Wang TJ, Massaro JM, Levy D, Vasan RS, Wolf PA, D'Agostino RB, Larson MG, Kannel WB, Benjamin EJ. A Risk Score for Predicting Stroke or Death in Individuals With New-Onset Atrial Fibrillation in the CommunityThe Framingham Heart Study. JAMA. 2003;290(8):1049–1056. doi:10.1001/jama.290.8.1049
Author Affiliations: Framingham Heart Study, Framingham (Drs Wang, Massaro, Levy, Vasan, Wolf, D'Agostino, Larson, Kannel, and Benjamin); Cardiology Division, Massachusetts General Hospital, Harvard Medical School (Dr Wang); Divisions of Cardiology and Epidemiology, Beth Israel Deaconess Hospital, Harvard Medical School (Dr Levy); Department of Mathematics, Boston University (Drs Massaro and D'Agostino), Boston; National Heart, Lung, and Blood Institute, Bethesda, Md (Dr Levy); and Department of Neurology (Dr Wolf), Division of Cardiology (Drs Levy, Vasan, and Benjamin), and Department of Preventive Medicine (Drs Levy, D'Agostino, Wolf, Kannel, and Benjamin), Boston University School of Medicine, Boston.
Context Prior risk stratification schemes for atrial fibrillation (AF) have
been based on randomized trial cohorts or Medicare administrative databases,
have included patients with established AF, and have focused on stroke as
the principal outcome.
Objective To derive risk scores for stroke alone and stroke or death in community-based
individuals with new-onset AF.
Design, Setting, and Participants Prospective, community-based, observational cohort in Framingham, Mass.
We identified 868 participants with new-onset AF, 705 of whom were not treated
with warfarin at baseline. Risk scores for stroke (ischemic or hemorrhagic)
and stroke or death were developed with censoring when warfarin initiation
occurred during follow-up. Event rates were examined in low-risk individuals,
as defined by the risk score and 4 previously published risk schemes.
Main Outcome Measures Stroke and the combination of stroke or death.
Results During a mean follow-up of 4.0 years free of warfarin use, stroke alone
occurred in 83 participants and stroke or death occurred in 382 participants.
A risk score for stroke was derived that included the following risk predictors:
advancing age, female sex, increasing systolic blood pressure, prior stroke
or transient ischemic attack, and diabetes. With the risk score, 14.3% of
the cohort had a predicted 5-year stroke rate ≤7.5% (average annual rate
≤1.5%), and 30.6% of the cohort had a predicted 5-year stroke rate ≤10%
(average annual rate ≤2%). Actual stroke rates in these low-risk groups
were 1.1 and 1.5 per 100 person-years, respectively. Previous risk schemes
classified 6.4% to 17.3% of subjects as low risk, with actual stroke rates
of 0.9 to 2.3 per 100 person-years. A risk score for stroke or death is also
Conclusion These risk scores can be used to estimate the absolute risk of an adverse
event in individuals with AF, which may be helpful in counseling patients
and making treatment decisions.
Atrial fibrillation (AF) is the most common cardiac rhythm disturbance,
affecting more than 2 million individuals in the United States.1 As
the population ages and the prevalence of cardiovascular disease increases,
the prevalence of this arrhythmia is expected to rise.2 Much
of the morbidity associated with AF is attributable to a 5- to 6-fold increased
risk of stroke.3 Because this stroke risk is
variable, numerous studies have attempted to define clinical criteria that
may be used to classify participants with AF as being at low or high risk.4- 8 Risk
stratification may aid in prognostication and in the selection of appropriate
candidates for therapies such as warfarin.4,9
The 2 best known risk stratification schemes for AF are based on follow-up
of randomized trial cohorts.4- 6 Participants
in trials are slightly younger, more likely to be men, and generally have
fewer comorbidities than those in the community with AF4,10,11 although
the rates of stroke and major bleeding with anticoagulation may be similar.10 Recently, Gage and colleagues7 proposed
an alternate stroke risk scheme for AF (CHADS2), based on a combination
of risk factors identified in earlier prediction algorithms. They tested this
scheme using Medicare claims data from patients who were hospitalized for
AF but did not receive anticoagulation therapy. A potential limitation of
this approach is selection bias, because clinical features associated with
nonuse of warfarin or hospitalization for AF are likely to influence stroke
risk. Also, some strokes may be missed by hospital discharge data if they
are small, immediately lethal, or improperly coded. Previous risk stratification
scores were also based on patients with new-onset or established AF. Arguably
clinicians are particularly interested in risk stratifying patients with newly
Management strategies should take into account a patient's absolute
risk of having an adverse outcome, particularly when therapies have potential
toxic effects.12 Accordingly, it would be useful
to have a scheme for predicting the absolute risk of an adverse event in a
patient with AF. Although existing risk schemes provide absolute event rates
for 3 to 7 levels of risk, it may be beneficial to have more specific estimates
of risk. Additionally, available risk scores do not include mortality as an
end point although studies have indicated that AF is an independent risk factor
for death as well as stroke13- 15 and
therapies for AF may affect mortality.4 Our
objective was to derive a clinical risk score for patients with AF in the
community, focusing on 2 outcomes: stroke alone and stroke or death.
