Predicting Atrial Fibrillation and Its Complications

Abstract

Atrial fibrillation (AF) is a common cardiac arrhythmia associated with an increased risk of stroke and other complications. Identifying individuals at higher risk of developing AF in the community is now possible using validated predictive models that take into account clinical variables and circulating biomarkers. These models have shown adequate performance in racially and ethnically diverse populations. Similarly, risk stratification schemes predict incidence of ischemic stroke in persons with AF, assisting clinicians and patients in decisions regarding oral anticoagulation use. Complementary schemes have been developed to predict the risk of bleeding in AF patients taking vitamin K antagonists. However, major gaps exist in our ability to predict AF and its complications. Additional research should refine models for AF prediction and determine their value to improve population health and clinical outcomes, advance our ability to predict stroke and other complications in AF patients, and develop predictive models for bleeding events and other adverse effects in patients using non-vitamin K oral anticoagulants. (Circ J 2016; 80: 1061–1066)

Atrial fibrillation (AF) is a common cardiac arrhythmia affecting >30 million persons worldwide.¹ In addition, AF increases the risk of stroke, heart failure, and overall mortality.² With the aging of populations worldwide, the incidence and prevalence of AF are expected to grow in future decades as well as the burden from its associated complications.³ The growing public health significance of AF has spurred efforts to identify individuals at higher risk of developing this arrhythmia and its complications. Identifying individuals more likely to develop AF could facilitate targeting of preventive interventions and screening programs, while risk stratification schemes in AF patients can assist clinicians and patients in treatment decisions.

We present an overview of the expanding field of AF prediction, as follows. First, we describe available models for the prediction of incident AF in the community and their potential applications. Second, we summarize the main existing risk schemes for prediction of ischemic stroke in patients with AF, highlighting some limitations. Third, we discuss prediction of bleeding and other complications in patients with AF. We end by highlighting areas that demand additional research. To inform the review, we searched PubMed through December 31, 2015 using the search terms “atrial fibrillation” AND “prediction”. We considered publications mostly from the past 5 years, though we did not exclude frequently referenced older publications, and selected those considered relevant. Though related to the topic, we do not discuss the extensive work on prediction of AF recurrence after electrical cardioversion or catheter ablation, or on AF prediction in the context of specific clinical contexts, such as postoperative AF after cardiac surgery.

Prediction of AF in the Community

Risk Scores and Equations for AF Prediction

Over the past few years, several risk scores and equations for the prediction of AF in the general population have been developed, published, and validated. Table 1 enumerates in chronological order the published scores, the variables included, the characteristics of the derivation and validation samples, if any, and the performance of the model (discrimination and calibration). Discrimination refers to the ability of the model to separate subjects who develop the outcome from those who do not, while calibration refers to the agreement between observed outcomes and predictions.⁴

Table 1. Risk Scores and Equations for the Prediction of Atrial Fibrillation in the Community

Risk model	Variables	Derivation	Performance	Validation in external populations	Performance
FHS (10-year risk)5	Age, sex, body mass index, SBP, treatment for hypertension, PR interval, cardiac murmur, heart failure	4,764 participants, 100% white, 55% women, 45–95 years of age, mean age 61	C-statistic (95% CI): 0.78 (0.76, 0.80) χ2=4.2 (P=0.09)	AGES:6 4,238 participants, 100% white, 63% women, mean age 76	C-statistic (95% CI): 0.67 (0.64, 0.71) Recalibrated χ2=16.2 (P=0.06)
				CHS:6 5,410 participants, 16% African-American, 84% white, 60% women, 65 and older, mean age 75 years	C-statistic (95% CI): 0.68 (0.66, 0.70) in whites, 0.66 (0.61, 0.71) in African-Americans Recalibrated χ2=46.1 (P<0.001) in whites and 10.6 (P=0.31) in African- Americans
				ARIC:7 14,546 participants, 27% African-American, 73% white, 55% women, 45–64 years of age	C-statistic: 0.68 overall, 0.69 in whites, 0.65 in African-Americans
				MESA:8 6,663 participants, 38% white, 28% African-American, 22% Hispanic, 12% Chinese-American, 53% women, 45–84 years of age, mean age 62	C-statistic (95% CI): 0.75 (0.72, 0.77) overall, 0.75 (0.72, 0.78) in whites, 0.74 (0.70, 0.78) in non-whites χ2=57.4 (P<0.001) overall, χ2=8.1 (P=0.53) in whites, χ2=73.9 (P<0.001) in non-whites
ARIC (10-year risk)7	Age, race, height, smoking, SBP, treatment for hypertension, cardiac murmur, ECG-based left ventricular hypertrophy, ECG-based left atrial enlargement, diabetes, coronary artery disease, heart failure	14,546 participants, 27% African- American, 73% white, 55% women, 45–64 years of age	C-statistic: 0.78 χ2=10.0 (P=0.35)	None
WHS (10-year risk)11	Age, weight, height, SBP, alcohol use, smoking	19,940 participants, 100% white, 100% women, median age 53	C-statistic (95% CI): 0.72 (0.68, 0.75) χ2=8.1 (P=0.43)	None
CHARGE-AF (5-year risk)12		18,556 participants, 81% white, 19% African-American, 57% women, 46–94 years of age, mean age 65	C-statistic (95% CI): 0.77 (0.75, 0.78) χ2=9.3 (P=0.41)	AGES:12 4,469 participants, 100% white, 60% women, mean age 76 years	C-statistic (95% CI): 0.66 (0.63, 0.70) Recalibrated χ2=12.6 (P=0.18)
				Rotterdam Study:12 3,203 participants, 100% white, 59% women, mean age 72 years	C-statistic (95% CI): 0.71 (0.66, 0.75) Recalibrated χ2=16.4 (P=0.06)
				EPIC-Norfolk:13 24,020 participants, >99% white, 55% women, 39–79 years of age, mean age 59	C-statistic (95% CI): 0.81 (0.75, 0.85) χ2=142.2 (P<0.001) Recalibrated χ2=13.3 (P=0.15)
				MESA:8 6,663 participants, 38% white, 28% African-American, 22% Hispanic, 12% Chinese-American, 53% women, 45–84 years of age, mean age 62	C-statistic (95% CI): 0.78 (0.74, 0.81) overall, 0.76 (0.72, 0.81) in whites, 0.78 (0.72, 0.83) in non-whites χ2=25.6 (P=0.002) overall, χ2=14.6 (P=0.10) in whites, χ2=12.3 (P=0.20) in non- whites

