the highest proportion of treatment burden to healthcare system-related aspects (e.g.…”

Section: Treatment Burdenmentioning

confidence: 89%

2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS)

Hindricks¹,

Potpara²,

Dagres³

et al. 2021

Revista Española de Cardiología (English Edition)

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Section: Treatment Burdenmentioning

confidence: 89%

2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS)

Hindricks¹,

Potpara²,

Dagres³

et al. 2021

Revista Española de Cardiología (English Edition)

Self Cite

322

664

View full text Add to dashboard Cite

show abstract

“…The comprehensive knowledge about the causes and effects of AF can help in proper diagnosis and management of AF. The information about these contributing factors can also be used in machine learning algorithms for auto-detection of AF as it is used by [8]. Authors used the statistical information bout clinical, biomarker and genetic attributes like blood pressure, Glomerular filtration rate (GFR), Genotype, Age and Gender in multivariate multinomial logistic regression based model for AF detection.…”

Section: Introductionmentioning

confidence: 99%

A Review on the State of the Art in Atrial Fibrillation Detection Enabled by Machine Learning

Rizwan

Zoha

Mabrouk

et al. 2021

IEEE Rev. Biomed. Eng.

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Atrial Fibrillation (AF) the most commonly occurring type of cardiac arrhythmia is one of the main causes of morbidity and mortality worldwide. The timely diagnosis of AF is an equally important and challenging task because of its asymptomatic and episodic nature. n this paper, state-ofthe-art ECG data-based machine learning models and signal processing techniques applied for auto diagnosis of AF are reviewed. Moreover, key biomarkers of AF on ECG and the common methods and equipment used for the collection of ECG data are discussed. Besides that, the modern wearable and implantable ECG sensing technologies used for gathering AF data are presented briefly. In the end, key challenges associated with the development of auto diagnosis solutions of AF are also highlighted. It is the first review paper of its kind that comprehensively presents a discussion on all these aspects related to AF auto-diagnosis at one place. It is observed that there is dire need of low energy, low cost but accurate auto diagnosis solutions for the proactive management of AF.

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“…Cardiac senescence, largely attributed to aging, hypertension, obesity, as well as genetic predisposition, has been associated with atrial fibrillation (AF) genesis and progression [ 1 ]. AF is expected to affect 6–16 million individuals in the United States, 14 million in Europe, and 72 million in Asia by 2050 [ 2 ], imposing a surge with economic and social implications for the public health care systems.…”

Section: Introductionmentioning

confidence: 99%

Molecular Insights in Atrial Fibrillation Pathogenesis and Therapeutics: A Narrative Review

et al. 2021

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The prevalence of atrial fibrillation (AF) is bound to increase globally in the following years, affecting the quality of life of millions of people, increasing mortality and morbidity, and beleaguering health care systems. Increasingly effective therapeutic options against AF are the constantly evolving electroanatomic substrate mapping systems of the left atrium (LA) and ablation catheter technologies. Yet, a prerequisite for better long-term success rates is the understanding of AF pathogenesis and maintenance. LA electrical and anatomical remodeling remains in the epicenter of current research for novel diagnostic and treatment modalities. On a molecular level, electrical remodeling lies on impaired calcium handling, enhanced inwardly rectifying potassium currents, and gap junction perturbations. In addition, a wide array of profibrotic stimuli activates fibroblast to an increased extracellular matrix turnover via various intermediaries. Concomitant dysregulation of the autonomic nervous system and the humoral function of increased epicardial adipose tissue (EAT) are established mediators in the pathophysiology of AF. Local atrial lymphomononuclear cells infiltrate and increased inflammasome activity accelerate and perpetuate arrhythmia substrate. Finally, impaired intracellular protein metabolism, excessive oxidative stress, and mitochondrial dysfunction deplete atrial cardiomyocyte ATP and promote arrhythmogenesis. These overlapping cellular and molecular alterations hinder us from distinguishing the cause from the effect in AF pathogenesis. Yet, a plethora of therapeutic modalities target these molecular perturbations and hold promise in combating the AF burden. Namely, atrial selective ion channel inhibitors, AF gene therapy, anti-fibrotic agents, AF drug repurposing, immunomodulators, and indirect cardiac neuromodulation are discussed here.

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Clinical, biomarker, and genetic predictors of specific types of atrial fibrillation in a community-based cohort: data of the PREVEND study

Abstract: We found clinical, biomarker and genetic predictors of specific types of incident AF in a community-based cohort. The genetic background seems to play a more important role than modifiable risk factors in self-terminating AF.

Cited by 20 publications

References 27 publications

2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS)

2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS)

A Review on the State of the Art in Atrial Fibrillation Detection Enabled by Machine Learning

Molecular Insights in Atrial Fibrillation Pathogenesis and Therapeutics: A Narrative Review

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