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Stroke risk assessment in atrial fibrillation: an update

An article from the e-journal of the ESC Council for Cardiology Practice

    There has been a major paradigm shift towards getting better at identifying the ‘truly low risk’ patients with atrial fibrillation who do not even need antithrombotic therapy, whilst those with one or more stroke risk factors can be treated with oral anticoagulation, whether as well-controlled warfarin or one or the new agents.  Recent large cohort studies have confirmed the fact that by being more inclusive, rather than exclusive, of common stroke risk factors in the assessmment of the risk for stroke and thromboembolism in atrial fibrillation patients, we can do much better in optimising thromboprophylaxis in reducing stroke and mortality.

Atrial Fibrillation

    Atrial fibrillation (AF) increases the risk of stroke and thromboembolism 5-fold. However we know this risk is not homogeneous: it is altered by the presence of other risk factors. Two systematic reviews (from the Stroke in AF Working Group and the UK National Institute for Health and Clinical Evidence (NICE) guidelines) have summarised the published evidence for risk factors of stroke, largely based on non-warfarin arms of clinical trials and epidemiological cohorts (1,2). Most commonly cited is the Stroke in AF Working Group analysis which (1) identified the following:

1) previous stroke/Transient ischemic attack (adjusted relative risk (RR) 2.5),
2) age (RR 1.5/decade),
3) hypertension (RR 2.0),
4) diabetes (RR 1.8) and 
5) female gender (RR 1.6), as significant risk factors. 

History of heart failure was not considered a significant risk factor, although the presence of moderate-systolic left ventricular dysfunction is considered an independent predictor of thromboembolism. It is worth remembering that some risk factors have not been systematically looked for and/or recorded – and there have been inconsistencies in definition - in various clinical trials.

Stroke risk factors have been studied in large cohort studies as well. In one recent nationwide cohort study (Taiwan), risk factors on multivariate analysis for ischemic stroke in AF patients were:
1) age (where stroke risk increased from age ≥65; OR=1.338 for age 65-74 years vs. age 20-64 years, P=0.014; OR=1.652 for age ≥75 years vs. age <65years, P<0.001),
2) hypertension (OR=2.656, P<0.001),
3) diabetes mellitus (OR=1.341, P=0.005),
4) heart failure (OR=1.611, P<0.001),
5) previous ischemic stroke or transient ischemic accident (TIA) (OR=2.752, P<0.001)
6) peripheral arterial disease (PAD) (OR=1.814, P=0.006) (3).

In another large cohort study (Denmark),the presence of vascular disease also increased the risk of thromboembolism significantly at 5 and 10 years’ followup, with hazard ratios of 2.04 and 2.22, respectively (4). In the Danish Diet, Cancer and Health (DCH) cohort study, vascular disease (prior MI and PAD) was an independent risk factor for the primary endpoint of 'stroke or death' in patients with AF, even after adjustment for the commonly used CHADS2 (Congestive heart failure, Hypertension, Age >75, Diabetes mellitus, and prior Stroke or transient ischemic attack) risk score (5).

Stroke risk stratification schema

The CHADS2 score (Congestive heart failure, Hypertension, Age >75, Diabetes mellitus, and prior Stroke or transient ischemic attack) is the most commonly used risk score for stroke in AF, and was derived by amalgamation of the AF Investigators and SPAF-1 risk schema (both trial-based risk stratification schema) (6). The pros and cons of the CHADS2 score have been recently debated (7). The new European Society of Cardiology guidelines (8) have recommended use of the ‘CHA2DS2-VASc’ score to complement the initial assessment with the CHADS2 score.  The ‘CHA2DS2-VASc’ score is more inclusive of common stroke risk factors seen in everyday clinical practice.

The first derivation and validation of the CHA2DS2-VASc score was in an European cohort from the EuroHeart survey on AF (9), which had various limitations (including a proportion of patients lost to followup). However, other validations of the CHA2DS2-VASc score have been published since. For example, a nationwide cohort study of 73,538 hospitalized non-anticoagulated patients with AF in Denmark reported that in ‘low risk’ subjects (CHA2DS2-VASc score=0), the 1 year rate of thromboembolism per 100 person-years was 0.78 (0.58 to 1.04), in contrast to an event rate with the CHADS2 score of 1.67 (95%CI 1.47 to 1.89) (4). The c-statistics (a statistical measure of the predictive value of a risk score) at 10 years follow-up were 0.812 (95%CI 0.796 to 0.827) with CHADS2 and 0.888 (95%CI 0.875 to 0.900) with CHA2DS2-VASc, respectively.