AGES, Age, Gene/Environment Susceptibility-Reykjavik Study; ARIC, Atherosclerosis Risk in Communities Study; CHARGE-AF, Cohorts for Aging and Research in Genomic Epidemiology-Atrial Fibrillation; CHS, Cardiovascular Health Study; CI, confidence interval; EPIC, European Prospective Investigation into Cancer and Nutrition; FHS, Framingham Heart Study; MESA, Multi-Ethnic Study of Atherosclerosis; SBP, systolic blood pressure; WHS, Women’s Health Study.

The first published risk score was derived in 4,764 mostly white participants in the Framingham Heart Study (FHS), and used basic demographic and clinical variables to predict the 10-year risk of AF.⁵ The discrimination of the model, assessed with the C-statistic, was good (0.78, 95% confidence interval (CI) 0.76, 0.80). This score was subsequently validated in 4 different cohorts: the Age, Gene/Environment Susceptibility-Reykjavik (AGES) study, Atherosclerosis Risk in Communities (ARIC) study, Cardiovascular Health Study (CHS), and the Multi-Ethnic Study of Atherosclerosis (MESA).^6–8 In these external cohorts, the discrimination of the model was acceptable, ranging from 0.67 in African-American participants in CHS to 0.75 in the racially diverse MESA cohort. In most populations, however, the model required recalibration to adjust the predicted probabilities to the actual risk of AF in the different cohorts. Independently, the ARIC study also developed a 10-year risk score for AF prediction among 14,546 study participants aged 45–64 years.⁷ In contrast to the FHS AF risk score, the ARIC model was based on a bi-racial cohort, including whites and African-Americans. Given the well-established lower risk of AF among non-whites compared with whites,^9,10 attention to race in AF prediction is relevant and the extrapolation of scores developed in a specific racial/ethnic group to another should be done carefully. The discrimination of the ARIC model was similar to the FHS AF risk score (C-statistic 0.78). The ARIC model, however, has not been applied in any external cohorts and, therefore, its validity outside the ARIC population is uncertain. More recently, the Women’s Health Study (WHS), a cohort of mostly white, healthy women, derived and validated a 10-year model among 19,940 participants.¹¹ The model had good discrimination (C-statistic 0.72) and excellent calibration in the WHS cohort, but has not been validated in external populations and its applicability to men is unknown.

The FHS, ARIC, and WHS risk scores and predictive models were derived in single cohorts, and restricted in terms of race/ethnicity (FHS), age (ARIC), or sex (WHS), which may reduce generalizability to other populations. To address this limitation, the Cohorts for Aging and Research in Genomic Epidemiology (CHARGE)-AF consortium derived a new predictive model, pooling data from 18,556 participants in the FHS, CHS and ARIC studies to predict the 5-year risk of AF. This model was then validated in 7,672 participants from the AGES and Rotterdam studies, showing acceptable discrimination.¹² The CHARGE-AF model, which included demographic and clinical information readily available in clinical settings, had good discrimination in the derivation cohorts (C-statistic 0.77, 95% CI 0.75, 0.78) and acceptable in AGES (0.66, 95% CI 0.63, 0.70) and the Rotterdam study (0.71, 95% CI 0.66, 0.75).¹² The CHARGE-AF risk model has been validated in 2 additional cohorts. In the European Prospective Investigation into Cancer and Nutrition (EPIC)-Norfolk study, the model had excellent discrimination (C-statistic 0.81, 95% CI 0.75, 0.85) but it overestimated the risk of AF, requiring recalibration.¹³ Similarly, the CHARGE-AF model had good discrimination in the MESA cohort (C-statistic 0.78, 95% CI 0.74, 0.81), but also overestimated AF risk, particularly among those with the highest observed risk.⁸

