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Detection of Atrial Fibrillation in a Large Population Using Wearable Devices: The Fitbit Heart Study

Circulation (New York, N.Y.), 2022-11, Vol.146 (19), p.1415-1424

2022

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Population-Based Screening for Atrial Fibrillation

Circulation research, 2020-06, Vol.127 (1), p.143-154

2020

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ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation

Circulation (New York, N.Y.), 2022-01, Vol.145 (2), p.122-133

2022

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Genetic Predisposition, Clinical Risk Factor Burden, and Lifetime Risk of Atrial Fibrillation

Circulation (New York, N.Y.), 2018-03, Vol.137 (10), p.1027-1038

2018

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Frequency of Cardiac Rhythm Abnormalities in a Half Million Adults

Circulation. Arrhythmia and electrophysiology, 2018-07, Vol.11 (7), p.e006273-e006273

2018

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Genomic basis of atrial fibrillation

Heart (British Cardiac Society), 2018-02, Vol.104 (3), p.201-206

2018

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Performance of Atrial Fibrillation Risk Prediction Models in Over 4 Million Individuals

Circulation. Arrhythmia and electrophysiology, 2021-01, Vol.14 (1), p.e008997-e008997

2021

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Relations between plasma microRNAs, echocardiographic markers of atrial remodeling, and atrial fibrillation: Data from the Framingham Offspring study

PloS one, 2020-08, Vol.15 (8), p.e0236960-e0236960

2020

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Genetics of Atrial Fibrillation: State of the Art in 2017

Heart, lung & circulation, 2017-09, Vol.26 (9), p.894-901

2017

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Deep Learning to Predict Cardiac Magnetic Resonance-Derived Left Ventricular Mass and Hypertrophy From 12-Lead ECGs

Circulation. Cardiovascular imaging, 2021-06, Vol.14 (6), p.e012281

2021

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Twelve–Single Nucleotide Polymorphism Genetic Risk Score Identifies Individuals at Increased Risk for Future Atrial Fibrillation and Stroke

Stroke (1970), 2014-10, Vol.45 (10), p.2856-2862

2014

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Identifying high risk clinical phenogroups of pulmonary hypertension through a clustering analysis

PloS one, 2023-08, Vol.18 (8), p.e0290553-e0290553

2023

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European Ancestry as a Risk Factor for Atrial Fibrillation in African Americans

Circulation (New York, N.Y.), 2010-11, Vol.122 (20), p.2009-2015

2010

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Trajectories of Risk Factors and Risk of New-Onset Atrial Fibrillation in the Framingham Heart Study

Hypertension (Dallas, Tex. 1979), 2016-09, Vol.68 (3), p.597-605

2016

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Atrial flutter: Clinical risk factors and adverse outcomes in the Framingham Heart Study

Heart rhythm, 2016-01, Vol.13 (1), p.233-240

2016

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Temporal trends in cause-specific mortality among individuals with newly diagnosed atrial fibrillation in the Framingham Heart Study

BMC medicine, 2021-07, Vol.19 (1), p.1-10, Article 170

2021

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Atrial Fibrillation Genetic Risk and Ischemic Stroke Mechanisms

Stroke (1970), 2017-06, Vol.48 (6), p.1451-1456

2017

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Proteomics Profiling and Risk of New-Onset Atrial Fibrillation: Framingham Heart Study

Journal of the American Heart Association, 2019-03, Vol.8 (6), p.e010976-e010976

2019

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Common Genetic Variants and Response to Atrial Fibrillation Ablation

Circulation. Arrhythmia and electrophysiology, 2015-04, Vol.8 (2), p.296-302

2015

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Heritability of Atrial Fibrillation

Circulation. Cardiovascular genetics, 2017-12, Vol.10 (6)

2017

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Association Between Frailty and Atrial Fibrillation in Older Adults: The Framingham Heart Study Offspring Cohort

Journal of the American Heart Association, 2021-01, Vol.10 (1), p.e018557-e018557

2021

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Blood Lipids and the Incidence of Atrial Fibrillation: The Multi‐Ethnic Study of Atherosclerosis and the Framingham Heart Study

Journal of the American Heart Association, 2014-10, Vol.3 (5), p.e001211-n/a

2014

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Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations

Nature genetics, 2018-09, Vol.50 (9), p.1219-1224

2018

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Natural Language Processing to Improve Prediction of Incident Atrial Fibrillation Using Electronic Health Records

Journal of the American Heart Association, 2022-08, Vol.11 (15), p.e026014

2022

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Re-CHARGE-AF: Recalibration of the CHARGE-AF Model for Atrial Fibrillation Risk Prediction in Patients With Acute Stroke

Journal of the American Heart Association, 2021-11, Vol.10 (21), p.e022363-e022363

2021

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Detection of Atrial Fibrillation in a Large Population Using Wearable Devices: The Fitbit Heart Study

Population-Based Screening for Atrial Fibrillation

ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation

Genetic Predisposition, Clinical Risk Factor Burden, and Lifetime Risk of Atrial Fibrillation

Frequency of Cardiac Rhythm Abnormalities in a Half Million Adults

Genomic basis of atrial fibrillation

Performance of Atrial Fibrillation Risk Prediction Models in Over 4 Million Individuals

Relations between plasma microRNAs, echocardiographic markers of atrial remodeling, and atrial fibrillation: Data from the Framingham Offspring study

Genetics of Atrial Fibrillation: State of the Art in 2017

Deep Learning to Predict Cardiac Magnetic Resonance-Derived Left Ventricular Mass and Hypertrophy From 12-Lead ECGs

Twelve–Single Nucleotide Polymorphism Genetic Risk Score Identifies Individuals at Increased Risk for Future Atrial Fibrillation and Stroke

Identifying high risk clinical phenogroups of pulmonary hypertension through a clustering analysis

European Ancestry as a Risk Factor for Atrial Fibrillation in African Americans

Trajectories of Risk Factors and Risk of New-Onset Atrial Fibrillation in the Framingham Heart Study

Atrial flutter: Clinical risk factors and adverse outcomes in the Framingham Heart Study

Temporal trends in cause-specific mortality among individuals with newly diagnosed atrial fibrillation in the Framingham Heart Study

Atrial Fibrillation Genetic Risk and Ischemic Stroke Mechanisms

Proteomics Profiling and Risk of New-Onset Atrial Fibrillation: Framingham Heart Study

Common Genetic Variants and Response to Atrial Fibrillation Ablation

Heritability of Atrial Fibrillation

Association Between Frailty and Atrial Fibrillation in Older Adults: The Framingham Heart Study Offspring Cohort

Blood Lipids and the Incidence of Atrial Fibrillation: The Multi‐Ethnic Study of Atherosclerosis and the Framingham Heart Study

Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations

Natural Language Processing to Improve Prediction of Incident Atrial Fibrillation Using Electronic Health Records

Re-CHARGE-AF: Recalibration of the CHARGE-AF Model for Atrial Fibrillation Risk Prediction in Patients With Acute Stroke

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