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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Items 201–300 of 352

Echocardiographic Parameters to Predict Atrial Fibrillation in Clinical Routine—The EAHsy-AF Risk Score
Sieweke, J., Hagemus, J., Biber, S., Berliner, D., Große, G. M., Schallhorn, S., Pfeffer, T. J., Derda, A. A., Neuser, J., Bauersachs, J. K., et al. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.851474 · Cited by 14
“We calculated previously published scores (ATLAS, ARIC, and CHARGE-AF) and the CHADS2-Score in our derivation and validation cohort to determine whether the inclusion of specific echocardiographic parameters of LA-dysfunction and— remodeling within the AF Score is superior to these scores in the ability to discriminat…”
BackgroundMethodInfluential
Associations of Dietary and Lifestyle Components with Atrial Fibrillation
Leszto, K., Frąk, W., Kurciński, S., Sinkowska, J., Skwira, S., Młynarska, E., Rysz, J., & Franczyk, B. (2024) · Nutrients · doi:10.3390/nu16030456 · Cited by 18
“The CHARGE-AF score also included age, height, race, blood pressure, comorbidities such as diabetes, previous heart attack, and HF [133].”
BackgroundMethod
Relation of red blood cell distribution width with CHADS2 and CHA2DS2-VASc score in Chinese patients with non-valvular atrial fibrillation
Liu, T., Shao, Q., Korantzopoulos, P. G., Miao, S., Zhang, Z., Xu, G., Yuan, R., & Li, G. (2016) · International Journal of Cardiology · doi:10.1016/j.ijcard.2016.11.255 · Cited by 15
Comparisons of changes in the adapted diabetes complications severity index and CHA2DS2-VASc score for atrial fibrillation risk stratification in patients with type 2 diabetes mellitus: A nationwide cohort study
Hu, W., Hsieh, M.-H., & Lin, C. (2018) · International Journal of Cardiology · doi:10.1016/j.ijcard.2018.07.010 · Cited by 15
Serum brain-derived neurotrophic factor and risk of atrial fibrillation
Rahman, F., Himali, J. J., Yin, X., Beiser, A., Ellinor, P. T., Lubitz, S. A., Vasan, R. S., Magnani, J. W., McManus, D. D., Seshadri, S. S., et al. (2016) · American Heart Journal · doi:10.1016/j.ahj.2016.07.027 · Cited by 15
Machine learning for atrial fibrillation risk prediction in patients with sleep apnea and coronary artery disease
Silva, C. A., Morillo, C. A., de Castro, C. L., González-Otero, R., Bessani, M., González, R. A., Ramírez, J. C. C., & Otero, L. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.1050409 · Cited by 13
Predicting Late Recurrence of Atrial Fibrillation After Radiofrequency Ablation in Patients With Atrial Fibrillation: Comparison of C2HEST and HATCH Scores
Han, J., Li, G., Zhang, D., Wang, X., & Guo, X. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.907817 · Cited by 14
“(5) Radiofrequency ablation was rejected by patients with AF.”
Background
The Use of Artificial Intelligence to Predict the Development of Atrial Fibrillation
Pipilas, D. C., Friedman, S. F., & Khurshid, S. S. (2023) · Current Cardiology Reports · doi:10.1007/s11886-023-01859-w · Cited by 13
“Several composite scores have been developed [17–22], such as the Framingham Heart Study [17], Atherosclerosis Risk In Communities (ARIC) study [18], Women’s Health Study [23], Cohorts for Heart and Aging Research in Genomic Epidemiology-Atrial Fibrillation (CHARGE-AF) [19], C2HEST [24], and…”
Method
Assessment of atrial conduction time by tissue Doppler echocardiography and P-wave dispersion in smokers
Aktürk, E., Yağmur, J., Açıkgöz, N., Ermiş, N., Cansel, M., Karakuş, Y., Taşolar, H., Eyyüpkoca, F., & Pekdemir, H. (2012) · Journal of Interventional Cardiac Electrophysiology · doi:10.1007/s10840-011-9658-x · Cited by 14
The appropriate use of risk scores in the prediction of atrial fibrillation
O’Neal, W. T., & Alonso, Á. (2016) · Journal of Thoracic Disease · doi:10.21037/jtd.2016.10.96 · Cited by 14
Discrimination between Obstructive Coronary Artery Disease and Cardiac Syndrome X in Women with Typical Angina and Positive Exercise Test; Utility of Cardiovascular Risk Calculators
Saadat, M., Masoudkabir, F., Afarideh, M., Ghodsi, S., & Vasheghani‐Farahani, A. (2019) · Medicina · doi:10.3390/medicina55010012 · Cited by 14
“Subsequent studies addressed their advantage in the prediction of the risk for the development of new-onset AF [29,30].”
Background
Early Detection of Atrial Fibrillation in Chronic Obstructive Pulmonary Disease Patients
Kotlyarov, S. N., & Lyubavin, A. (2024) · Medicina · doi:10.3390/medicina60030352 · Cited by 13
“The model showed good predictive validity, with an AUC of 0.77 (95% CI: 0.75–0.78; p < 0.01), which was significantly higher than that in the Framingham Study [80].”
