Risk Prediction in Cardiovascular Medicine
Cardiovascular Risk Prediction
Basic Concepts, Current Status, and Future Directions
Donald M. Lloyd-Jones, MD, ScM
F
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likelihood of developing or dying of CVD, whereas others
appear destined to live long lives free of CVD. However,
translating this simple observation into a quantitative probability of disease requires a number of factors. First, reliable
data must be available to indicate disease incidence rates.
Second, a number of risk markers must be available to assess
associations with future disease incidence. Statistical methods
must be available to quantify the prospective association
between risk markers and the occurrence of disease. Further
refinement of risk prediction requires adequate statistical
methods for quantifying the potential improvement that 1 risk
prediction model may offer over another. Finally, and importantly, the utility of risk prediction algorithms must be
assessed in the context of the clinical environment, including
considerations of the burden and severity of the disease being
predicted, the availability of safe and effective interventions
to prevent disease, and cost-benefit considerations of applying those therapies to different segments of the population.
Only in the last 5 to 10 years have all of these tools been
available.
Early guidelines from the National High Blood Pressure
Education Program and the National Cholesterol Education
Program focused on those at the highest relative risks for
developing CVD related to markedly elevated blood pressure
and cholesterol levels.7–10 However, physicians and patients
often have difficulty interpreting relative risk estimates.11–13
For example, a relative risk for disease of 10 might seem very
high, but if the incidence rate in the referent group is close to
0, it will also be close to 0 in the group with the relative risk
of 10. Conversely, a relative risk of 1.3 might be very
important when a disease is common. Therefore, researchers
and policy makers have focused on absolute risk estimation to
provide absolute probabilities of developing CVD within a
given time frame. These estimates may be more easily
understood than relative risks, and they allow clinical recommendations for interventions in individuals who exceed
unacceptable risk thresholds.11–13
ew topics have received as much attention in the cardiovascular literature over the last 5 years as risk prediction.
The assessment of risk has been a key element in efforts to
define risk factors for cardiovascular disease (CVD), to
identify novel markers of risk for CVD, to identify and assess
potential targets of therapy, and to enhance the cost-effective
implementation of therapies for both primary and secondary
prevention of CVD. With the publication of the Third Report
of the National Cholesterol Education Program’s Adult Treatment Panel (ATP-III)1 in 2001 and similar guidelines from
other national and international bodies,2–5 risk prediction
assumed a central role in the field of CVD prevention. Since
then, numerous attempts have been made to refine and
improve risk assessment methods. A basic review of the
history and principles of risk prediction, their statistical
underpinnings, and their clinical implications is thus required
to help clinicians and researchers understand the increasingly
complex approaches being undertaken to refine CVD risk
prediction. Other articles in this series address the current
adoption, utility, and effect of risk estimation algorithms in
clinical prevention practice.
Rationale for CVD Risk Prediction
Risk estimates can theoretically be used to raise population
awareness of diseases (such as CVD) that cause a significant
burden of morbidity and mortality, to communicate knowledge
about that risk to individuals and subgroups, and to motivate
adherence to recommended lifestyle changes or therapies. In
clinical practice, risk prediction algorithms have been used most
directly to identify individuals at high risk for developing CVD
in the short term to select those individuals for more intensive
preventive interventions. The prime example of this latter
“high-risk” prevention strategy, which relies heavily on the
quantitative prediction of CVD risk, is the approach promulgated by the ATP-III panel.1 In this algorithm, the stated
underlying assumption is that the intensity of treatment and risk
factor reduction should match the level of absolute predicted
risk.1,6 Therefore, it is useful to understand the current and
potential future roles of quantitative risk prediction in guidelines.
Current CVD Risk Prediction in Practice
Five- and 10-year risk estimates have been widely adopted by
ATP-III1 and other guidelines4,14,15 and are most often based
on multivariable regression equations derived from the Fra-
Historical Perspective
Even with the most rudimentary understanding of CVD, it is
clear that certain individuals appear to have a substantial
From the Department of Preventive Medicine and Bluhm Cardiovascular Institute, Department of Medicine, Northwestern University Feinberg School
of Medicine, Chicago, Ill.
Correspondence to Donald M. Lloyd-Jones, MD, ScM, Department of Preventive Medicine, 680 N Lake Shore Dr, Suite 1400, Chicago, IL 60611.