The design of the Framingham Heart Study has been described previously.16,17 The original cohort was recruited
in 1948; members have been followed up biennially since then. The offspring
cohort was initiated in 1971 with the recruitment of offspring (and their
spouses) of the original Framingham cohort. Participants in the offspring
cohort have been examined approximately every 4 years.
Participants in the original and offspring cohorts aged 55 to 94 years
at the time of AF diagnosis were eligible for this study (n = 1216). We excluded
individuals for the following reasons: AF prior to the first Framingham examination
in the offspring cohort (n = 1) or prior to 1960 in the original cohort (n
= 23); missing covariate data (n = 160); stroke, transient ischemic attack
(TIA), or death within 30 days of AF diagnosis (n = 153); and rheumatic mitral
stenosis (n = 11). We excluded participants with an event in the first 30
days following AF because we thought that a risk score would have less clinical
relevance for individuals with a very short life expectancy. Of the remaining
868 eligible individuals with new-onset AF, 705 were not treated with warfarin
at baseline and were used in the derivation of the risk scores.
At each Framingham Study clinic examination, participants underwent
a medical history, physical examination, and electrocardiogram. If a participant
saw a physician or was admitted to the hospital between Framingham examinations
for symptoms that could be related to AF or another cardiovascular event,
the records and electrocardiograms from that visit were obtained. The diagnosis
of AF was made if AF or atrial flutter was present on an electrocardiogram
obtained from the Framingham clinic visit, hospital charts, or physician office
record. The electrocardiographic interpretation was confirmed by 1 of 2 Framingham
Heart Study cardiologists.
Baseline risk factor data were derived from the examination cycle prior
to and closest to the onset of AF. Systolic and diastolic blood pressure values
were the means of 2 physician-obtained measurements. Diabetes was defined
by history of a fasting glucose of at least 140 mg/dL (7.8 mmol/L), a random
glucose of at least 200 mg/dL (11.1 mmol/L), or use of insulin or hypoglycemic
medications. Persons were classified as current cigarette smokers if they
reported having smoked cigarettes during the previous year. Because echocardiographic
data were unavailable for most subjects, valvular heart disease was defined
clinically by the presence of at least a grade 3 out of 6 systolic murmur,
or any diastolic murmur. Electrocardiographic left ventricular hypertrophy
(ECG LVH) was determined by the presence of voltage criteria accompanied by
lateral repolarization abnormalities.18 Warfarin
and aspirin use was determined by self-report at routine Framingham examinations
and by a review of outside medical records.
A panel of 3 Framingham investigators, including a neurologist, adjudicated
the diagnosis of stroke or TIA, based on a review of all relevant medical
records and Framingham clinical data. In addition, a study neurologist examined
most subjects with a suspected cerebrovascular event. For analytic purposes,
previous stroke or TIA was considered as a potential risk factor, but subsequent
stroke (not TIA, n = 21) was considered the outcome event. A prior stroke
or TIA was defined as a stroke or TIA occurring prior to the first documented
onset of AF. Participants with a stroke or TIA diagnosed on the same day as
AF were excluded because we treated these events as occurring within the first
30 days after AF onset.
