Cited by 354Atrial Fibrillation Management and OutcomesAcute Ischemic Stroke ManagementCardiovascular Health and Risk Factors

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A Clinical Risk Score for Atrial Fibrillation in a Biracial Prospective Cohort (from the Atherosclerosis Risk In Communities [ARIC] Study)

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Chamberlain, Alanna M., Agarwal, Sunil K., Folsom, Aaron R., Soliman, Elsayed Z., Chambless, Lloyd E., Crow, Richard, Ambrose, Marietta, Alonso, Alvaro·2011·The American Journal of Cardiology

DOI 10.1016/j.amjcard.2010.08.049PMC PMC3031130

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Abstract

A risk score for atrial fibrillation (AF) has been developed by the Framingham Heart Study; however, the applicability of this risk score, derived using data from white patients, to predict new-onset AF in nonwhites is uncertain. Therefore, we developed a 10-year risk score for new-onset AF from risk factors commonly measured in clinical practice using 14,546 subjects from the Atherosclerosis Risk In Communities (ARIC) study, a prospective community-based cohort of blacks and whites in the United States. During 10 years of follow-up, 515 incident AF events occurred. The following variables were included in the AF risk score: age, race, height, smoking status, systolic blood pressure, hypertension medication use, precordial murmur, left ventricular hypertrophy, left atrial enlargement, diabetes, coronary heart disease, and heart failure. The area under the receiver operating characteristics curve (AUC) of a Cox regression model that included the previous variables was 0.78, suggesting moderately good discrimination. The point-based score developed from the coefficients in the Cox model had an AUC of 0.76. This clinical risk score for AF in the Atherosclerosis Risk In Communities cohort compared favorably with the Framingham Heart Study's AF (AUC 0.68), coronary heart disease (CHD) (AUC 0.63), and hard CHD (AUC 0.59) risk scores and the Atherosclerosis Risk In Communities CHD risk score (AUC 0.58). In conclusion, we have developed a risk score for AF and have shown that the different pathophysiologies of AF and CHD limit the usefulness of a CHD risk score in identifying subjects at greater risk of AF.

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Background
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Background
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Method
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Background
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Background
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BackgroundMethodInfluential
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Yamauchi, T., Sakata, Y., Miura, M., Onose, T., Tsuji, K., Abe, R., Oikawa, T., Kasahara, S., Sato, M., Nochioka, K., et al. (2017) · Circulation Journal · doi:10.1253/circj.cj-16-0759 · Cited by 27
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“com Copyright © 2022 Wolters Kluwer of atrial fibrillation [16,17].”
Background
Genetic Variants Related to Height and Risk of Atrial Fibrillation
Rosenberg, M. A., Kaplan, R. C., Siscovick, D. S., Psaty, B. M., Heckbert, S. R., Newton‐Cheh, C., & Mukamal, K. J. (2014) · American Journal of Epidemiology · doi:10.1093/aje/kwu126 · Cited by 25
Atrial electrocardiography in obesity and hypertension: Clinical insights from the Polish‐Norwegian Study (PONS)
Vaidean, G. D., Mańczuk, M., & Magnani, J. W. (2016) · Obesity · doi:10.1002/oby.21678 · Cited by 26
“In the ARIC study, PR prolongation did not have incremental predictive value in the presence of P-wave duration prolongation (33).”