ResultInfluential
The association between lipoprotein(a) and atrial fibrillation: A systemic review and meta‐analysis
Yang, M., Nasr, B., Liu, J., Du, Y., & Yang, J. (2023) · Clinical Cardiology · doi:10.1002/clc.24086 · Cited by 13
“It is proven that Lp (a) levels are associated with atherosclerotic cardiovascular disease, which, in turn, is associated with incident AF.(16,17) However, to this date, prospective Lp(a) levels associations with incident AF have not been thoroughly investigated.”
Background
Clinical and Echocardiographic Factors Associated With New-Onset Atrial Fibrillation in Heart Failure – Subanalysis of the WARCEF Trial –
Kato, T., Tullio, M. R. D., Qian, M., Wu, M., Thompson, J. L. P., Mann, D. L., Sacco, R. L., Pullicino, P. M., Freudenberger, R. S., Teerlink, J. R., et al. (2016) · Circulation Journal · doi:10.1253/circj.cj-15-1054 · Cited by 13
Differential Prognostic Impact of Atrial Fibrillation in Hospitalized Heart Failure Patients With Preserved Ejection Fraction According to Coronary Artery Disease Status ― Report From the Japanese Nationwide Multicenter Registry ―
Temma, T., Nagai, T., Watanabe, M., Kamada, R., Takahashi, Y., Hagiwara, H., Koya, T., Nakao, M., Omote, K., Kamiya, K., et al. (2020) · Circulation Journal · doi:10.1253/circj.cj-19-0963 · Cited by 13
Development, validation, and proof-of-concept implementation of a two-year risk prediction model for undiagnosed atrial fibrillation using common electronic health data (UNAFIED)
Grout, R. W., Hui, S. L., Imler, T. D., El‐Azab, S. A., Baker, J., Sands, G. H., Ateya, M., & Pike, F. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01482-1 · Cited by 13
“The Atherosclerosis Risk in Communities (ARIC) study provided an alternative AF risk score in a different patient population [6].”
Method
Risk calculator for incident atrial fibrillation across a range of prediction horizons
Wu, J., Nadarajah, R., Nakao, Y. M., Nakao, K., Arbel, R., Haim, M., Zahger, D., Lip, G. Y. H., Cowan, J. C., & Gale, C. P. (2024) · American Heart Journal · doi:10.1016/j.ahj.2024.03.001 · Cited by 12
The Application of Artificial Intelligence in Atrial Fibrillation Patients: From Detection to Treatment
Liang, H., Zhang, H., Wang, J., Shao, X., Wu, S., Lyu, S., Xu, W., Wang, L., Tan, J., Wang, J., et al. (2024) · Reviews in Cardiovascular Medicine · doi:10.31083/j.rcm2507257 · Cited by 12
“Currently, clinical risk scores for predicting AF have been well recognized, such as the Framingham Heart Study (FHS) [44], Atherosclerosis Risk In Communities study (ARIC) [45], Cohorts for Heart and Aging Research in Genomic Epidemiology-Atrial Fibrillation (CHARGE-AF) [46], and C2HEST [47].”
Risk Prediction Model for Incident Atrial Fibrillation in a General Japanese Population ― The Hisayama Study ―
Hata, J., Nagata, T., Sakata, S., Oishi, E., Furuta, Y., Hirakawa, Y., Honda, T., Yoshida, D., Kitazono, T., & Ninomiya, T. (2021) · Circulation Journal · doi:10.1253/circj.cj-20-0794 · Cited by 12
Accuracy of wristwatch-type photoplethysmography in detecting atrial fibrillation in daily life
Nonoguchi, N. M., Soejima, K., Goda, A., Nishimura, K., Onozuka, D., Fujita, S., Koyama, F., Takano, Y., Iguchi, S., Sato, H., et al. (2022) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztac041 · Cited by 11
“Chamberlain et al.(19) reported that age, height, smoking status, systolic blood pressure, hypertension treatment, precordial murmur, left ventricular hypertrophy, left atrial enlargement, diabetes, coronary heart disease, and heart failure were risk factors for both Black and White patients.”
Background
Structural and electrophysiological determinants of atrial cardiomyopathy identify remodeling discrepancies between paroxysmal and persistent atrial fibrillation
Huang, T., Nairn, D., Chen, J., Mueller-Edenborn, B., Pilia, N., Mayer, L., Eichenlaub, M., Weidmann, Z. M., Allgeier, J., Trenk, D., et al. (2023) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.1101152 · Cited by 11
“However, they excelled in FHS (38), CHARGE-AF score (39) and ARIC score (40) in practicability by using less and convenient parameters, the components do not reflect the pathological manifestation of atrial remodeling (e.”