The primary outcomes were stroke alone and stroke or death. We used
Cox proportional hazards models to assess predictors for developing each outcome.
The proportional hazards assumption was confirmed by examining log-log survival
plots and by comparing the regression coefficients from models censored at
2, 5, and 10 years. The beginning of the follow-up period was the date of
the AF diagnosis. Follow-up after 10 years was censored. Continuous variables
for age and systolic blood pressure were forced into the multivariable models
given the importance of these characteristics in prior studies of stroke after
AF.4,6,19 The following
additional variables were entered into the Cox models in stepwise fashion,
using a threshold of P<.10 as the criterion for
inclusion: use of antihypertensive medication, prior myocardial infarction
(MI) or congestive heart failure (CHF), prior stroke or TIA, current smoking,
ECG LVH, diabetes, and clinical valvular heart disease (except for mitral
stenosis, which was an exclusion criterion).
Analyses were performed before and after the exclusion of subjects taking
warfarin. In models excluding warfarin users, individuals taking warfarin
were censored when warfarin use was first recorded (at baseline or during
follow-up). The warfarin-censored analyses were used as the primary models
in the development of the risk scores and calculation of event rates. In secondary
analyses, we estimated models excluding those with atrial flutter and those
with prior stroke or TIA. We also tested for interactions between age, sex,
warfarin use, aspirin use, and cohort status (original vs offspring cohort)
and each of the other risk factors. We also constructed models in which warfarin
use was considered as a time-dependent covariate.
A risk scoring system for each outcome was developed based on the warfarin-censored
Cox models, using previously established methods.20,21 Briefly,
points were assigned to each risk factor according to the product of the corresponding β
coefficient and the value of the risk factor. Fractional values were converted
to integer values by dividing by a constant. For each possible score, a linear
function was computed and corrected for the means of the risk factors in the
cohort. The result was inserted into a survival function, using a 5-year baseline
hazard at the means of the risk factors to produce an estimate of 5-year risk.
To assess calibration, the agreement between
predicted outcomes and actual outcomes, we compared predicted and observed
5-year event rates for Framingham participants in different quintiles of predicted
risk. Differences between predicted and observed event rates were used to
calculate a Hosmer-Lemeshow statistic.22 To
assess discrimination, we calculated a c statistic for each risk scheme.22 Analogous
to the area under the receiver operating characteristic curve, the c statistic corresponds to the probability that a scoring system correctly
ranks 2 randomly selected observations with respect to an outcome of interest.23 Values for the c statistic
range from 0.5 (noninformative test) to 1.0 (perfect test discrimination).
We performed an internal validation of the c statistics
using a bootstrap analysis in which the cohort was resampled 1000 times with
We also evaluated whether the risk score had the ability to identify
low-risk participants, using the following thresholds of 5-year stroke risk:
10% (average annual rate 2%), 7.5% (average annual rate 1.5%), and 5% (average
annual rate 1%). We determined the proportion of participants with predicted
stroke rates at or below each threshold and the actual event rates in each
group. Event rates were also reported for subjects in the lowest risk strata
for existing risk schemes developed by the Atrial Fibrillation Investigators
(AFI),4 the Stroke Prevention in AF (SPAF)
investigators,5,6 Gage et al (CHADS2),7 and van Walraven et al.8 For these comparisons, we used the same variable definitions
that were used in the original studies,4- 8 except
that we substituted a history of CHF for recent CHF (CHADS2, SPAF).
Analyses were performed using SAS version 8.1 (SAS Institute Inc, Cary, NC).
Characteristics of participants who were alive and stroke-free 30 days
after the onset of AF are shown in Table
1. During a mean follow-up of 4.3 years (range, 30 days-10 years),
stroke occurred in 111 subjects (13%), and stroke or death in 485 subjects
(56%). After follow-up was censored for warfarin use, there were 83 strokes
and 382 stroke or death events during a mean follow-up of 4.0 years. Crude
incidence rates were 2.9 per 100 person-years for stroke (95% confidence interval
[CI], 2.3-3.5) and 13.4 per 100 person-years for stroke or death (95% CI,
12.5-14.3), after censoring for warfarin use.
Results of the Cox proportional hazards analyses for stroke and stroke
or death are shown in Table 2.
