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Current Cardiology Reviews

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ISSN (Print): 1573-403X
ISSN (Online): 1875-6557

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CHARGE-AF: A Useful Score For Atrial Fibrillation Prediction?

Author(s): Christos Goudis*, Stylianos Daios, Fotios Dimitriadis and Tong Liu

Volume 19, Issue 2, 2023

Published on: 19 October, 2022

Article ID: e010922208402 Pages: 6

DOI: 10.2174/1573403X18666220901102557

Price: $65

Abstract

Atrial fibrillation (AF) is the commonest arrhythmia in clinical practice and is associated with increased morbidity and mortality. Various predictive scores for new-onset AF have been proposed, but so far, none have been widely used in clinical practice. CHARGE-AF score was developed from a pooled diverse population from three large cohorts (Atherosclerosis Risk in Communities study, Cardiovascular Health Study and Framingham Heart Study). A simple 5-year predictive model includes the variables of age, race, height, weight, systolic and diastolic blood pressure, current smoking, use of antihypertensive medication, diabetes mellitus, history of myocardial infarction and heart failure. Recent studies report that the CHARGE-AF score has good discrimination for incident AF and seems to be a promising prediction model for this arrhythmia. New screening tools (smartphone apps, smartwatches) are rapidly developing for AF detection. Therefore, the wide application of the CHARGE-AF score in clinical practice and the upcoming usage of mobile health technologies and smartwatches may result in better AF prediction and adequate stroke prevention, especially in high-risk patients.

Keywords: atrial fibrillation, CHARGE-AF score, Cardiovascular, myocardial infarction, FHS-AF, prediction.