North American Thrombosis Forum, AF Action Initiative Consensus Document
Ruff, C. T., Ansell, J., Becker, R. C., Benjamin, E. J., Deicicchi, D. J., Estes, N. A. M., Ezekowitz, M. D., Fanikos, J. R., Fareed, J., Garcia, D. A., et al. (2016) · The American Journal of Medicine · doi:10.1016/j.amjmed.2016.02.001 · Cited by 24
Relationship between smoking and adverse outcomes in patients with atrial fibrillation: A meta-analysis and systematic review
Zhu, W., Guo, L., & Hong, K. (2016) · International Journal of Cardiology · doi:10.1016/j.ijcard.2016.07.220 · Cited by 25
Comparison of CHA 2 DS 2 -VASc, CHADS 2 and HATCH scores for the prediction of new-onset atrial fibrillation in cancer patients: A nationwide cohort study of 760,339 study participants with competing risk analysis
Hu, W., & Lin, C. (2017) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2017.10.007 · Cited by 25
Selective screening for atrial fibrillation using multivariable risk models
Linker, D. T., Murphy, T., & Mokdad, A. H. (2018) · Heart · doi:10.1136/heartjnl-2017-312686 · Cited by 25
Biomarkers and the prediction of atrial fibrillation: state of the art
O’Neal, W. T., Venkatesh, S., Broughton, S. T., Griffin, W. F., & Soliman, E. Z. (2016) · Vascular Health and Risk Management · doi:10.2147/vhrm.s75537 · Cited by 25
“Additionally, a score was developed from the Atherosclerosis Risk In Communities study in which age, race, height, smoking, systolic blood pressure, treatment of hypertension, cardiac murmur, ECG left ventricular hypertrophy, prolonged P-wave duration, diabetes, coronary heart disease, and heart failure were incorpora…”
Self-citation
A novel atrial fibrillation prediction model for Chinese subjects: a nationwide cohort investigation of 682 237 study participants with random forest model
Hu, W., Hsieh, M.-H., & Lin, C. (2019) · EP Europace · doi:10.1093/europace/euz036 · Cited by 24
Identification of undiagnosed atrial fibrillation patients using a machine learning risk prediction algorithm and diagnostic testing (PULsE-AI): Study protocol for a randomised controlled trial
Hill, N. R., Arden, C., BERESFORD-HULME, L. M., Camm, A. J., Clifton, D. A., Davies, D. W., Farooqui, U., Gordon, J. P., Groves, L., Hurst, M. A., et al. (2020) · Contemporary Clinical Trials · doi:10.1016/j.cct.2020.106191 · Cited by 24
“The algorithm was trained and tested based on retrospective data of patients (≥30 years) listed on the UK Clinical Practice Research Datalink (CPRD) GOLD, and validated against existing AF prediction models (CHARGEAF [14], ARIC [15], Framingham AF [16]).”
Association of epicardial adipose tissue thickness and inflammation parameters with CHA2DS2-VASc score in patients with nonvalvular atrial fibrillation
Akdağ, S., Şimşek, H., Şahin, M., Akyol, A., Düz, R., & Babat, N. (2015) · Therapeutics and Clinical Risk Management · doi:10.2147/tcrm.s94955 · Cited by 24
“Numerous scoring systems have been used in the stratification of thromboembolism risk in AF patients.(2) Of these, CHA 2 DS 2 -VASc is the most commonly suggested system.”
BackgroundMethod
Traditional Cardiovascular Risk Factors for Incident Atrial Fibrillation
Kokubo, Y., & Matsumoto, C. (2016) · Circulation Journal · doi:10.1253/circj.cj-16-0919 · Cited by 24
Predicting atrial fibrillation using a combination of genetic risk score and clinical risk factors
Okubo, Y., Nakano, Y., Ochi, H., Onohara, Y., Tokuyama, T., Motoda, C., Amioka, M., Hironobe, N., Okamura, S., Ikeuchi, Y., et al. (2020) · Heart Rhythm · doi:10.1016/j.hrthm.2020.01.006 · Cited by 23
Risk factors for new-onset atrial fibrillation in patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis
Huang, Q., Xiong, H., Shuai, T., Zhang, M., Zhang, C., Wang, Y., Zhu, L., Lü, J., & Liu, J. (2020) · PeerJ · doi:10.7717/peerj.10376 · Cited by 22
“, HF, CAD and MI) in COPD patients or fewer competing risk factors, resulting in a lower proportion of comorbid factors attributed to new-onset AF in patients with COPD (Chamberlain et al., 2011; Vestbo et al., 2013).”