After warfarin censoring and adjustment for age and systolic blood pressure,
the following predictors entered stepwise models for stroke: female sex, diabetes,
and prior stroke or TIA. The following predictors entered the model for the
combined outcome of stroke or death: smoking, prior MI or CHF, diabetes, heart
murmur, and ECG LVH. For both outcomes, there was no significant interaction
between warfarin or aspirin use and any of the predictors. For the overall
analyses, model coefficients were essentially unchanged when warfarin was
considered as a time-dependent covariate. There was also no significant effect
modification by age, sex, or cohort status for any of the predictors.
The risk scores for stroke and stroke or death are shown in Figure 1 and Figure 2. For an individual patient, the probability of stroke can
be estimated by calculating a point score based on risk-factor information.
For instance, a 75-year-old man with a systolic blood pressure of 150 mm Hg
and diabetes receives a score of 12 for predicting stroke: 5 points for age,
0 points for sex, 2 points for systolic blood pressure, 5 points for diabetes,
and 0 points for prior stroke or TIA. This score corresponds to a predicted
5-year stroke risk of 16%. The probability of stroke or death can be estimated
in a similar manner, using the point score shown in Figure 2. The incremental influence of selected characteristics
on the risk of stroke or death for 60- and 70-year-old men and women is depicted
in Figure 3.
A computerized spreadsheet that allows for individual risk prediction
for stroke and stroke or death can be downloaded for use at http://www.nhlbi.nih.gov/about/framingham/stroke.htm.
Ranking participants into quintiles according to their stroke-risk score
yielded predicted 5-year stroke rates of 7% (lowest quintile), 10%, 14%, 20%,
and 33% (highest quintile). These predicted rates corresponded closely with
actual 5-year stroke rates in each quintile: 8%, 9%, 13%, 20%, and 29%. The
stroke-risk score and stroke or death–risk score had Hosmer-Lemeshow
statistics of 7.6 and 6.5, respectively; values of 20 or less indicate good
calibration. The c statistics were 0.66 for stroke
and 0.70 for stroke or death. Mean bootstrap validated c statistics (SD) were 0.66 (0.03) for stroke and 0.70 (0.01) for stroke
or death. The c statistics for other stroke risk
schemes tested in our sample were 0.62 (CHADS2), 0.62 (SPAF), and
The accuracy of the stroke score was similar among aspirin users (n
= 156; c statistic 0.67; Hosmer-Lemeshow statistic,
14.1) and nonaspirin users (n = 549, with censoring at the initiation of aspirin
therapy; c statistic, 0.64; Hosmer-Lemeshow statistic,
5.1). Results were also similar in analyses excluding participants with atrial
flutter (n = 91) or prior stroke or TIA (n = 102).
The ability of the different schemes to identify low-risk persons is
presented in Table 3. The proportion
of Framingham participants classified as low risk using the risk score ranged
from 3.3% to 30.6%, depending on the threshold. Fourteen percent of the cohort
had a Framingham risk score of 4 or less, corresponding to a predicted stroke
rate of 7.5% or less (average annual rate, ≤1.5%); these subjects had an
actual stroke rate of 1.1 per 100 person-years. When we used criteria for
the lowest-risk stratum from each of the other risk schemes, 6.4% (AFI) to
17.3% (SPAF) were classified as low risk, and these participants had actual
stroke rates ranging from 0.9 to 2.3 per 100 person-years.
We examined the predictors of stroke alone and stroke or death among
individuals with new-onset AF and derived clinical risk scores for these outcomes.
These risk scores can be used to estimate the absolute risk of an adverse
event in individuals diagnosed with AF, which may be helpful in counseling
patients and in making treatment decisions. Our data indicate that although
AF is associated with a high overall risk of stroke or death, risk factors
can be used to easily stratify patients at particularly high or low risk.