Background
Can we predict new AF occurrence in single-chamber ICD patients? Insights from an observational investigation
Biffi, M., Ziacchi, M., Ricci, R. P., Facchin, D., Morani, G., Landolina, M., Lunati, M., Iacopino, S., Capucci, A., Bianchi, S., et al. (2016) · International Journal of Cardiology · doi:10.1016/j.ijcard.2016.12.126 · Cited by 11
A general presentation on how to carry out a CHARMS analysis for prognostic multivariate models
Palazón‐Bru, A., Martín‐Pérez, F., Mares‐García, E., Beneyto‐Ripoll, C., Gil-Guillén, V. F., Pérez-Sempere, Á., & Carbonell‐Torregrosa, M. Á. (2020) · Statistics in Medicine · doi:10.1002/sim.8660 · Cited by 10
Anticoagulants in Patients with Atrial Fibrillation and End-Stage Renal Disease
Brimble, K. S., Ingram, A. J., Eikelboom, J. W., & Hart, R. G. (2012) · Postgraduate Medicine · doi:10.3810/pgm.2012.11.2609 · Cited by 11
Incremental value of rare genetic variants for the prediction of multifactorial diseases
Mihăescu, R., Pencina, M. J., Alonso, Á., Lunetta, K. L., Heckbert, S. R., Benjamin, E. J., & Janssens, A. C. J. W. (2013) · Genome Medicine · doi:10.1186/gm480 · Cited by 10
“Algorithms for detection of individuals at risk, based on routinely collected clinical risk factors, have already been developed and validated in various populations [17,30,31].”
Background
Incidence of atrial fibrillation and its risk prediction model based on a prospective urban Han Chinese cohort
Ding, L., Li, J., Wang, C., Li, X., Su, Q., Zhang, G., & Xue, F. (2017) · Journal of Human Hypertension · doi:10.1038/jhh.2017.23 · Cited by 11
Development of a Risk Score for Atrial Fibrillation in Adults With Diabetes Mellitus (from the ACCORD Study)
Yang, P. C., Zhao, Y., & Wong, N. D. (2020) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2020.03.002 · Cited by 11
The Impact of Time Horizon on Classification Accuracy: Application of Machine Learning to Prediction of Incident Coronary Heart Disease
Simon, S. T., Mandair, D., Albakri, A., Fohner, A. E., Simon, N., Lange, L. A., Biggs, M. L., Mukamal, K. J., Psaty, B. M., & Rosenberg, M. A. (2022) · JMIR Cardio · doi:10.2196/38040 · Cited by 11
“Given the high prevalence of MI, there is significant focus on identifying those most likely to develop incident coronary heart disease [2-5].”
Background
Early life predictors of atrial fibrillation-related mortality: Evidence from the health and retirement study
Glymour, M. M., Benjamin, E. J., Кошелева, А., Gilsanz, P., Curtis, L. H., & Patton, K. K. (2013) · Health & Place · doi:10.1016/j.healthplace.2012.12.008 · Cited by 10
“(Chamberlain et al., 2011, Mont et al., 2008, Rosengren et al., 2009) However, this finding contrasts with much lifecourse epidemiology, which implicates low birthweight and short adult stature in increased risk of adult cardiovascular disease.”
Result
Baseline Demographics, Safety, and Patient Acceptance of an Insertable Cardiac Monitor for Atrial Fibrillation Screening: The REVEAL-AF Study
Conti, S., Reiffel, J. A., Gersh, B. J., Kowey, P. R., Wachter, R., Halperin, J. L., Kaplon, R. E., Pouliot, E., & Verma, A. (2017) · Journal of Atrial Fibrillation · doi:10.4022/jafib.1551 · Cited by 10
Personalized medicine and atrial fibrillation: will it ever happen?
Lubitz, S. A., & Ellinor, P. T. (2012) · BMC Medicine · doi:10.1186/1741-7015-10-155 · Cited by 9
“In a given sample of individuals without AF, they correctly assigned a higher predicted risk to those who ultimately developed AF only about 60% to 70% of the time [44-46].”
Background
Prediction model for thyrotoxic atrial fibrillation: a retrospective study
Ponomartseva, D. A., Derevitskii, I. V., Kovalchuk, S. V., & Babenko, A. Y. (2021) · BMC Endocrine Disorders · doi:10.1186/s12902-021-00809-3 · Cited by 9
Explainable Machine Learning for Atrial Fibrillation in the General Population Using a Generalized Additive Model ― A Cross-Sectional Study ―
Kawakami, M., Karashima, S., Morita, K., Tada, H., Okada, H., Aono, D., Kometani, M., Nomura, A., Demura, M., Furukawa, K., et al. (2021) · Circulation Reports · doi:10.1253/circrep.cr-21-0151 · Cited by 10
“21 , 22 , 24 Our model also extracted several variables (i.e., age, SBP, γ -GTP [liver function], HbA1c, a past history of CAD, kidney function [creatinine], and anemia [hematocrit, hemoglobin]) that are similar to the factors derived from previous studies.”