Finally, some studies have suggested that scores derived for prediction of stroke in patients with AF, such as the CHADS₂ and CHA₂DS₂-VASc, could be also applied to AF prediction.^14,15 In fact, most of the elements included in these scores (age, diabetes, hypertension, heart failure, vascular disease) are well-established risk factors for AF. However, these scores perform worse than AF-specific predictive models (as assessed with C-statistics, for example) and are not adequately calibrated to predict AF, failing to provide estimates of actual predicted AF risk over a particular time period.⁸

AF Prediction Beyond Clinical Variables

Extensions of these models have evaluated whether information on blood biomarkers, echocardiographic and electrocardiographic (ECG) measurements, or genetic variants would improve prediction of AF beyond the information provided by clinical variables.

Blood Biomarkers The predictive value of a diverse array of circulating biomarkers, including markers of inflammation (high-sensitivity C-reactive protein (CRP), fibrinogen),^16–18 atrial overload (atrial and B-type natriuretic peptides),^16–18 myocardial ischemia (high-sensitivity troponin T and I),^17,19,20 cardiac fibrosis (galectin-3),²¹ and others (soluble ST2, growth differentiation factor-15),²⁰ has been assessed in the literature. Of these, only the natriuretic peptides have consistently demonstrated added predictive value beyond information on clinical variables across multiple populations. For instance, in the CHARGE-AF pooled analysis, which included 5 separate cohorts, B-type natriuretic peptides but not CRP helped in risk reclassification of individuals, as measured by the net reclassification index (NRI).¹⁶ Similar observations have been made in the FHS,²² the Malmö Diet and Cancer Study,¹⁸ the MESA cohort,⁸ and in the Gutenberg Health Study.¹⁷

Electrocardiography Some of the existing AF risk scores and models include ECG-derived variables, such as the PR interval in the FHS AF score, the ARIC score, and the CHARGE-AF model, or ECG-based left ventricular hypertrophy in the ARIC score and the CHARGE-AF model.^5,7,12 Their added predictive value beyond clinical variables, however, is only marginal. Other ECG measurements considered as potential predictors of AF include P-wave indices. A pooled analysis of the FHS and ARIC cohorts found that even though P-wave indices such as duration, area, and terminal force were associated with the incidence of AF, their contribution to risk prediction on top of established risk factors was minimal.²³ Information on atrial ectopy assessed through longer term heart rhythm monitoring, however, could improve AF prediction. An analysis of 1,260 participants in the CHS cohort found that information on premature atrial contraction count from 24-h Holter monitoring led to clinically significant improvements in AF prediction beyond the information provided by the FHS AF score (C-statistic of 0.65 in the FHS AF score alone vs. 0.72 after adding atrial ectopy information to the statistical model).²⁴

Imaging Information on cardiac structure and function obtained from echocardiographic studies, such as left atrial diameter, left ventricular function, left ventricular mass, or left ventricular wall thickness, have not demonstrated benefit in the prediction of AF once demographic and clinical information is considered.^5,25 Whether more novel measures of echocardiography-based left atrial function (eg, left atrial strain by speckle-tracking, tissue Doppler imaging-derived atrial conduction time)^26,27 or other cardiac imaging modalities (eg, periatrial epicardial adipose tissue from computed tomography)²⁸ can be used for AF prediction remains to be determined.

Genetics Recent research has identified several common genetic variants associated with the risk of AF.²⁹ The added value of information on these genetic variants to predict AF has been explored in at least 2 different populations. The WHS cohort found that a genetic risk score, calculated with information on 12 single nucleotide polymorphisms previously associated with AF, significantly improved prediction, measured by change in C-statistic and continuous NRI, beyond a clinical risk score in approximately 20,000 women: the C-statistic increased from 0.72 to 0.74, while the continuous NRI was 0.49 (95% CI 0.30, 0.67).¹¹ In a similar analysis of 27,471 participants of the Malmö Diet and Cancer Study, however, a genetic risk score also based on 12 single nucleotide polymorphisms only minimally improved risk prediction (C-statistic changed from 0.735 to 0.738).³⁰ Notably, none of these analyses considered information on natriuretic peptides, which are possibly the strongest biomarkers for AF risk. Future studies should evaluate whether genetic information improves our ability to predict AF on top of clinical variables and established circulating biomarkers.