Several prior studies have examined risk factors for stroke in clinical
trial cohorts. The AF Investigators reported that individuals with the following
risk factors were at moderate or high risk of stroke: age 65 years or older,
hypertension, diabetes, or prior stroke or TIA.4 These
results were based on analysis of 81 stroke events obtained from pooling the
control arms of 5 randomized trials. Investigators from SPAF III, which had
36 strokes and 23 TIAs, proposed similar risk factors but did not include
diabetes and confined the age criterion for high-risk subjects to women.5 In addition, the SPAF investigators included CHF or
left ventricular dysfunction as a risk factor. The American College of Chest
Physicians synthesized these data in formulating its 1998 recommendations
for anticoagulation use for patients with AF.24 These
clinical risk factors also have been incorporated into subsequent AF risk
The results of the current study are similar to those of prior studies
with respect to risk factors such as advanced age, diabetes, and elevated
blood pressure. The relative risk associated with prior stroke or TIA in our
study was lower than that observed in other studies. Although chance may have
played a role, it is also important to note that we focused on individuals
with new-onset AF. Thus, the prior strokes in these people may not have been
embolic in origin because the events did not occur in the setting of documented
AF. We did not study individuals with prior stroke occurring in the setting
of AF because there is broad consensus that these individuals are at high
risk of events and should receive anticoagulation if possible.
We also found female sex to be an independent risk factor for stroke
among those with AF. Higher rates of stroke in women compared with men have
been reported in prior observational studies25,26 and
a report from the SPAF investigators.19 Possible
mechanisms for a sex-related difference in stroke with AF have not been fully
elucidated. Sex-related differences in thrombotic tendency27 or
atrial remodeling28 with AF are possibilities
that warrant investigation. Female sex was not significantly associated with
the combined end point of stroke or death, probably because of the higher
risk of mortality from other causes in men.
There are few data regarding the prediction of long-term outcomes among
patients with AF in the community. Existing risk schemes have been derived
from trial cohorts followed up over relatively short periods (<2 years).
Ideally, a scheme for predicting absolute risk would be based on longer follow
up to minimize the influence of acute events that confer short-term risk.
Furthermore, prior studies have not necessarily followed up participants from
the onset of AF. Thus, survival bias and lead-time bias are potential limitations
of these studies and may influence predicted event rates.
Our risk scheme was derived by use of a community-based cohort, a potential
strength because patients with AF in typical practice may resemble individuals
in a community-based cohort more than participants in a randomized trial.
One of our goals was to document the performance of existing risk classification
schemes when applied in a community setting.29 However,
a direct comparison between our risk scheme and other schemes requires an
independent data set because a risk scheme will always perform best in the
cohort from which it was derived.
In evaluating the internal validity of the risk score, we found that
actual event rates were closely correlated with predicted event rates for
participants at different levels of risk.30 A
formal test of this observation, the Hosmer-Lemeshow statistic, indicated
good calibration, both for the overall sample and several subgroups. The c statistics for stroke and stroke or death were lower
than for risk scores in other settings, such as acute coronary syndromes,31 which is probably attributable to the longer follow-up
period covered by the AF score, the competing risk of mortality, and the multifactorial
nature of stroke in older individuals. Additionally, we found c statistics for other stroke schemes that were lower than those reported
in a previous study by Gage et al,7 which may
be attributable to differences in the study samples (hospitalized Medicare
patients with prevalent AF vs community-based individuals with new-onset AF)
and follow-up periods (mean follow-up 1.2 years vs 4.0 years).
An important function of risk prediction schemes for AF is to identify
patients at low risk of stroke. Individuals with an annual stroke risk of
2% or less (approximately 10% over 5 years) may not realize additional benefit
from warfarin compared with aspirin9,32 and
their risk of stroke may not exceed the risk of life-threatening bleeding
with warfarin.33 Schemes with stringent age
and clinical criteria successfully identify individuals with a low risk of
stroke but may miss some older individuals who are also at low risk (Table 3). On the other hand, criteria that
are too liberal have the possibility of labeling too many individuals as being
at low risk of stroke.
A potential advantage of the Framingham scheme over existing risk schemes
is the greater flexibility provided by a point-scoring system because a given
score may be attained by different combinations of patient characteristics.
Additionally, in contrast to prior schemes, the Framingham score allows the
use of different thresholds of risk, an important feature because the amount
of risk that is tolerable may vary according to the clinical situation. However,
further studies are necessary to confirm that this score successfully identifies
low-risk patients in other cohorts.
