We validated a risk function for the prediction of incident AF, which was originally developed in the middle-aged to elderly Framingham cohort of white Americans, in two large independent studies from the US and Europe. The risk algorithm worked reasonably well for 5-year risk prediction after calibration for the underlying event rates. We were able to extend these findings to AA. The hazard ratios for specific risk factors were comparable across cohorts. Discrimination of the Framingham AF risk model was consistent across groups and calibration was satisfactory after adjustment. The risk algorithm may thus provide a tool applicable across a broad range of individuals at risk for AF.
The AF incidence observed across studies showed differences that may be explained by several factors. First, the age structure varied across cohorts, with FHS being the youngest cohort. In secondary analyses we investigated whether the relation of age with incident AF deviates from linearity, but failed to discover non-linear associations over the age range (45–95 years) examined. Second, the years during which AF was ascertained differed by cohort: AF was ascertained between the 1960s and early 1990s in FHS, but during 1989–2008 in CHS and AGES. There may be secular trends in the diagnosis and coding of AF that favor increased recognition of this arrhythmia in more recent years. Third, CHS and Framingham had more vigorous ascertainment of AF cases than AGES, which relied on hospital discharge diagnoses, perhaps leading to greater misclassification of AF cases. Finally, we demonstrated lower AF incidence in AA compared with their white CHS counterparts of the same age distribution, a finding that is in accordance with prior observations of lower AF prevalence in AA.15,16
Risk factors in relation to incident AF
Previous replication attempts of Framingham risk scores for coronary heart disease events revealed good reproducibility, both in similarly-structured as well as less-comparable cohorts and different ethnic groups.12,17,18
Due to differing cohort characteristics and baseline event rates in other samples, recalibration is usually necessary to achieve better model fit, as was observed in the current analysis. The age distribution in FHS and the other two cohorts also provides a likely explanation for the difference in discrimination observed across cohorts. Recalibration and adjustment for baseline survival in the respective cohorts is another way of accounting for differences in baseline characteristics of the samples. In secondary analyses, we examined whether a more parsimonious model without the interaction terms for age and sex would simplify the risk function. The elimination of these additional terms did not change the discrimination ability of the model, as was expected,12
but reduced the calibration performance. For this reason, we recommend leaving the age-squared and interaction terms in the algorithm. Overall, the algorithm performed well with good calibration and discrimination underlining the central role of risk factors such as age, sex, elevated blood pressure, and heart failure.16,19,20
We were able to confirm the role of electrocardiographic PR-interval as an AF risk factor. Atrial conduction defects have been suggested to constitute precursors of a reduced threshold for AF21,22
and the knowledge on abnormalities in atrial electrical activity may help to better understand the pathophysiology of imminent AF.23
Important from the perspective of primary prevention is that risk factors such as body mass index, high blood pressure, and heart failure are modifiable or treatable and thus accessible to intervention. They may thus provide direct targets for prevention of AF or, at least, the delay of disease onset.
Although not all risk factors reached statistical significance in age- and sex-adjusted models due to a small sample size and resulting wide confidence intervals, the point estimates for the hazard ratios in AA were similar to those in whites. The risk algorithm performed similarly in both races. The distribution of risk factors for AF in AA was similar or even higher for unfavorable risk factors compared to whites, confirming earlier reports.15,24
For example, hypertension as one of the major predictors of AF in whites was more frequent in AA15,24
and revealed hazard ratios for AF comparable to whites as shown by our data but did not translate into a higher AF incidence.
Regarding the risk factor associations and distribution, our data suggest differences in incidence rate of AF in AA rather than a completely different set of variables that account for AF risk. However, additional factors may be responsible for differences of AF risk between races and need to be identified and evaluated. Genetic association studies, for example, may show whether there is a genetically-determined predisposition to AF beyond classical AF risk factors.
The comparable strength of risk factors in different ethnicities emphasizes their central importance and potential direct role in the disease process. Similar risk factors in both sexes and different ethnicities may facilitate risk communication and the development of uniform concepts of prevention. Similar to other risk algorithms, the risk score may help to identify individuals at high risk for AF and at the same time provide a starting-point for active prevention since some of the clinical risk factors included in the algorithm are modifiable. Whether the risk function can be applied effectively for the identification of participants for clinical intervention trials needs to be examined.
Strengths and Limitations
We acknowledge several limitations to our study. Observed differences in AF incidence beyond different age ranges and real incidence differences in the cohorts may be due to secular trends in the diagnosis and coding of AF and to differences in AF adjudication and intensity of collection of follow-up data. The performance of the risk algorithm indicates that the risk function seems to be robust against minor systematic misclassifications and real sample-specific differences.
Unfortunately, information on valvular heart disease, one of the strongest risk factors for atrial fibrillation, was not available in AGES and in most of the CHS AA. Reliance on physical examination for heart murmur (versus echocardiography) may have led to misclassification of valvular heart disease in both CHS whites and FHS. Whereas in FHS significant valvular heart disease was considered in a graded fashion, heart murmur was classified as present-versus-absent in CHS. The different classification reduces the comparability of the two studies, as is evident in the different prevalence and smaller hazard ratio related to cardiac murmur in CHS. Severe valvular heart disease is uncommon (<5% prevalence) in the community. Although its diagnosis is associated with a high relative risk, the population attributable risk is low, which may help to explain why the risk algorithm achieved similar accuracy to the FHS function in the replication samples even without the valvular heart disease variable. Similarly, the definition of heart failure and thus the baseline prevalence differed between cohorts. Rigorously-adjudicated heart failure events in CHS and Framingham compared with hospital discharge diagnoses in AGES may have led to somewhat different relations between heart failure and AF.9
Again, at the community level, the prevalence of heart failure was low and the slightly different definitions did not impair discrimination and calibration markedly. Overall, only the prospective application of the risk algorithm and the development of effective strategies to prevent AF will provide support for the utility of the risk function.
The utility of a risk prediction algorithm ultimately depends on several factors, including whether or not: 1) the algorithm accurately classifies individual risk; 2) effective preventive therapies for AF are available; and 3) targeting preventive therapies to level of risk improves outcome in a cost-effective way. The present study is an effort to develop a robust transportable prediction instrument. Prior to demonstrating improved outcomes, the risk prediction instrument may be useful to identify high-risk individuals for primary prevention trials, or as a screen to identify whether putative biological or genetic markers aid in risk stratification over and above easily assessed clinical factors.
We have demonstrated that an individual’s absolute AF risk can reliably be assessed in independent, community-based samples of different age structure and ethnic background based on easily-accessible clinical variables. It needs to be shown whether the application of the risk algorithm and the knowledge of the relative importance of the potentially modifiable risk factors can reduce the number of incident AF cases.