Background
A Common SCN5A Variant Is Associated with PR Interval and Atrial Fibrillation Among African Americans
Ilkhanoff, L., Arking, D. E., Lemaître, R. N., Alonso, Á., Chen, L. Y., Durda, P., Hesselson, S., Kerr, K. F., Magnani, J. W., Marcus, G. M., et al. (2014) · Journal of Cardiovascular Electrophysiology · doi:10.1111/jce.12483 · Cited by 25
“Age in years (SD) 53 (6) 37 (19) 73 (6) 50 (12) 62 (10) Male (%) 37 43 37 39 45”
Self-citation
C2HEST score predicts clinical outcomes in heart failure with preserved ejection fraction: a secondary analysis of the TOPCAT trial
Liang, W., Wu, Y., Xue, R., Wu, Z., Wu, D., He, J., Dong, Y., Lip, G. Y. H., Zhu, W., & Liu, C. (2021) · BMC Medicine · doi:10.1186/s12916-021-01921-w · Cited by 23
“population, such as the Framingham risk score [25], the Atherosclerosis Risk In Communities Study (ARIC) score [26], HATCH [27], and CHARGE-AF (Cohorts for Heart and Aging Research in Genomic Epidemiology) [28].”
Method
Meta-analysis of CHADS2 Score in Predicting Atrial Fibrillation
Yang, Y., Zhang, Z., Ng, C. Y., Li, G., & Liu, T. (2015) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2015.05.010 · Cited by 22
Cancer antigen-125 plasma level as a biomarker of new-onset atrial fibrillation in postmenopausal women
Sekiguchi, H., Shimamoto, K., Takano, M., Kimura, M., Takahashi, Y., Tatsumi, F., Watanabe, E., Jujo, K., Ishizuka, N., Kawana, M., et al. (2017) · Heart · doi:10.1136/heartjnl-2016-310272 · Cited by 22
Association of N-terminal pro B-type natriuretic peptide (NT-proBNP) change with the risk of atrial fibrillation in the ARIC cohort
Li, L., Selvin, E., Lutsey, P. L., Hoogeveen, R. C., O’Neal, W. T., Soliman, E. Z., Chen, L. Y., & Alonso, Á. (2018) · American Heart Journal · doi:10.1016/j.ahj.2018.07.008 · Cited by 22
Self-citation
Utility of risk prediction models to detect atrial fibrillation in screened participants
Poorthuis, M. H. F., Jones, N. R., Sherliker, P., Clack, R. M., de Borst, G. J., Clarke, R., Lewington, S., Halliday, A. W., & Bulbulia, R. A. (2020) · European Journal of Preventive Cardiology · doi:10.1093/eurjpc/zwaa082 · Cited by 22
“69 in six models.(4,12,32,34,37,40) (Figure 3 and Supplementary material online, eTable 7).”
Background
Machine Learning Methods for Identifying Atrial Fibrillation Cases and Their Predictors in Patients With Hypertrophic Cardiomyopathy: The HCM-AF-Risk Model
Bhattacharya, M., Lu, D.-Y., Ventoulis, I., Greenland, G. V., Yalcin, H., Guan, Y., Marine, J. E., Olgin, J. E., Zimmerman, S. L., Abraham, T. P., et al. (2021) · CJC Open · doi:10.1016/j.cjco.2021.01.016 · Cited by 22
Genetic Polymorphisms for Estimating Risk of Atrial Fibrillation in the General Population: A Prospective Study
Newton‐Cheh, C. (2012) · Archives of Internal Medicine · doi:10.1001/archinternmed.2012.786 · Cited by 21
The Associations between Various Ectopic Visceral Adiposity and Body Surface Electrocardiographic Alterations: Potential Differences between Local and Remote Systemic Effects
Chi, P., Chang, S., Yun, C., Kuo, J., Hung, C., Hou, C. J., Liu, C.-Y., Yang, F.-S., Wu, T.-H., Bezerra, H. G., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0158300 · Cited by 20
Globalization of the Epidemiologic, Clinical, and Financial Burden of Atrial Fibrillation
Boriani, G., & Diemberger, I. (2012) · CHEST Journal · doi:10.1378/chest.12-1091 · Cited by 21
Comparative Effectiveness of Pharmacotherapies for Prevention of Atrial Fibrillation Following Coronary Artery Bypass Surgery
Piccini, J. P., Zhao, Y., Steinberg, B. A., He, X., Mathew, J. P., Fullerton, D. A., Hegland, D. D., Hernandez, A. F., Mills, R. M., Klaskala, W. I., et al. (2013) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2013.05.029 · Cited by 20
“Patients with longer perfusion and crossclamp times had a higher frequency of AF.”