Prediction Models ― Why Are They Used or Not Used? ―
Nakamura, F., & Nakai, M. (2017) · Circulation Journal · doi:10.1253/circj.cj-17-1185 · Cited by 8
Genetic Discoveries in Atrial Fibrillation and Implications for Clinical Practice
Mahida, S., & Leeds General Infirmary, G. G. S. (2014) · Arrhythmia & Electrophysiology Review · doi:10.15420/aer.2014.3.2.69 · Cited by 8
Biomarkers of Myocardial Injury and Inflammation after Permanent Pacemaker Implantation: The Lead Fixation Type Effect
Varvarousis, D. (2018) · Journal of Atrial Fibrillation · doi:10.4022/jafib.1798 · Cited by 8
Adult height and incidence of atrial fibrillation and heart failure in older men: The British Regional Heart Study
Wannamethee, S. G., Papacosta, O., LENNON, L. T., Hingorani, A. D., & Whincup, P. H. (2021) · IJC Heart & Vasculature · doi:10.1016/j.ijcha.2021.100835 · Cited by 8
“Indeed height has already been included in clinical prediction score for AF [3].”
Background
Using the C2HEST Score for Predicting Postoperative Atrial Fibrillation After Cardiac Surgery: A Report From the Western Denmark Heart Registry, the Danish National Patient Registry, and the Danish National Prescription Registry
Rasmussen, L. F., Andreasen, J. J., Lundbye‐Christensen, S., Riahi, S., Johnsen, S. P., & Lip, G. Y. H. (2022) · Journal of Cardiothoracic and Vascular Anesthesia · doi:10.1053/j.jvca.2022.03.037 · Cited by 8
Incident Atrial Fibrillation Hazard in Hypertensive Population
Alves‐Cabratosa, L., García-Gil, M., Comas‐Cufí, M., Ponjoan, A., Martí‐Lluch, R., Parramon, D., Blanch, J., & Ramos, R. (2015) · Hypertension · doi:10.1161/hypertensionaha.115.05198 · Cited by 9
Retracted
Prevalence, significance and reversal of abnormal P-wave indices in hypertension: A review and meta-analysis
Aizawa, Y., Sato, T., & Akazawa, K. (2018) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2018.12.008 · Cited by 8
Renal function and outcomes in atrial fibrillation patients after catheter ablation
Kawaji, T., Shizuta, S., Aizawa, T., Yamagami, S., Takeji, Y., Yoshikawa, Y., Kato, M., Yokomatsu, T., Miki, S., Ono, K., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0241449 · Cited by 8
The Influence of Risk Factor Modification on Atrial Fibrillation Outcomes and Their Impact on the Success of Catheter Ablation
Chandan, N., Ashok, V., Hwang, T., Lim, V. G., Lachlan, T., Eftekhari, H., McGregor, G., & Osman, F. (2025) · Reviews in Cardiovascular Medicine · doi:10.31083/rcm27175 · Cited by 7
“Results from the ARIC study [98] have shown that being overweight and obese BMI ≥ 25 kg/m 2 ) accounted for about 18% of AF incidents, making obesity the second strongest AF risk factor.”
AI ‐ ECG for early detection of atrial fibrillation: First‐year results from a stroke prevention study in Shimizu, Japan
Masumura, M., Ohno, A., Yoshinaga, H., Sasaki, T., Yamauchi, Y., Hachiya, H., Takahashi, A., Imai, Y., Fujita, H., Ihara, K., et al. (2025) · Journal of Arrhythmia · doi:10.1002/joa3.70132 · Cited by 10
Prospective multicentric validation of a novel prediction model for paroxysmal atrial fibrillation
Schmidt, C., Benda, S., Kraft, P., Wiedmann, F., Pleger, S. T., Büscher, A., Thomas, D., Wachter, R., Schmid, C. D., Eils, R., et al. (2020) · Clinical Research in Cardiology · doi:10.1007/s00392-020-01773-z · Cited by 7
Modified Taiwan Atrial Fibrillation Score for the Prediction of Incident Atrial Fibrillation
Liao, J., Lim, S., Chen, T., Tuan, T., Chen, S., & Chao, T. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2021.805399 · Cited by 7
Alcohol consumption in relation to the incidence of atrial fibrillation in an elderly Chinese population.
Ye, X., Zhang, W., Chen, Y., Miao, C., Huang, Q., Sheng, C., Shao, S., Wang, D., Xu, S., Lei, L., et al. (2022) · PubMed Central · doi:10.11909/j.issn.1671-5411.2022.01.005 · Cited by 7
“Previous studies identified older age, female sex,([33,34]) overweight/obesity,([33,34]) cigarette smoking,([35]) and various cardiovascular and metabolic diseases such as hypertension, [33−35] diabetes mellitus, [35 ] heart failure,([33,35]) stroke([34]) and myocardial infarction([34]) as risk factors of incident atr…”
Background
Is machine learning the future for atrial fibrillation screening?