Applications of Models for AF Prediction

Available risk scores, though imperfect, may play a role in identifying individuals at higher risk of developing AF, particularly the externally validated FHS and CHARGE-AF models. A follow-up question is whether this information has any clinical or public health implications. We think of 2 major areas in which these scores could be useful: as aids for selection of high-risk participants to screening programs and primary prevention trials, and as benchmarks for the testing of potential novel biomarkers of AF risk.

The interest in developing screening programs for identification of asymptomatic AF is growing.³¹ AF is responsible for a substantial proportion of strokes, and in a number of cases, stroke is the first clinical manifestation of AF.³² Identifying individuals with asymptomatic AF offers a unique preventive opportunity if AF diagnosis is followed by adequate antithrombotic therapy. Restricting screening programs to individuals more likely to have AF (as identified by one of the validated risk scores) would make those programs more cost-effective. A similar rationale can be applied to the selection of participants for primary prevention trials of AF. Currently, there are no established interventions for the primary prevention of AF. Trials testing such interventions will have to be conducted in subgroups at higher risk of AF, which will lead to more efficient designs.

Validated risk scores, particularly those including circulating natriuretic peptides as predictors, can also be used as benchmarks against which novel biomarkers purported to improve AF prediction can be compared. In this era of “precision medicine,” rigorous comparisons with extensively validated risk scores are needed to avoid the hype that frequently surrounds the discovery of novel markers of disease. For example, as summarized above, adequate testing against a model including natriuretic peptides (BNP or NT-proBNP) showed that inflammatory markers such as CRP, despite being associated with increased risk of AF in observational studies, are not particularly useful in AF prediction.^8,16

Predicting Stroke in Persons With AF

Cardioembolic stroke is a major complication of AF. Fortunately, oral anticoagulation, using either vitamin K antagonists or the newer direct oral anticoagulants, has consistently demonstrated a reduction in the risk of ischemic stroke in persons with AF.³³ Oral anticoagulation, however, is not exempt from complications. Thus, identifying patients with a higher stroke risk, more likely to benefit from oral anticoagulation, is imperative in AF treatment. Over the past 15 years several scores and risk stratification schemes have been developed (Table 2).^34–37 Of these, the CHADS₂ and CHA₂DS₂-VASc scores have gained the most acceptance, being included in AF treatment guidelines from different professional organizations across the world.^33,38,39 Predictors consistently associated with the risk of stroke in AF patients in the different risk scores include older age, female sex, prior stroke, hypertension, diabetes, and heart failure.

Table 2. Risk Stratification Schemes for Stroke Prediction in Atrial Fibrillation Patients

Risk score	CHADS2 34	CHA2DS2-VASc 35	Framingham 36	ATRIA 37
Variables	Heart failure	Heart failure	Age	Age
	Hypertension	Hypertension	Female sex	Female sex
	Age	Age	SBP	Diabetes
	Diabetes	Diabetes	Prior stroke/TIA	Heart failure
	Prior stroke/TIA	Female sex	Diabetes	Hypertension
		Prior stroke/TIA		Proteinuria
		Vascular disease		Reduced kidney function or ESRD
				Prior stroke

ESRD, end-stage renal disease; SBP, systolic blood pressure; TIA, transient ischemic stroke.

Despite current recommendations suggesting the use of the CHA₂DS₂-VASc score for stroke risk stratification in AF patients, the evidence supporting this score above others is mixed. Studies that have compared multiple risk scores in the same population have not found meaningful and consistent differences between the various scores. An analysis of 2,720 patients with AF from Olmsted County, Minnesota, in the United States, tested 9 different stratification schemes, reporting similar performance across the models.⁴⁰ Other studies in larger populations have reported one scoring system does better than the rest, but these differences tend not to be clinically relevant and lack consistency across populations.^41,42 Overall, all scores offer limited discrimination ability (C-statistic <0.7) and, in the end, decisions to use one approach over another should balance simplicity, availability of data required for the score calculation, and predictive ability.⁴³ Finally, though some published studies suggest that circulating biomarkers (eg, creatinine, troponin, natriuretic peptides, D-dimer) may improve stroke prediction in AF patients, few of these observations have been replicated in multiple populations.^44–46

Predicting Bleeding and Other Complications in Persons With AF

Even though oral anticoagulation in AF reduces the risk of ischemic stroke and systemic thromboembolism, this benefit is accompanied by an increased bleeding risk. Identifying individuals at higher risk of bleeding complications when using oral anticoagulants may facilitate personalized treatments. To date, at least 4 risk scores for the assessment of bleeding risk in patients with AF treated with vitamin K antagonists have been published (summarized in Table 3).^47–50 Variables consistently associated with increased bleeding risk in this patient population include older age, renal disease, and a history of prior bleeding, with anemia, cancer, and hypertension included in several of the scores. As with the scores used for stroke risk stratification, the existing bleeding predictive models have only moderate discrimination and do not differ significantly from each other when applied to the same population.^51–53 Of note, these scores have been developed for the prediction of bleeding among persons with AF using vitamin K antagonists. Given the different bleeding profile of the newer direct oral anticoagulants (NOACs) compared with vitamin K antagonists, future research should develop bleeding risk scores specifically for patients using NOACs.