Influential
Variable Importance in Matched Case–Control Studies in Settings of High Dimensional Data
Balasubramanian, R., Houseman, E. A., Coull, B. A., Lev, M. H., Schwamm, L. H., & Betensky, R. A. (2014) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/rssc.12056 · Cited by 20
“Several well-established multivariable algorithms, such as random forests (Breiman, 2001) and support vector machines (SVMs) (Cortes and Vapnik, 1995) are popular for analysing high dimensional, unmatched data. Random forests is a classification procedure based on aggregation of results from several individual classif…”
MethodInfluential
Pro-atrial natriuretic peptide and prediction of atrial fibrillation and stroke: The Malmö Preventive Project
Berntsson, J., Smith, J. G., Nilsson, P. M., Hedblad, B., Melander, O., & Engström, G. (2017) · European Journal of Preventive Cardiology · doi:10.1177/2047487317693948 · Cited by 20
“A previous validation study from the city of Malmö reported a very high case validity for AF using this definition; 95% of the cases were definite AF, 2% were probable AF and 3% were not AF.21 Incident stroke was ascertained by linkage to the Stroke Register of Malmö.22 Seventy-six per cent of all stroke cases in th…”
MethodInfluential
Novel Risk Modeling Approach of Atrial Fibrillation With Restricted Mean Survival Times
Stærk, L., Preis, S. R., Lin, H., Casas, J. P., Lunetta, K. L., Weng, L.-C. C., Anderson, C. D., Ellinor, P. T., Lubitz, S. A., Benjamin, E. J., et al. (2020) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.119.005918 · Cited by 20
Predictors of Successful Cardioversion with Vernakalant in Patients with Recent‐Onset Atrial Fibrillation
Mochalina, N., Juhlin, T., Öhlin, B., Carlson, J., Holmqvist, F., & Platonov, P. G. (2014) · Annals of Noninvasive Electrocardiology · doi:10.1111/anec.12178 · Cited by 18
Simple predictors for new onset atrial fibrillation
Cabrera, S., Vallès, E., Benito, B., Alcalde, Ó., Jiménez-López, J. I., Fan, R., & Martí‐Almor, J. (2016) · International Journal of Cardiology · doi:10.1016/j.ijcard.2016.07.077 · Cited by 19
Association of Aldosterone Synthase Polymorphism (CYP11B2 -344T>C) and Genetic Ancestry with Atrial Fibrillation and Serum Aldosterone in African Americans with Heart Failure
Bress, A. P., Han, J., Patel, S., Desai, A. A., Mansour, I. N., Groo, V. L., Progar, K., Shah‐Williams, E., Stamos, T. D., Wing, C., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0071268 · Cited by 20
“Covariates were selected for inclusion in the model based on previous data [41,42,43,44,45,46] and included age, sex, body size (BMI), creatinine clearance, systemic hypertension, diabetes, coronary artery disease, left atrial size, mitral regurgitation and European ancestry.”
