Risk Prediction in Atrial Fibrillation
Risk Prediction in Atrial Fibrillation
Abstract & Commentary
By John P. DiMarco, MD, PhD, and Michael Crawford, MD Dr. DiMarco is Professor of Medicine, Division of Cardiology, University of Virginia, Charlottesville Dr. DiMarco is a consultant for Novartis, and does research for Medtronic and Guidant.
Source: Fang MC, et al. Comparison of risk stratification schemes to predict thromboembolism in people with nonval- vular atrial fibrillation. J Am Coll Cardiol. 2008;51:810-815.
In this paper, fang and colleagues compared five different risk stratification schemes as predictors of thromboembolism in patients with nonvalvular atrial fibrillation. The risk schemes studied were those of the Atrial Fibrillation Investigators, the Stroke Prevention in Atrial Fibrillation Investigators, the CHADS2 Score, the Framingham Heart Study risk score, and the Seventh American College of Chest Physicians (ACCP) risk score. Although these clinical scoring systems have many similar characteristics, several differences may lead to different classifications in individual patients.
In this study, Fang et al used the ATRIA (AnTicoagulation and Risk Factors In Atrial Fibrillation) study cohort of 13,559 adults with diagnosed nonvalvular atrial fibrillation to test the accuracy of the risk scoring systems. The analysis was restricted to 10,932 patients who were not taking warfarin at any point in time during the study period. Each of the five risk stratification schemes was used to assign patients to low, intermediate, and high-risk groups using data from the available electronic records in a large HMO based in California. Some specific variables included in the Framingham score could only be estimated from the database. Thromboembolic events were identified from electronic hospitalization and billing records, with confirmation after review of the primary data by an outcomes committee.
There were 32,721 patient-years of follow-up available for analysis. The mean age of the patients at baseline was 72 years, and 78.7% had at least one clinical risk factor for thromboembolism. The most common risk factors were age 75 years or older (46%), hypertension (50.5%), diagnosed heart failure (28.8%), and diabetes mellitus (16.5%). The five risk schemes showed significantly different results in classifying patients. The low-risk group ranged from 11.7% of the cohort using the 7th ACCP guidelines to 37.1% using the Framingham risk score. The 7th ACCP risk scheme classified 80.4% of patients as high risk in contrast to only 16.4% in the Framingham risk scheme. As expected, the thromboembolic event rates in individual risk categories varied depending upon the risk stratification schemes used, but a measure of the ability of all the risk schemes to predict outcome were relatively weak. All five risk schemes had roughly comparable receiver-operating characteristic curves, and the discriminating ability for all of the schemes was only moderately better than the 45o line of no information.
Fang et al conclude that the five available risk schemes studied had only a modest ability to predict thromboembolism in patients with nonvalvular atrial fibrillation. Fang et al argue that development of improved risk schemes in the future will help physicians select patients in whom the risk of long-term anticoagulation can be justified.
Dr. Dimarco's Commentary
Prevention of thromboembolic events is a major goal when treating patients with atrial fibrillation. Unfortunately, warfarin anticoagulation is the most effective established therapy, but treatment with warfarin is frequently complicated by major and minor bleeding. In large studies, the rate of major bleeding has been as high as 5-10% when warfarin is first started, and then the risk continues at a rate of about 2% per year. Since the risk:benefit ratio for anticoagulation may be high, several groups of investigators have proposed scoring systems to assist clinical decision making. In this paper, Fang et al from the ATRIA study examine the value of five scoring systems that are available in the literature. None of them are clearly superior to the others, and all have a relatively low positive predictive value in their middle range. This should not be surprising since the annual rates for stroke and systemic embolic are below 10-15%, even in high-risk groups. For that reason, I personally favor the CHADS2 system since it is very easy to use to estimate risk during clinical practice. More complicated systems are unlikely to provide a significant improvement in their estimations.
Dr. Crawford's Commentary
Interestingly, none of the five schemes were superior to the others in predicting TE. However, a low-risk group with annual TE rates of < 1.0% can be identified across the groups: age < 75 years and no risk factors, or only a history of hypertension. Once you have any risk factors such as heart failure, age > 75 years, hypertension, diabetes, or previous stroke, your risk is between 1.0-2.5% per year. Such patients could benefit from warfarin if the risk of anticoagulation is low. Patients with multiple risk factors have a risk > 2.5%, and should get warfarin if at all possible. So if you remember this information, you don't have to remember any of the schemes, some of which involve adding up points (eg, CHADS2 and Framingham). If in doubt, always err on the side of prescribing warfarin since its benefits have been clearly shown. Also, document your thinking in the record which ever way you go.
There are some limitations to this analysis. They did not know who was on aspirin, since it was not in their database. Perhaps widespread aspirin use is the reason that the highest TE rates observed (3.9%) are much lower than previous studies (6%). Of course, anyone not treated with warfarin should be put on aspirin, if feasible. Clearly, we need a more discriminatory risk scheme, or a better oral anticoagulant, or both.
In this paper, fang and colleagues compared five different risk stratification schemes as predictors of thromboembolism in patients with nonvalvular atrial fibrillation.Subscribe Now for Access
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