Table 3. Risk Stratification Schemes for Bleeding Prediction in Atrial Fibrillation Patients Using Vitamin K Antagonists

Risk score	HEMORR2HAGES 47	HAS-BLED 48	ATRIA 49	ORBIT-AF 50
Variables	Age	Age	Age	Age
	Hepatic/renal disease	Abnormal renal/liver function	Renal disease	Abnormal kidney function
	Hypertension	Hypertension	Hypertension	Prior bleeding
	Prior bleeding	Prior bleeding	Prior bleeding	Anemia
	Stroke	Stroke	Anemia	Antiplatelet use
	Alcohol abuse	Drugs/alcohol		(Heart failure)
	Anemia	Labile INR		(Cancer)
	Cancer			(COPD)
	Reduced platelet count/function			(Hip fracture/osteoporosis)
	Genetic factors			(Smoking)
	Fall risk

COPD, chronic obstructive pulmonary disease; INR, international normalized ratio.

AF is associated with an increased risk of other cardiovascular complications beyond ischemic stroke, such as heart failure or myocardial infarction.⁵⁴ A limited number of published studies have developed models for the prediction of these and other endpoints in persons with AF. For example, investigators from the FHS developed a predictive model including information on age, body mass index, left ventricular hypertrophy, diabetes, cardiac murmur, and prior myocardial infarction.⁵⁵ Though this and other models can help identify individuals at a particular higher risk of AF-related complications, the clinical implications of the information provided from those models are unclear because they do not translate directly into specific changes in the management of these patients.

Next Steps in the Prediction of AF and Its Complications

As summarized in this review, over the past 15 years we have made great advances in our ability to predict AF and its complications. However, a number of areas require additional research.

The discriminative ability of models for prediction of AF needs improvement. Additional work should determine whether novel biomarkers, imaging techniques, and genomic markers can refine the existing predictive models. Also, current models predict 5- or 10-year risk of AF in the community. Future efforts may be directed to the development of models for the prediction of lifetime risk of AF and models that predict AF using information obtained from the electronic health record. The usefulness of these predictive models to implement preventive strategies and screening programs that lead to improved population health and clinical outcomes has not been determined.

The CHA₂DS₂-VASc, HAS-BLED and other scores are widely recommended for the risk stratification of persons with AF. However, multiple studies conducted in diverse settings have demonstrated that these scores have limited ability to separate those who will develop ischemic stroke or other AF-related complications from those who will not. In order to treat the right patients at the right time with the right therapies, additional work needs to build upon the existing scores and improve them.

Finally, with the advent of new therapies for the prevention of cardioembolic stroke in AF, such as NOACs and left atrial appendage closure devices, new models for the prediction of complications among persons receiving these novel treatments need to be developed.

Acknowledgments

This work was supported by grant 16EIA26410001 from the American Heart Association, and grant R01-HL122200 from the National Institutes of Health.

Disclosures

A.A. reports grant support from the American Heart Association and the National Institutes of Health. F.L.N. reports no disclosures.