Sivanandarajah, P., Wu, H., Bajaj, N., Khan, S. N., & Ng, F. S. (2022) · Cardiovascular Digital Health Journal · doi:10.1016/j.cvdhj.2022.04.001 · Cited by 8
Use of CHA2DS2-VASc Score to Predict New-Onset Atrial Fibrillation in Chronic Obstructive Pulmonary Disease Patients ― Large-Scale Longitudinal Study ―
Hu, W., & Lin, C. (2017) · Circulation Journal · doi:10.1253/circj.cj-17-0130 · Cited by 7
Genetic Loci Associated With Atrial Fibrillation: Relation to Left Atrial Structure in the Framingham Heart Study
Magnani, J. W., Yin, X., McManus, D. D., Chuang, M. L., Cheng, S. C., Lubitz, S. A., Arora, G., Manning, W. J., Ellinor, P. T., & Benjamin, E. J. (2014) · Journal of the American Heart Association · doi:10.1161/jaha.113.000616 · Cited by 7
“Covariates were selected for their established associations with AF in the Framingham Heart Study and other cohorts, and for their potential relation to longitudinal modification of atrial structure.(13,18,19) Covariates selected for inclusion were age, sex, body mass index, systolic blood pressure, treatment”
Method
Multimodal data integration to predict atrial fibrillation
Yao, Y., Zhang, M. J., Wang, W., Zhuang, Z., He, R., Ji, Y., Knutson, K. A., Norby, F. L., Alonso, Á., Soliman, E. Z., et al. (2024) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztae081 · Cited by 8
Self-citation
Risk Factors of Atrial Fibrillation in Patients with Heart Failure
Sarfraz, A., Akhtar, P., Rizvi, S. N. H., & Karim, M. (2018) · Cureus · doi:10.7759/cureus.3774 · Cited by 6
Predicting Atrial Fibrillation after Ischemic Stroke: Clinical, Genetics, and Electrocardiogram Modelling
Poh, M. Q. W., Tham, C. H., Chee, J. D. M. S., Saffari, S. E., Tan, K. W. K., Tan, L. W., Ng, E. Y., Yeo, C. P. X., Seet, C. Y. H., Xie, J. P., et al. (2022) · Cerebrovascular Diseases Extra · doi:10.1159/000528516 · Cited by 6
Hybrid predictive modelling: Thyrotoxic atrial fibrillation case
Derevitskii, I. V., Savitskaya, D. A., Babenko, A. Y., & Kovalchuk, S. V. (2021) · Journal of Computational Science · doi:10.1016/j.jocs.2021.101365 · Cited by 6
Simple risk scoring using sinus rhythm electrocardiograms predicts the incidence of atrial fibrillation in the general population
Kamada, H., Kawasoe, S., Kubozono, T., Ninomiya, Y., Enokizono, K., Yoshimoto, I., Iriki, Y., Ikeda, Y., Miyata, M., Miyahara, H., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-60219-y · Cited by 5
Atrial flutter/fibrillation in adult congenital heart disease: Insights from a large Asian cohort.
Chiu, S.-N., Tseng, W.-C., Lu, C.-W., Lin, M.-T., Chen, C.-A., Wang, J.-K., & Wu, M.-H. (2024) · Journal of the Formosan Medical Association = Taiwan yi zhi · doi:10.1016/j.jfma.2024.07.023 · Cited by 7
Relationship between SCN5A gene H558R polymorphism and atrial fibrillation in Tibetan and Han nationalities at high altitude
Liu, J., Yao, F., Han, K., Chai, J., Tian, D., Zhang, J., Wang, R., Li, W., Shen, Y., Ma, Y.-F., et al. (2021) · Medicine · doi:10.1097/md.0000000000025229 · Cited by 5
“Similarly, Chamberlain et al[10] reported that the risk of AF was significantly higher in patients with myocardial infarction than in the control group; it was an important predictor of AF.”
Background
Atrial fibrillation in young hospitalized patients: clinical characteristics, predictors of new onset, and outcomes.
Segev, A., Maor, E., Goldenfeld, M., Itelman, E., Grossman, E., Beinart, R., Leshem, E., Klempfner, R., Klang, E., Rahman, N., et al. (2023) · Journal of Cardiology · doi:10.1016/j.jjcc.2023.04.013 · Cited by 4
Evidence Gaps and Lessons in the Early Detection of Atrial Fibrillation: A Prospective Study in a Primary Care Setting (PREFATE Study)
Clua‐Espuny, J. L., Hernández-Pinilla, A., Gentille-Lorente, D. I., Múria-Subirats, E., Forcadell-Arenas, T., de Diego‐Cabanes, C., Ribas-Seguí, D., Diaz-Vilarasau, A., Molins-Rojas, C., Pallejà-Millán, M., et al. (2025) · Biomedicines · doi:10.3390/biomedicines13010119 · Cited by 6
“Despite the development of several AF prediction scores, such as CHARGE-AF [33], ARIC AF [34], AFRICAT [35], and others [10], which demonstrate notable predictive performance [36,37], challenges persist in integrating these tools into clinical records for assessing and monitoring patients at…”
Evaluation of Anticoagulation Practice With New-Onset Atrial Fibrillation in Patients with Sepsis and Septic Shock in Medical Intensive Care Unit: A Retrospective Observational Cohort Study
Arunachalam, K., Sundaram, A. K., Jha, K. K., Thakur, L., & Pond, K. K. (2020) · Cureus · doi:10.7759/cureus.10026 · Cited by 5
The SilenT AtRial FIBrillation (STAR-FIB) study programme – design and rationale
Nozica, N., Lam, A., Goulouti, E., Elchinova, E., Spirito, A., Branca, M., Servatius, H., Noti, F., Seiler, J., Baldinger, S. H., et al. (2021) · Swiss Medical Weekly · doi:10.4414/smw.2021.20421 · Cited by 5
Changing the Paradigm to Understand and Manage Atrial Fibrillation
Dan, G. (2013) · Springer eBooks · doi:10.1007/978-1-4471-5475-4_6 · Cited by 7
“Several clinical based models have been developed aimed to develop scores with a better prediction value and to indicate how to incorporate new biomarkers for the further improvement of the prediction[39–42].”