Method
Bilateral common carotid artery ultrasound for prediction of incident strokes using intima-media thickness and external diameter: an observational study
Eigenbrodt, M. L., Evans, G. W., Rose, K. M., Bursac, Z., Tracy, R. E., Mehta, J. L., & Couper, D. J. (2013) · Cardiovascular Ultrasound · doi:10.1186/1476-7120-11-22 · Cited by 19
Machine learning prediction of atrial fibrillation in cardiovascular patients using cardiac magnetic resonance and electronic health information
Dykstra, S., Satriano, A., Cornhill, A. K., Lei, L. Y., Labib, D., Mikami, Y., Flewitt, J. A., Rivest, S. M., Sandonato, R., Feuchter, P., et al. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.998558 · Cited by 18
“Targeted eﬀorts to develop and validate AF risk scores have been described (6, 8, 10–12) leveraging data from healthy populations without cardiovascular disease.”
BackgroundMethodInfluential
Evaluation of Huawei smart wearables for detection of atrial fibrillation in patients following ischemic stroke: The Liverpool-Huawei stroke study
Harrison, S. L., Buckley, B. J. R., Zheng, Y., Hill, A. M., Hlaing, T., Davies, R., Guo, Y., Lane, D. A., Lip, G. Y. H., Choi, S. E., et al. (2022) · American Heart Journal · doi:10.1016/j.ahj.2022.12.004 · Cited by 17
Heart Failure and Major Adverse Cardiovascular Events in Atrial Fibrillation Patients: A Retrospective Primary Care Cohort Study
Moltó-Balado, P., Reverté‐Villarroya, S., Monclús-Arasa, C., Balado-Albiol, M. T., Baset-Martínez, S., Carot-Domenech, J., & Clua‐Espuny, J. L. (2023) · Biomedicines · doi:10.3390/biomedicines11071825 · Cited by 25
“Additionally, the role of AF burden in patients with subclinical paroxysmal AF is still under debate [48].”
Background
Incidence, Risk Factors and Outcomes of New Onset Supraventricular Arrhythmias in African American Patients with Severe Sepsis
Lewis, O., Ngwa, J. S., Gillum, R. F., Thomas, A. N., Davis, W., Poddar, V., Adams, G. R., Alvin Thomas, Jr., & Mehari, A. (2016) · Ethnicity & Disease · doi:10.18865/ed.26.2.205 · Cited by 19
Role of CHA2DS2-VASc score in predicting new-onset atrial fibrillation in patients with type 2 diabetes mellitus with and without hyperosmolar hyperglycaemic state: real-world data from a nationwide cohort
Hu, W., & Lin, C. (2018) · BMJ Open · doi:10.1136/bmjopen-2017-020065 · Cited by 18
“Although prediction models for incident AF were currently available, the major limitations of the scoring system were user unfriendly and relatively complicated for clinical use.(25) 26 Thus, using a large-scale nationwide dataset, we sought to identify the predictive role of CHA2DS2-VASc score in this population.”
Method
Recent-onset atrial fibrillation: challenges and opportunities
Svennberg, E., Freedman, B., Andrade, J. G., Anselmino, M., Biton, Y., Boriani, G., Brandes, A., Buckley, C. M., Cameron, A. C., Clua‐Espuny, J. L., et al. (2025) · European Heart Journal · doi:10.1093/eurheartj/ehaf478 · Cited by 24
Risk factor changes and incident atrial fibrillation among middle-aged men in the Malmö Preventive Project cohort
Johnson, L., Juhlin, T., Engström, G., & Nilsson, P. M. (2015) · European Heart Journal - Cardiovascular Pharmacotherapy · doi:10.1093/ehjcvp/pvv056 · Cited by 17
Hypertension, Prehypertension, Hypertensive Heart Disease, and Atrial Fibrillation
Lau, D. H., Shenasa, H. A., & Shenasa, M. (2021) · Cardiac Electrophysiology Clinics · doi:10.1016/j.ccep.2020.11.009 · Cited by 18
Distinct risk factors of atrial fibrillation in patients with and without coronary artery disease: a cross-sectional analysis of the BOREAS-CAG Registry data
Murakami, N., Tanno, M., Kokubu, N., Nishida, J., Nagano, N., Ohnishi, H., Akasaka, H., Miki, T., Tsuchihashi, K., & Miura, T. (2017) · Open Heart · doi:10.1136/openhrt-2016-000573 · Cited by 17
“8 – 10 However, clinical factors that are not incorporated into the score systems also increase the risk of AF development under certain conditions.”