References

• 1.    Chugh  SS,  Havmoeller  R,  Narayana  K,  Singh  D,  Rienstra  M,  Benjamin  EJ, et al. Worldwide epidemiology of atrial fibrillation: A Global Burden of Disease 2010 Study. Circulation 2014; 129: 837–847.
• 2.    Mozaffarian  D,  Benjamin  EJ,  Go  AS,  Arnett  DK,  Blaha  MJ,  Cushman  M, et al. Heart disease and stroke statistics – 2016 update: A report from the American Heart Association. Circulation 2016; 133: 447–454.
• 3.    Alonso  A,  Bengtson  LG. A rising tide: The global epidemic of atrial fibrillation. Circulation 2014; 129: 829–830.
• 4.    Steyerberg  EW,  Vickers  AJ,  Cook  NR,  Gerds  T,  Gonen  M,  Obuchowski  N, et al. Assessing the performance of prediction models: A framework for traditional and novel measures. Epidemiology 2010; 21: 128–138.
• 5.    Schnabel  RB,  Sullivan  LM,  Levy  D,  Pencina  MJ,  Massaro  JM,  D’Agostino  RB Sr, et al. Development of a risk score for atrial fibrillation (Framingham Heart Study): A community-based cohort study. Lancet 2009; 373: 739–745.
• 6.    Schnabel  RB,  Aspelund  T,  Li  G,  Sullivan  LM,  Suchy-Dicey  A,  Harris  TB, et al. Validation of an atrial fibrillation risk algorithm in whites and African-Americans. Arch Intern Med 2010; 170: 1909–1917.
• 7.    Chamberlain  AM,  Agarwal  SK,  Folsom  AR,  Soliman  EZ,  Chambless  LE,  Crow  R, 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: 85–91.
• 8.    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: e003077, doi:10.1161/JAHA.115.003077.
• 9.    Alonso  A,  Agarwal  SK,  Soliman  EZ,  Ambrose  M,  Chamberlain  AM,  Prineas  RJ, et al. Incidence of atrial fibrillation in whites and African-Americans: The Atherosclerosis Risk in Communities (ARIC) study. Am Heart J 2009; 158: 111–117.
• 10.    Dewland  TA,  Olgin  JE,  Vittinghoff  E,  Marcus  GM. Incident atrial fibrillation among Asians, Hispanics, blacks and whites. Circulation 2013; 128: 2470–2477.
• 11.    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: 2243–2251.
• 12.    Alonso  A,  Krijthe  BP,  Aspelund  T,  Stepas  KA,  Pencina  MJ,  Moser  CB, 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: e000102, doi:10.1161/JAHA.112.000102.
• 13.    Pfister  R,  Brägelmann  J,  Michels  G,  Wareham  NJ,  Lubne  R,  Khaw  KT. Performance of the CHARGE-AF risk model for incident atrial fibrillation in the EPIC Norfolk cohort. Eur J Prev Cardiol 2015; 22: 932–939.
• 14.    Zuo  ML,  Liu  S,  Chan  KH,  Lau  KK,  Chong  BH,  Lam  KF, et al. The CHADS2 and CHA2DS2-VASc scores predict new occurrence of atrial fibrillation and ischemic stroke. J Interv Card Electrophysiol 2013; 37: 47–54.
• 15.    Fauchier  L,  Clementy  N,  Pelade  C,  Collignon  C,  Nicolle  E,  Lip  GY. Patients with ischemic stroke and incident atrial fibrillation: A nationwide cohort study. Stroke 2015; 46: 2432–2437.
• 16.    Sinner  MF,  Stepas  KA,  Moser  CB,  Krijthe  BP,  Aspelund  T,  Sotoodehnia  N, et al. B-type natriuretic peptide and C-reactive protein in the prediction of atrial fibrillation risk: The CHARGE-AF Consortium of community-based cohort studies. Europace 2014; 16: 1426–1433.
• 17.    Schnabel  RB,  Wild  PS,  Wilde  S,  Ojeda  FM,  Schulz  A,  Zeller  T, et al. Multiple biomarkers and atrial fibrillation in the general population. PLoS One 2014; 9: e112486, doi:10.1371/journal.pone.0112486.
• 18.    Smith  JG,  Newton-Cheh  C,  Almgren  P,  Struck  J,  Morgenthaler  NG,  Bergmann  A, et al. Assessment of conventional cardiovascular risk factors and multiple biomarkers for the prediction of incident heart failure and atrial fibrillation. J Am Coll Cardiol 2010; 56: 1712–1719.
• 19.    Filion  KB,  Agarwal  SK,  Ballantyne  CM,  Eberg  M,  Hoogeveen  RC,  Huxley  RR, et al. High-sensitivity cardiac troponin T and the risk of incident atrial fibrillation: The Atherosclerosis Risk in Communities (ARIC) Study. Am Heart J 2015; 169: 31–38.
• 20.    Rienstra  M,  Yin  X,  Larson  MG,  Fontes  JD,  Magnani  JW,  McManus  DD, et al. Relation between soluble ST2, growth differentiation factor-15, and high-sensitivity troponin I and incident atrial fibrillation. Am Heart J 2014; 167: 109–115.