Background
Clinical risk scores for the prediction of incident atrial fibrillation: a modernized review
Louka, A. M., Tsagkaris, C., & Stoica, A. M. (2021) · Romanian Journal of Internal Medicine · doi:10.2478/rjim-2021-0018 · Cited by 5
“Nevertheless, most risk factors predicted AF by race in a similar manner [9], while the Framingham study performed better in predicting AF in whites, than blacks.”
BackgroundResult
Genomic risk scores in atrial fibrillation: predicting the unpredictable?: Figure 1
Lubitz, S. A., & Husser, D. (2013) · European Heart Journal · doi:10.1093/eurheartj/eht106 · Cited by 5
Translating HARMS2-AF into benefits for our atrial fibrillation patients and the general public: challenges and opportunities
Marcus, G. M. (2023) · European Heart Journal · doi:10.1093/eurheartj/ehad423 · Cited by 5
Association between circulating biomarkers and atrial fibrillation burden in patients with paroxysmal atrial fibrillation: a subanalysis of the RACE V study
Frausing, M. H. J. P., Rienstra, M., Kronborg, M. B., Melis, M. D., Schotten, U., Nielsen, J. C., Tieleman, R. G., Crijns, H. J. G. M., Gelder, I. C. V., & Samuel, M. (2025) · Open Heart · doi:10.1136/openhrt-2025-003433 · Cited by 4
“25 27 Current established risk stratification tools for incident AF do not incorporate biomarkers, 28–31 but inclusion of NT-proBNP might add value, 15 32 33 that is, adding NT-proBNP to the Cohorts for Heart and Aging Research in Genomic Epidemiology AF (CHARGE-AF) risk score was shown to improve…”
Predicting Incident Atrial Fibrillation after Stroke: A Scoping Review of Clinical Scores, Biomarkers, and AI-Enhanced Strategies
de Andrade, J. B. C., Pisa, I. T., de Oliveira, N. S., Gomes, R. P., de Morais, A. B. C. G., da Silva, J. V., Fagundes, T. P., & Goulart, T. O. (2025) · Cerebrovascular Diseases Extra · doi:10.1159/000550097 · Cited by 5
Left atrial functional impairment as a predictor of atrial fibrillation: insights from cardiac CT
Aronson, D., Perlow, D., Abadi, S., & Lessick, J. (2025) · European Radiology · doi:10.1007/s00330-025-11348-z · Cited by 4
Prevalence of heart disease in patients with mitochondrial abnormalities on skeletal muscle biopsy
Binder, M. S., Roda, R. H., Corse, A. M., Sidhu, S., Stewart, S., & Barth, A. S. (2021) · Annals of Clinical and Translational Neurology · doi:10.1002/acn3.51327 · Cited by 4
Value of baseline characteristics in the risk prediction of atrial fibrillation
He, J., Liu, S., Yang, C., & Wei, Y. Q. (2023) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2023.1068562 · Cited by 4
Precise risk‐prediction model including arterial stiffness for new‐onset atrial fibrillation using machine learning techniques
Kanegae, H., Fujishiro, K., Fukatani, K., Ito, T., & Kario, K. (2024) · Journal of Clinical Hypertension · doi:10.1111/jch.14848 · Cited by 4
Is it time to include non-alcoholic fatty liver disease in the current risk scores for atrial fibrillation?