BackgroundResult
OBESITY ASSOCIATED ATRIAL FIBRILLATION: EPICARDIAL FAT TISSUE IN ETIOPATHOGENESIS
Druzhilov, М. A., & Kuznetsova, T. Y. (2017) · Russian Journal of Cardiology · doi:10.15829/1560-4071-2017-7-178-184 · Cited by 17
Role of PR-Interval In Predicting the Occurrence of Atrial Fibrillation.
Bidstrup, S., OLESEN, M. S., Svendsen, J. H., & Nielsen, J. B. (2013) · Europe PMC (PubMed Central) · doi:10.4022/jafib.956 · Cited by 17
Predictive model for atrial fibrillation in hypertensive diabetic patients
Abellana, R. M., Gonzalez‐Loyola, F., Verdú-Rotellar, J. M., Bustamante, A., Palà, E., Clua‐Espuny, J. L., Montaner, J., Pedrote, A. A., García, J. L. D. V., Ribas-Seguí, D., et al. (2021) · European Journal of Clinical Investigation · doi:10.1111/eci.13633 · Cited by 16
ECG abnormalities and stroke incidence
Agarwal, S. K., & Soliman, E. Z. (2013) · Expert Review of Cardiovascular Therapy · doi:10.1586/14779072.2013.811980 · Cited by 16
Self-citation

Authors: Chamberlain, A. M., Agarwal, S. K., Folsom, A. R., Soliman, E. Z., Chambless, L. E., Crow, R., Ambrose, M., & Alonso, A.
Affiliations: Mayo Clinic (US)
University of North Carolina at Chapel Hill (US)
University of Minnesota (US)
Wake Forest University Health Sciences (US)
Johns Hopkins University (US)
Corresponding author: Chamberlain, Alanna M.
ORCID: Chamberlain, Alanna M.
Agarwal, Sunil K.
Folsom, Aaron R.
Soliman, Elsayed Z.
Chambless, Lloyd E.
Ambrose, Marietta
Venue: The American Journal of Cardiology
Publisher: Elsevier BV
Published: 2011-01
Volume: 107
Issue: 1
Pages: 85-91
ISSN: 0002-9149 (ISSN-L)
DOI: 10.1016/j.amjcard.2010.08.049
PMC: PMC3031130
OpenAlex: W2154432863
Cited by: 354
References: 30
Citations by year: 2026: 6 · 2025: 22 · 2024: 25 · 2023: 19 · 2022: 24 · 2021: 37 · 2020: 30 · 2019: 14 · 2018: 24 · 2017: 26 · 2016: 33 · 2015: 17 · 2014: 22 · 2013: 31 · 2012: 14
FWCI: 11.85
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: The American Journal of Cardiology (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
UNC Libraries
Funders: American Heart Association (RC1HL101056, RC1HL099452)
National Heart, Lung, and Blood Institute (T32-HL-007779, 09SDG2280087)
Topics: Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Atrial Fibrillation Management and Outcomes
Health Sciences › Medicine › Clinical Neurology › Acute Ischemic Stroke Management
Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Health and Risk Factors
Keywords: atrial fibrillation, risk score, new-onset atrial fibrillation, Framingham Heart Study, Atherosclerosis Risk in Communities Study, racial differences, black and white populations, left atrial enlargement, left ventricular hypertrophy, hypertension, diabetes mellitus, heart failure, coronary artery disease
MeSH terms: Atrial Fibrillation, Female, Follow-Up Studies, Forecasting, Humans, Male, Middle Aged, Risk Factors, Cohort Studies, Incidence

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A Clinical Risk Score for Atrial Fibrillation in a Biracial Prospective Cohort (from the Atherosclerosis Risk In Communities [ARIC] Study)

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