• 21.    Ho  JE,  Yin  X,  Levy  D,  Vasan  RS,  Magnani  JW,  Ellinor  PT, et al. Galectin 3 and incident atrial fibrillation in the community. Am Heart J 2014; 167: 729–734.
• 22.    Schnabel  RB,  Larson  MG,  Yamamoto  JF,  Sullivan  LM,  Pencina  MJ,  Meigs  JB, et al. Relations of biomarkers of distinct pathophysiological pathways and atrial fibrillation incidence in the community. Circulation 2010; 121: 200–207.
• 23.    Magnani  JW,  Zhu  L,  Lopez  F,  Pencina  MJ,  Agarwal  SK,  Soliman  EZ, et al. P-wave indices and atrial fibrillation: Cross-cohort assessments from the Framingham Heart Study (FHS) and Atherosclerosis Risk in Communities (ARIC) study. Am Heart J 2015; 169: 53–61.
• 24.    Dewland  TA,  Vittinghoff  E,  Mandyam  MC,  Heckbert  SR,  Siscovick  DS,  Stein  PK, et al. Atrial ectopy as a predictor of incident atrial fibrillation: A cohort study. Ann Intern Med 2013; 159: 721–728.
• 25.    Bekwelem  W,  Misialek  JR,  Konety  S,  Solomon  SD,  Soliman  EZ,  Loehr  LR, et al. Echocardiographic measures of cardiac structure and function are associated with risk of atrial fibrillation in blacks: The Atherosclerosis Risk in Communities (ARIC) study. PLoS One 2014; 9: e110111, doi:10.1371/journal.pone.0110111.
• 26.    Cameli  M,  Lisi  M,  Focardi  M,  Reccia  R,  Natali  BM,  Sparla  S, et al. Left atrial deformation analysis by speckle tracking echocardiography for prediction of cardiovascular outcomes. Am J Cardiol 2012; 110: 264–269.
• 27.    Yuasa  T,  Imoto  Y. Usefulness of tissue Doppler imaging-derived atrial conduction time for prediction of atrial fibrillation. Circ J 2015; 80: 58–59.
• 28.    Nakanishi  K,  Fukuda  S,  Tanaka  A,  Otsuka  K,  Sakamoto  M,  Taguchi  H, et al. Pero-atrial epicardial adipose tissue is associated with new-onset nonvalvular atrial fibrillation. Circ J 2012; 76: 2748–2754.
• 29.    Ellinor  PT,  Lunetta  KL,  Albert  CM,  Glazer  NL,  Ritchie  MD,  Smith  AV, et al. Meta-analysis identifies six new susceptibility loci for atrial fibrillation. Nat Genet 2012; 44: 670–675.
• 30.    Tada  H,  Shiffman  D,  Smith  JG,  Sjögren  M,  Lubitz  SA,  Ellinor  PT, et al. Twelve-single nucleotide polymorphism genetic risk score identifies individuals at increased risk for future atrial fibrillation and stroke. Stroke 2014; 45: 2856–2862.
• 31.    Ben Freedman  S,  Lowres  N. Asymptomatic atrial fibrillation: The case for screening to prevent stroke. JAMA 2015; 314: 1911–1912.
• 32.    Lin  HJ,  Wolf  PA,  Benjamin  EJ,  Belanger  AJ,  D’Agostino  RB. Newly diagnosed atrial fibrillation and acute stroke: The Framingham Study. Stroke 1995; 26: 1527–1530.
• 33.    January  CT,  Wann  LS,  Alpert  JS,  Calkins  H,  Cigarroa  JE,  Cleveland  JC, et al. 2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation: A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and the Heart Rhythm Society. Circulation 2014; 130: e199–e267, doi:10.1161/CIR.0000000000000040.
• 34.    Gage  BF,  Waterman  AD,  Shannon  W,  Boechler  M,  Rich  MW,  Radford  MJ. Validation of clinical classification schemes for predicting stroke: Results from the National Registry of Atrial Fibrillation. JAMA 2001; 285: 2864–2870.
• 35.    Lip  GYH,  Nieuwlaat  R,  Pisters  R,  Lane  DA,  Crijns  HM. Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach: The Euro Heart Survey on Atrial Fibrillation. Chest 2010; 137: 263–272.
• 36.    Wang  TJ,  Massaro  JM,  Levy  D,  Vasan  RS,  Wolf  PA,  D’Agostino  RB, et al. A risk score for predicting stroke or death in individuals with new-onset atrial fibrillation in the community: The Framingham Heart Study. JAMA 2003; 290: 1049–1056.
• 37.    Singer  DE,  Chang  Y,  Borowsky  LH,  Fang  MC,  Pomernacki  NK,  Udaltsova  N, et al. A new risk scheme to predict ischemic stroke and other thromboembolism in atrial fibrillation: The ATRIA study stroke risk score. J Am Heart Assoc 2013; 2: e000250, doi:10.1161/JAHA.113.000250.
• 38.    Camm  AJ,  Kirchhof  P,  Lip  GYH,  Schotten  U,  Savelieva  I,  Ernst  S, et al. Guidelines for the management of atrial fibrillation. Eur Heart J 2010; 31: 2369–2429.