E-mail dlj@northwestern.edu
(Circulation. 2010;121:1768-1777.)
© 2010 American Heart Association, Inc.
Circulation is available at http://circ.ahajournals.org
DOI: 10.1161/CIRCULATIONAHA.109.849166
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Lloyd-Jones
Table 1.
CVD Risk Prediction
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Examples of Currently Available CVD Risk Prediction Scores
Risk Score
End Point
Comments
All CHD
Includes CHD death, MI, unstable angina, and angina pectoris
Hard CHD
Includes CHD death and nonfatal MI
Framingham global CVD, 2008
Global CVD
Includes CVD death, all CHD, stroke, heart failure, and claudication
PROCAM19
Hard CHD
Includes CHD death and nonfatal MI
CVD
Includes CHD, stroke, and transient ischemic attack
Global CVD
Includes CVD death, MI, stroke, and revascularization
Framingham, 199816
ATP-III risk estimator, 20011,17 (Framingham)
23
QRISK20
Reynolds risk score (women)21
22
Reynolds risk score (men)
Global CVD
Includes CVD death, MI, stroke, and revascularization
SCORE18
CVD death
Includes CVD death only; does not include nonfatal events; multiple region-specific
(northern European, southern European) and country-specific versions available
MI indicates myocardial infarction.
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mingham cohorts in which the levels of traditional risk
factors (age, total cholesterol, high-density-lipoprotein cholesterol, systolic blood pressure, smoking status) are assigned
weights (points) to predict coronary heart disease (CHD)
events separately for men and women.1,16,17 The calculated
risk score is then converted into an absolute probability of
developing CHD within that time frame.
Whereas Framingham-based equations have been the most
widely used for clinical practice guidelines, other scores such
as the European Systematic COronary Risk Evaluation
(SCORE) algorithm18 and the Prospective Cardiovascular
Munster (PROCAM) model,19 among many others,20 –23 have
also been published and disseminated (Table 1). The SCORE
algorithm, derived from European data, has both region-specific
and country-specific versions; however, this risk score system
predicts only fatal cardiovascular events. PROCAM incorporates a larger number of covariates, which may limit clinical
utility somewhat. The concepts discussed in this article are
directly relevant to other risk scores and prevention algorithms
beyond those based on the Framingham risk score (FRS).
In the current ATP-III treatment algorithm,24 all patients
with existing CHD, diabetes mellitus, peripheral arterial
disease, aortic aneurysm, or symptomatic or significant carotid artery disease are categorized as high risk and treated as
if they require secondary prevention of CHD. For those
without CHD or CHD risk equivalents, absolute risk estimates are categorized into discrete risk categories (Table 2) to
guide primary prevention decision making. Individuals with
10-year risks ⬎20% are recommended for immediate lipidlowering drug therapy to reduce their risk. In patients with
intermediate risk (10% to 20%), the recommendation is either
to start drug therapy or to pursue other noninvasive testing for
further risk stratification. Lower-risk subjects are typically
not recommended for drug therapy, but for lifestyle modification as appropriate.
The interval of 10 years for risk estimation was chosen as
the focus for a variety of reasons. Most notably, consideration
of 10-year risk identifies those patients most likely to benefit
from drug therapy in the near term, thus improving costeffectiveness and safety of therapy. In addition, robust data
for estimation of current CVD risks associated with risk
factors in a contemporary environment require a focus on
shorter-term follow-up. The risk for CHD associated with
traditional risk factors is continuous across the population,
and there are no obvious natural thresholds. Nonetheless, the
thresholds used by ATP-III for clinical decision making were
determined from population data and cost-effectiveness estimates in an era when statin medications cost substantially
more than they do currently.1 In addition, optimal prevention
necessitates broader application of lipid-lowering drugs because the majority of events occur in those with average or
only modestly adverse levels of blood lipids (because this is
where the vast majority of the population at risk is found).
Therefore, it seems likely that future guidelines may choose
lower thresholds for therapy in light of demonstrated benefit
of statins in populations at predicted risk ⬍20%, the availability of inexpensive statins, and longer-term safety data.