Mantovani, A., & Nascimbeni, F. (2018) · Digestive and Liver Disease · doi:10.1016/j.dld.2018.03.012 · Cited by 4
Examining the lower range of the association between alcohol intake and risk of incident hospitalization with atrial fibrillation
Ariansen, I., Degerud, E., Gjesdal, K., Tell, G. S., & Næss, Ø. (2020) · IJC Heart & Vasculature · doi:10.1016/j.ijcha.2020.100679 · Cited by 4
C2HEST score for atrial fibrillation risk prediction models: a Diagnostic Accuracy Tests meta-analysis
Haybar, H., Shirbandi, K., & Rahim, F. (2021) · The Egyptian Heart Journal · doi:10.1186/s43044-021-00230-0 · Cited by 4
A predictive score for atrial fibrillation in poststroke patients
Teixeira, C. T., Rizelio, V., Robles, A. C., Barros, L. C. M., Silva, G. S., & de Andrade, J. B. C. (2024) · Arquivos de Neuro-Psiquiatria · doi:10.1055/s-0044-1788271 · Cited by 5
A risk score for predicting atrial fibrillation in individuals with preclinical diastolic dysfunction: a retrospective study in a single large urban center in the United States
Li, D. ‘., Quispe, R., Madan, N., Zhang, L., & Taub, C. C. (2019) · BMC Cardiovascular Disorders · doi:10.1186/s12872-019-1024-4 · Cited by 4
Electrocardiographic and Echocardiographic Abnormalities in Patients with Risk Factors for Atrial Fibrillation
Pérez‐Riera, A. R., Barbosa‐Barros, R., Pereira-Rejálaga, L. E., Nikus, K. C., & Shenasa, M. (2021) · Cardiac Electrophysiology Clinics · doi:10.1016/j.ccep.2020.10.002 · Cited by 4
Cardiovascular and Stroke Risk in Japanese Hemodialysis Patients With Atrial Fibrillation
Hasegawa, J., Bieber, B. A., Larkina, M., Robinson, B. M., Wakai, S., Akizawa, T., Saitō, A., Fukuhara, S., & Akiba, T. (2016) · Therapeutic Apheresis and Dialysis · doi:10.1111/1744-9987.12460 · Cited by 4
“The risk factors identified in our study approximately matched the known risk factors for AF development in the nondialyzed population, including age, male sex, smoking, high BMI, hypertension, diabetes, coronary artery disease, and congestive heart failure (18,19).”
Background
More or Less Irregularity in Understanding an Irregular Rhythm: Atrial Fibrillation Classification and Racial Differences
Soliman, E. Z. (2013) · Journal of the American Heart Association · doi:10.1161/jaha.113.000482 · Cited by 4
Self-citation
Identifying the presence of atrial fibrillation during sinus rhythm using a dual-input mixed neural network with ECG coloring technology
Chen, W., Liu, C., Tseng, C., Huang, C.-C., Wu, I., Chen, P., Chang, S., Lin, Y.-J., Lo, L., Chung, F., et al. (2024) · BMC Medical Research Methodology · doi:10.1186/s12874-024-02421-0 · Cited by 5
“Therefore, various risk prediction models, such as the FHS (Framingham Heart Study) [37], ARIC (Atherosclerosis Risk in Communities Study) [38], and CHARGE-AF (Cohorts for Heart and Aging Research in Genomic Epidemiology–Atrial Fibrillation) score [39], have been proposed.”
Validating risk models versus age alone for atrial fibrillation in a young Dutch population cohort: should atrial fibrillation risk prediction be expanded to younger community members?
Himmelreich, J. C. L., Harskamp, R. E., Geelhoed, B., Virdone, S., Lucassen, W. A. M., Gansevoort, R. T., & Rienstra, M. (2022) · BMJ Open · doi:10.1136/bmjopen-2021-057476 · Cited by 3
Clinical Application of the HCM-AF Risk Score in the Prediction of Clinical Outcomes of Polish Patients with Hypertrophic Cardiomyopathy
Stec, M., Suleja, A., Gondko, D., Kuczmik, W., Roman, J., Dziadosz, D., Szydło, K., & Mizia‐Stec, K. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12134484 · Cited by 3
“The performance of their model was compared with other scores, such as CHARGE-AF, FHS and ARIC [14,20,21], and it was significantly higher, taking into account specificity, sensitivity, and area under the ROC curve.”
Method
Association of life’s essential 8 score with incidence of atrial fibrillation: The Framingham heart study
Toro, C., Shapira‐Daniels, A., Prescott, B. R., Xanthakis, V., Nayor, M. G., Gajjar, P., Benjamin, E. J., & Preis, S. R. (2025) · American Journal of Preventive Cardiology · doi:10.1016/j.ajpc.2025.101018 · Cited by 3
Artificial Intelligence‐Enabled Short‐Term Ambulatory Monitoring ECG During Sinus Rhythm for Prediction of Hidden Atrial Fibrillation
Chang, T., Chen, S., Hsieh, J., Huang, G., Hung, K., Guo, S., Liu, C., Chang, S., Lin, Y., Lo, L., et al. (2025) · Journal of Cardiovascular Electrophysiology · doi:10.1111/jce.70028 · Cited by 3
“However, a scoring scheme for the prediction of hidden AF developed for White and Black populations may not be applied well to Asian individuals [13].”
Machine-learning approach to atrial fibrillation prediction among individuals without prior cardiovascular diseases
Ding, M., Murata, S., Louro, J., Hammar, N., & Modig, K. (2025) · Open Heart · doi:10.1136/openhrt-2025-003451 · Cited by 3
Prediction of new-onset atrial fibrillation in sepsis patients by machine learning: A systematic review
Ye, S., Xu, W., Jiang, Z., Zhan, Y., Shen, Y., Su, L., Yang, K., & Ju, B. (2025) · Digital Health · doi:10.1177/20552076251384237 · Cited by 3
“24 In terms of time prediction windows, these models generally predict the timing of NOAF occurrence at an hourly resolution.”