• 39.   JCS Joint Working Group. Guidelines for pharmacotherapy of atrial fibrillation (JCS 2013): Digest version. Circ J 2014; 78: 1997–2021.
• 40.    Aakre  CA,  McLeod  CJ,  Cha  SS,  Tsang  TSM,  Lip  GYH,  Gersh  BJ. Comparison of clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation. Stroke 2014; 45: 426–431.
• 41.    Chao  TF,  Liu  CJ,  Wang  KL,  Lin  YJ,  Chang  SL,  Lo  LW, et al. Using the CHA2DS2-VASc score for refining stroke risk stratification in ‘low-risk’ Asian patients with atrial fibrillation. J Am Coll Cardiol 2014; 64: 1658–1665.
• 42.    van den Ham  HA,  Klungel  OH,  Singer  DE,  Leufkens  HGM,  van Staa  TP. Comparative performance of ATRIA, CHADS2, and CHA2DS2-VASc risk scores predicting stroke in patients with atrial fibrillation. J Am Coll Cardiol 2015; 66: 1851–1859.
• 43.    Ikeda  T. Which score should be used for risk stratification of ischemic stroke in patients with atrial fibrillation. Circ J 2014; 78: 1331–1332.
• 44.    Piccini  JP,  Stevens  SR,  Chang  Y,  Singer  DE,  Lokhnygina  Y,  Go  AS, et al. Renal dysfunction as a predictor of stroke and systemic embolism in patients with nonvalvular atrial fibrillation: Validation of the R(2)CHADS(2) index in the ROCKET AF (Rivaroxaban Once-daily, oral, direct factor Xa inhibition Compared with vitamin K antagonism for prevention of stroke and Embolism Trial in Atrial Fibrillation) and ATRIA (AnTicoagulation and Risk factors In Atrial fibrillation) study cohorts. Circulation 2013; 127: 224–232.
• 45.    Hijazi  Z,  Oldgren  J,  Andersson  U,  Connolly  SJ,  Ezekowitz  MD,  Hohnloser  SH, et al. Importance of persistent elevation of cardiac biomarkers in atrial fibrillation: A RE-LY substudy. Heart 2014; 100: 1193–1200.
• 46.    Christersson  C,  Wallentin  L,  Andersson  U,  Alexander  JH,  Ansell  J,  De Caterina  R, et al. D-dimer and risk of thromboembolic and bleeding events in patients with atrial fibrillation: Observations from the ARISTOTLE trial. J Thromb Haemost 2014; 12: 1401–1412.
• 47.    Gage  BF,  Yan  Y,  Milligan  PE,  Waterman  AD,  Culverhouse  R,  Rich  MW, et al. Clinical classification schemes for predicting hemorrhage: Results from the National Registry of Atrial Fibrillation (NRAF). Am Heart J 2006; 151: 713–719.
• 48.    Pisters  R,  Lane  DA,  Nieuwlaat  R,  de Vos  CB,  Crijns  HJGM,  Lip  GYH. A novel user-friendly score (HAS-BLED) to assess 1-year risk of major bleeding in patients with atrial fibrillation: The Euro Heart Survey. Chest 2010; 138: 1093–1100.
• 49.    Fang  MC,  Go  AS,  Chang  Y,  Borowsky  LH,  Pomernacki  NK,  Udaltsova  N, et al. A new risk scheme to predict warfarin-associated hemorrhage: The ATRIA (Anticoagulation and Risk Factors in Atrial Fibrillation) Study. J Am Coll Cardiol 2011; 58: 395–401.
• 50.    O’Brien  EC,  Simon  DN,  Thomas  LE,  Hylek  EM,  Gersh  BJ,  Ansell  JE, et al. The ORBIT bleeding score: A simple bedside score to assess bleeding risk in atrial fibrillation. Eur Heart J 2015; 36: 3258–3264.
• 51.    Barnes  GD,  Gu  X,  Haymart  B,  Kline-Rogers  E,  Almany  S,  Kozlowski  J, et al. The predictive ability of the CHADS2 and CHA2DS2-VASc scores for bleeding risk in atrial fibrillation: The MAQI2 experience. Thromb Res 2014; 134: 294–299.
• 52.    Senoo  K,  Proietti  M,  Lane  DA,  Lip  GY. Evaluation of the HAS-BLED, ATRIA and ORBIT bleeding risk scores in atrial fibrillation patients on warfarin. Am J Med 2015 October 16, doi:10.1016/j.amjmed.2015.10.001.
• 53.    Zhu  W,  He  W,  Guo  L,  Wang  X,  Hong  K. The HAS-BLED score for predicting major bleeding risk in anticoagulated patients with atrial fibrillation: A systematic review and meta-analysis. Clin Cardiol 2015; 38: 555–561.
• 54.    Soliman  EZ,  Lopez  F,  O’Neal  WT,  Chen  LY,  Bengtson  L,  Zhang  Z, et al. Atrial fibrillation and risk of ST-segment-elevation versus non-ST-segment-elevation myocardial infarction: The Atherosclerosis Risk in Communities (ARIC) Study. Circulation 2015; 131: 1843–1850.
• 55.    Schnabel  RB,  Rienstra  M,  Sullivan  LM,  Sun  JX,  Moser  CB,  Levy  D, et al. Risk assessment for incident heart failure in individuals with atrial fibrillation. Eur J Heart Fail 2013; 15: 843–849.