Several limitations should be acknowledged. Because echocardiograms
were not available during the first 2 decades of this study, we did not include
the results of echocardiography in the risk scores. Of the prior risk schemes,
only SPAF included echocardiography. In that scheme, evidence of left ventricular
dysfunction by echocardiogram could be substituted for clinical CHF.5,6,34 However, given the
association of left ventricular dysfunction with age and cardiovascular disease,
the results of echocardiography often do not change the risk stratification
based on clinical criteria alone.35 Nevertheless,
our risk score may underestimate the stroke risk for individuals with significant
left ventricular systolic dysfunction. The impact of other echocardiographic
findings, including left atrial enlargement and mitral annular calcification,
has been inconsistent.34,36 Existing
guidelines recommend echocardiography on patients with new-onset AF because
it may elucidate the etiology of AF.37
Individuals who were receiving warfarin therapy were censored in the
models used to derive the risk scores. Because warfarin therapy was not assigned
randomly, it is possible that censoring removed some of the highest-risk individuals
from our cohort, resulting in an indication bias. However, results of multivariable
models for stroke were similar before and after censoring and in models including
warfarin as a time-dependent covariate. Additionally, we did not find any
significant interaction between warfarin use and covariates in our models.
Some of our participants were receiving aspirin therapy, which reduces
the risk of thromboembolism in AF.38 Thus,
our event rates may underestimate the risk of stroke for patients not receiving
aspirin therapy. However, it is likely that patients with AF who do not get
warfarin would start aspirin therapy or would be receiving aspirin already
for other cardiovascular indications. Additionally, the risk score performed
similarly in both aspirin and nonaspirin users.
We did not distinguish between paroxysmal and chronic AF or between
AF and atrial flutter. There are some data to suggest that the stroke risk
associated with paroxysmal AF is similar to that associated with chronic AF.39 Although some studies suggest that the stroke risk
in atrial flutter may be lower than in AF, a large proportion of individuals
presenting with atrial flutter have subsequent episodes of documented AF,
and results of the risk prediction model were similar when those with atrial
flutter were excluded.40- 42 We
did not adjust for use of hormone replacement therapy19 because
ascertainment of this therapy was not uniform at all examination cycles. We
also did not examine the influence of noncardiovascular comorbidities, such
as cancer, on our outcomes.
Although it is possible that the different examination intervals in
the Framingham offspring and original cohorts affected our results, the clinical
end points (stroke and death) did not rely on attendance at a Framingham examination.
Also, there was no significant interaction between cohort status and any predictors
in the models. Although participation in an observational cohort study may
lead to earlier diagnoses of AF, 76% of AF diagnoses were based on outside
hospital or physician records.
Finally, it should be noted that the Framingham cohort is overwhelmingly
white. Although the Framingham risk score for coronary heart disease performs
reasonably well in ethnically and geographically diverse cohorts,30 we acknowledge that the same may not be true for
this risk score. Our cohort was also elderly, which reflects the age at which
AF most commonly presents in the community.
Several strengths of the investigation deserve comment as well. The
Framingham Heart Study cohorts have been followed up longitudinally over many
decades, which provides the unique opportunity to study the natural history
of AF prior to the widespread use of anticoagulant therapy. The continuous
surveillance of the cohorts for cardiovascular events, the adjudication of
all stroke diagnoses by a physician panel, and the routine ascertainment of
antecedent cardiovascular risk factors (facilitating multivariable analyses)
are additional strengths of this investigation.
Although AF is an important source of morbidity and mortality in the
community, the risk associated with this disorder is highly variable. Accordingly,
it has become increasingly important for clinicians to be able to risk-stratify
patients with AF. We propose risk scores that enable the prediction of the
risk of stroke or death over a 5-year period in an individual patient at the
time of diagnosis. An understanding of absolute risk is fundamental to many
clinical decisions involving patients with AF, such as the decisions to initiate
anticoagulant therapy or temporarily stop anticoagulation for surgical procedures.7,43 Anticoagulation therapy may not be
justified in individuals with low predicted rates of stroke. Because several
risk schemes for AF now exist, it will be critical to validate this risk prediction
instrument in an independent cohort and to assess its performance relative
to other risk schemes.