Graphical Abstract

[1]
Hindricks G, Potpara T, Dagres N, et al. 2020 ESC guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio Thoracic Surgery (EACTS). Eur Heart J 2021; 42(5): 373-498.
[http://dx.doi.org/10.1093/eurheartj/ehaa612] [PMID: 32860505]
[2]
Andrade J, Khairy P, Dobrev D, Nattel S. The clinical profile and pathophysiology of atrial fibrillation: Relationships among clinical features, epidemiology, and mechanisms. Circ Res 2014; 114(9): 1453-68.
[http://dx.doi.org/10.1161/CIRCRESAHA.114.303211] [PMID: 24763464]
[3]
Alonso A, Krijthe BP, Aspelund T, et al. Simple risk model predicts incidence of atrial fibrillation in a racially and geographically diverse population: The CHARGE-AF consortium. J Am Heart Assoc 2013; 2(2), e000102.
[http://dx.doi.org/10.1161/JAHA.112.000102] [PMID: 23537808]
[4]
Schnabel RB, Sullivan LM, Levy D, et al. Development of a risk score for atrial fibrillation (Framingham Heart Study): A community-based cohort study. Lancet 2009; 373(9665): 739-45.
[http://dx.doi.org/10.1016/S0140-6736(09)60443-8] [PMID: 19249635]
[5]
Chamberlain AM, Agarwal SK, Folsom AR, et al. A clinical risk score for atrial fibrillation in a biracial prospective cohort (from the Atherosclerosis Risk In Communities [ARIC] study). Am J Cardiol 2011; 107(1): 85-91.
[http://dx.doi.org/10.1016/j.amjcard.2010.08.049] [PMID: 21146692]
[6]
Li YG, Bisson A, Bodin A, et al. C 2 HEST Score and prediction of incident atrial fibrillation in poststroke patients: A french nationwide study. J Am Heart Assoc 2019; 8(13), e012546.
[http://dx.doi.org/10.1161/JAHA.119.012546] [PMID: 31234697]
[7]
Li YG, Pastori D, Farcomeni A, et al. A simple clinical risk score (C2HEST) for predicting incident atrial fibrillation in asian subjects. Chest 2019; 155(3): 510-8.
[http://dx.doi.org/10.1016/j.chest.2018.09.011] [PMID: 30292759]
[8]
Everett BM, Cook NR, Conen D, Chasman DI, Ridker PM, Albert CM. Novel genetic markers improve measures of atrial fibrillation risk prediction. Eur Heart J 2013; 34(29): 2243-51.
[http://dx.doi.org/10.1093/eurheartj/eht033] [PMID: 23444395]
[9]
Aronson D, Shalev V, Katz R, Chodick G, Mutlak D. Risk score for prediction of 10-year atrial fibrillation: A community-based study. Thromb Haemost 2018; 118(9): 1556-63.
[http://dx.doi.org/10.1055/s-0038-1668522] [PMID: 30103243]
[10]
Hamada R, Muto S. Simple risk model and score for predicting of incident atrial fibrillation in Japanese. J Cardiol 2019; 73(1): 65-72.
[http://dx.doi.org/10.1016/j.jjcc.2018.06.005] [PMID: 30064946]
[11]
Ding L, Li J, Wang C, et al. Incidence of atrial fibrillation and its risk prediction model based on a prospective urban han chinese cohort. J Hum Hypertens 2017; 31(9): 574-9.
[http://dx.doi.org/10.1038/jhh.2017.23] [PMID: 28357998]
[12]
Christopoulos G, Graff-Radford J, Lopez CL, et al. Artificial intelligence-electrocardiography to predict incident atrial fibrillation. Circ Arrhythm Electrophysiol 2020; 13(12), e009355.
[http://dx.doi.org/10.1161/CIRCEP.120.009355] [PMID: 33185118]
[13]
Hulme OL, Khurshid S, Weng LC, et al. Development and validation of a prediction model for atrial fibrillation using electronic health records. JACC Clin Electrophysiol 2019; 5(11): 1331-41.
[http://dx.doi.org/10.1016/j.jacep.2019.07.016] [PMID: 31753441]
[14]
Himmelreich JCL, Veelers L, Lucassen WAM, et al. Prediction models for atrial fibrillation applicable in the community: A systematic review and meta-analysis. Europace 2020; 22(5): 684-94.
[http://dx.doi.org/10.1093/europace/euaa005] [PMID: 32011689]
[15]
Poorthuis MHF, Jones NR, Sherliker P, et al. Utility of risk prediction models to detect atrial fibrillation in screened participants. Eur J Prev Cardiol 2021; 28(6): 586-95.
[http://dx.doi.org/10.1093/eurjpc/zwaa082] [PMID: 33624100]
[16]
Alonso A, Roetker NS, Soliman EZ, Chen LY, Greenland P, Heckbert SR. Prediction of atrial fibrillation in a racially diverse cohort: The Multi Ethnic Study of Atherosclerosis (MESA). J Am Heart Assoc 2016; 5(2), e003077.
[http://dx.doi.org/10.1161/JAHA.115.003077] [PMID: 26908413]
[17]
Bundy JD, Heckbert SR, Chen LY, Lloyd-Jones DM, Greenland P. Evaluation of risk prediction models of atrial fibrillation (from the Multi-Ethnic Study of Atherosclerosis [MESA]). Am J Cardiol 2020; 125(1): 55-62.
[http://dx.doi.org/10.1016/j.amjcard.2019.09.032] [PMID: 31706453]
[18]
Amponsah MKD, Benjamin EJ, Magnani JW. Atrial fibrillation and race - a contemporary review. Curr Cardiovasc Risk Rep 2013; 7(5): 336-45.
[http://dx.doi.org/10.1007/s12170-013-0327-8] [PMID: 24198867]
[19]
Shulman E, Kargoli F, Aagaard P, et al. Validation of the Framingham heart study and CHARGE-AF risk scores for atrial fibrillation in hispanics, African-Americans, and non-hispanic whites. Am J Cardiol 2016; 117(1): 76-83.
[http://dx.doi.org/10.1016/j.amjcard.2015.10.009] [PMID: 26589820]
[20]
Khurshid S, Kartoun U, Ashburner JM, et al. Performance of atrial fibrillation risk prediction models in over 4 million individuals. Circ Arrhythm Electrophysiol 2021; 14(1), e008997.
[http://dx.doi.org/10.1161/CIRCEP.120.008997] [PMID: 33295794]
[21]
Pluymaekers NAHA, Hermans ANL, van der Velden RMJ, et al. Implementation of an on-demand app-based heart rate and rhythm monitoring infrastructure for the management of atrial fibrillation through teleconsultation: Telecheck-AF. Europace 2021; 23(3): 345-52.
[http://dx.doi.org/10.1093/europace/euaa201] [PMID: 32887994]
[22]
William AD, Kanbour M, Callahan T, et al. Assessing the accuracy of an automated atrial fibrillation detection algorithm using smartphone technology: The iread study. Heart Rhythm 2018; 15(10): 1561-5.
[http://dx.doi.org/10.1016/j.hrthm.2018.06.037] [PMID: 30143448]
[23]
Isakadze N, Martin SS. How useful is the smartwatch ECG? Trends Cardiovasc Med 2020; 30(7): 442-8.
[http://dx.doi.org/10.1016/j.tcm.2019.10.010] [PMID: 31706789]

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