These data are consistent with another recent study investigating how AF burden improved clinical stroke risk assessment, which reported that the c statistics of the CHADS2 and CHA2DS2 VASc for predicting thromboembolism were 0.653 (95% CI 0.50–0.81) and 0.898 (95% CI 0.84–0.96), respectively – and the c statistic for the CHADS2 score was improved by the addition of AF burden data (5). Another large validation study was performed in a cohort of 79884 AF patients aged ≥18 years in the UK General Practice Research Database, who were followed for an average of 4 years (11).  Again, low-risk subjects (CHA2DS2-VASc score=0) were truly low risk (with annual stroke events <0.5%) with the CHA2DS2-VASc score. In a trial-based anticoagulated AF cohort (n = 7329 subjects) (12), the CHA2DS2-VASc scheme correctly identified the greatest proportion of AF patients at high risk, and the negative predictive value (ie, the percent categorised as "not high risk" actually being free from thromboembolism) for CHA2DS2-VASc was 99.5%.   Poli et al (13) also reported a "real world" of 662 consecutive elderly anticoagulated AF patients, where the CHADS2 and CHA2DS2-VASc schemes had the best c-statistics (0.717 and 0.724, respectively).   


In summary, those categorised as ‘low risk’ using CHA2DS2-VASc as recommended in the ESC guidelines were ‘truly low risk’ for thromboembolism, and the CHA2DS2-VASc scheme performs as good as – and possibly better - than the CHADS2 score in predicting those at ‘high risk’. 


1. Stroke Risk in Atrial Fibrillation Working Group. Independent predictors of stroke in patients with atrial fibrillation: a systematic review. Neurology. 2007;69(6):546-54. 2. Hughes M, Lip GY. Stroke and thromboembolism in atrial fibrillation: a systematic review of stroke risk factors, risk stratification schema and cost effectiveness data. Thromb Haemost 2008; 99:295-304.
3. Lin LY, Lee CH, Yu CC, Tsai CT, Lai LP, Hwang JJ, Chen PC, Lin JL. Risk factors and incidence of ischemic stroke in Taiwanese with nonvalvular atrial fibrillation. A nationwide database analysis. Atherosclerosis 2011 doi:10.1016/j.atherosclerosis.2011.03.033
4. Olesen J, Lip GYH, Hansen ML, et al Validation of risk stratification schemes for predicting stroke and thromboembolism in patients with atrial fibrillation: A nationwide cohort study. BMJ 2011; doi=10.1136/bmj.d124 role=elps
5. Rasmussen LH, Larsen TB, Due KM, Tjønneland A, Overvad K, Lip GY. Impact of vascular disease in predicting stroke, and death in patients with atrial fibrillation: The Danish Diet, Cancer and Health (DCH) cohort study. J Thromb Haemost. 2011 Apr 19. doi: 10.1111/j.1538-7836.2011.04308.x
6. 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
7. Karthikeyan G, Eikelboom JW. The CHADS2 score for stroke risk stratification in atrial fibrillation--friend or foe? Thromb Haemost. 2010 Jul 5;104(1):45-8.
8. Camm AJ, Kirchhof P, Lip GY, Schotten U, Savelieva I, Ernst S, Van Gelder IC, Al-Attar N, Hindricks G, Prendergast B, Heidbuchel H, Alfieri O, Angelini A, Atar D, Colonna P, De Caterina R, De Sutter J, Goette A, Gorenek B, Heldal M, Hohloser SH, Kolh P, Le Heuzey JY, Ponikowski P, Rutten FH. Guidelines for the management of atrial fibrillation: the Task Force for the Management of Atrial Fibrillation of the European Society of Cardiology (ESC). Eur Heart J. 2010 Oct;31(19):2369-429.
9. Lip GY, Nieuwlaat R, Pisters R, Lane DA, Crijns HJ. 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; 137:263-72. 10. Boriani G, Botto G, Padeletti L, Santini M, Capucci A, Gulizia M, Ricci R, Biffi M, De Santi T, Corbucci G, Lip GYH. Improving stroke risk stratification using the CHADS2 and CHA2DS2-VASc risk scores in paroxysmal atrial fibrillation patients by continuous arrhythmia burden monitoring.  Stroke. 2011 Jun;42(6):1768-1770.
11. Van Staa TP, Setakis E, Di Tanna GL, Lane DA, Lip GY. A comparison of risk stratification schema for stroke in 79884 atrial fibrillation patients in general practice. J Thromb Haemost. 2011 Jan;9(1):39-48.
12. Lip GY, Frison L, Halperin JL, Lane DA. Identifying Patients at High Risk for Stroke Despite Anticoagulation. A Comparison of Contemporary Stroke Risk Stratification Schemes in an Anticoagulated Atrial Fibrillation Cohort. Stroke. 2010 Dec;41(12):2731-8.
13. Poli D, Lip GY, Antonucci E, Grifoni E, Lane D. Stroke Risk Stratification in a "Real-World" Elderly Anticoagulated Atrial Fibrillation Population. J Cardiovasc Electrophysiol 2010; Epub ahead of print.


Vol9 N°34

Notes to editor

Corresponding Author:
Professor GYH Lip MD
Professor of Cardiovascular Medicine
University of Birmingham Centre for Cardiovascular Sciences,
City Hospital,
Birmingham, B18 7QH, United Kingdom
Tel:  +44 121 507 5080; Fax:  +44 121 507 5907; e-mail:

The content of this article reflects the personal opinion of the author/s and is not necessarily the official position of the European Society of Cardiology.