Impact Of Obesity On Cardiac Structural And Functional Changes
Podzolkov, V. I., Евгеньевна, П. А., Bazhanova, U. S., Vanina, D. D., & Vargina, T. S. (2022) · Russian Open Medical Journal · doi:10.15275/rusomj.2022.0103 · Cited by 4
“Based on the results obtained, special calculators were developed to assess the risk of AF development [27].”
Performance of an electronic health record-based predictive model to identify patients with atrial fibrillation across countries
Mokgokong, R., Schnabel, R. B., Witt, H., Miller, R. E., & Lee, T. C. (2022) · PLoS ONE · doi:10.1371/journal.pone.0269867 · Cited by 3
Developing a simple clinical risk score for ibrutinib-associated atrial fibrillation
Jacobson, T., Peigh, G., Patel, R. N., Issa, R. P., & Akhter, N. (2025) · Journal of Interventional Cardiac Electrophysiology · doi:10.1007/s10840-025-01990-4 · Cited by 3
“Other AF risk models have also been developed from the general population, such as the Framingham AF risk score [20] and the ARIC AF risk score [21], which were out-performed by HARMS-2-AF and CHARGE-AF in a recent large cohort study [12].”
Identifying High-Risk Atrial Fibrillation in Diabetes: Evidence from Nomogram and Plasma Metabolomics Analysis
Luo, Q., Ma, X., Mei, S., Wuyun, Q., Zhou, L., Cai, Z., Wen, Y., Wang, S., Yan, J., Li, H., et al. (2025) · Biomedicines · doi:10.3390/biomedicines13071557 · Cited by 4
Racial Differences in Incidence and Clinical Course of Atrial Fibrillation and What Remains to be Investigated
Prineas, R. J., & Soliman, E. Z. (2015) · Current Cardiovascular Risk Reports · doi:10.1007/s12170-014-0433-2 · Cited by 3
Self-citation
Relation of Functional Status to Risk of Development of Atrial Fibrillation
Ramkumar, S., Yang, H., Wang, Y., Nolan, M. T., Negishi, K., Sanders, P., & Marwick, T. H. (2016) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2016.10.043 · Cited by 3
“Relation of Functional Status to Risk of Development of Atrial Fibrillation Satish Ramkumar, MBBS, Hong Yang, BMed, Ying Wang, BMed, Mark Nolan, MBBS, Kazuaki Negishi, MD, PhD, Prashanthan Sanders, MBBS, PhD, Thomas Hugh Marwick, MBBS, PhD, MPH PII: S0002-9149(16)31815-X DOI: 10.”
Background
Risk Stratification of Atrial Fibrillation in Patients with Arterial Hypertension and the Ways of Primary Prevention: A Literature Review
Bukvalnaya, N. V., Yakubova, L. V., & Снежицкий, В. А. (2023) · Кардиология в Беларуси · doi:10.34883/pi.2023.15.2.009 · Cited by 2
Validation, bias assessment, and optimization of the UNAFIED 2-year risk prediction model for undiagnosed atrial fibrillation using national electronic health data
Ateya, M., Aristeridou, D., Sands, G. H., Zielinski, J., Grout, R. W., Colavecchia, A. C., Wazni, O. M., & Haque, S. N. (2024) · Heart Rhythm O2 · doi:10.1016/j.hroo.2024.09.010 · Cited by 2
The mC2HEST Score for Incident Atrial Fibrillation
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Developing and Validating a New Atrial Fibrillation Risk Score Using Medical Examination Items in a Japanese Population ― The Suita Study ―
Arafa, A., Kato, Y., Sakata, S., Ninomiya, T., Khairan, P., Kawachi, H., Nakao, Y. M., Matsumoto, C., Mizuno, A., & Kokubo, Y. (2025) · Circulation Journal · doi:10.1253/circj.cj-24-0939 · Cited by 2
Prevalence and Major Risk Factors for Atrial Fibrillation
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Perspectives on Sex- and Gender-Specific Prediction of New-Onset Atrial Fibrillation by Leveraging Big Data
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Association between big endothelin-1 and CHADS2/CHA2DS2-VASc scores in non-valvular atrial fibrillation.
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AASK Why Is Left Ventricular Hypertrophy So Deleterious?
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Prediction of Paroxysmal Atrial Fibrillation With Incorporating Genomic Information Into AI-Based ECG Analysis
Ihara, K., Nagata, Y., Takahashi, K., Tsuchiya, K., Sugiyama, K., Sasaki, T., Shigeta, T., Yamauchi, Y., Miwa, N., Hachiya, H., et al. (2026) · JACC Asia · doi:10.1016/j.jacasi.2025.10.009 · Cited by 1

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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