Interpretation of Absolute Risk Estimates
Ten-year absolute risk estimates may serve not only as a basis
for decision making about institution of lipid-lowering drug
therapy but also as a useful means for risk communication
with patients. However, interpretation of a 10-year risk
estimate must be done appropriately. For a given patient with
a 10-year risk estimate of 7%, it would be inappropriate to
state that that individual’s risk is 7%. Rather, a correct
interpretation would be to say that, given 100 similar individuals, we expect that 7 will experience an event in the next
10 years and 93 will not.
Table 2. Risk Classification Algorithm Used in the ATP-III
2004 Update
Risk Category
High risk
Moderately high risk
Moderate risk
Lower risk
Definition
CHD or CHD risk equivalent* or ⱖ2 risk factors†
and 10-y predicted risk of ⬎20%
ⱖ2 Risk factors and 10-y predicted risk of
10% to 20%
ⱖ2 Risk factors and 10-y predicted risk of ⬍10%
0–1 Risk factor
Table data from Grundy et al.24
*CHD risk equivalents include clinical manifestations of noncoronary forms
of atherosclerotic disease (peripheral arterial disease, abdominal aortic aneurysm, and carotid artery disease, ie, transient ischemic attacks, stroke of
carotid origin, or ⬎50% obstruction of a carotid artery) or diabetes mellitus.
†Risk factors include cigarette smoking, hypertension (blood pressure
ⱖ140/90 mm Hg or on antihypertensive medication), low high-density lipoprotein cholesterol (⬍40 mg/dL), family history of premature CHD (CHD in male
first-degree relative ⬍55 years of age; CHD in female first-degree relative ⬍65
years of age), and age (men ⱖ45 years; women ⱖ55 years).
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The clinician’s job, therefore, is to understand the probabilistic nature of the estimate and to weigh additional factors,
such as obesity or the presence of a family history of
premature CVD, or perhaps to perform further testing that
might modify the assessment and assist in discerning whether
the individual patient is more likely to be among the diseased
or the disease-free group at the end of 10 years. These
additional factors have not been incorporated into many risk
estimation equations because they do not achieve statistical
significance or contribute meaningfully beyond the traditional risk factors in predicting 10-year risk across the entire
population. Nonetheless, they can assist in modifying the
base risk estimate, particularly in intermediate-risk individuals, as noted below. It is also important to recognize that a
“low-risk” risk estimate (eg, ⬍5%) does not mean “no risk,”
particularly in the context of longer time horizons than 10
years. If the patient is expected to live ⬎10 years, his or her
10-year risk estimate will perforce increase over time, and
any adverse levels of risk factors present at younger ages may
cause marked elevations in lifetime risks for CVD.25
A common criticism of risk prediction models is that they
provide risk estimates for populations, not individuals. However, this naïve criticism ignores the fact that much of
currently recommended medical practice and essentially all
evidence-based practice require the application of population
mean effects to individuals. This will continue to be the case
for the foreseeable future (at least for CVD risk estimation)
until the potential promise of “personalized medicine” is
reached, perhaps through refined genetic profiling.
Metrics for Assessing the Utility of Absolute
Risk Prediction Equations
Demonstration that a risk score has a significant statistical
association with the outcome of interest is necessary but
grossly insufficient for evaluating its utility. A number of
metrics are available to assist in the evaluation of the
performance and utility of risk estimation models. In general,
these metrics assess parameters of the risk prediction model
(similar to a diagnostic test), its ability to discriminate future
cases from noncases, the calibration of the model, the model
fit, and the informativeness of the model for the outcome of
interest. Consideration of all of these factors is important to
understanding the utility of a risk score. Newer methods of
assessment involving analysis of risk reclassification also
allow comparison of different risk stratification algorithms
using novel markers or risk scores. Knowledge of a few of
these metrics and concepts will suffice for most clinicians to
interpret the utility of risk prediction models.
The C Statistic
The most widely reported measure of model discrimination
for CVD risk prediction models is the C statistic. The C
statistic is a function of both the sensitivity and specificity of
the model across all of its values, and it represents the ability
of the score to discriminate (future) cases from noncases.
Simply put, the C statistic indicates the probability that a
randomly selected patient who develops the disease (a
“case”) will have a higher risk score than a randomly selected
noncase. Thus, a C statistic of 0.75 for a given model would
indicate that a randomly selected case has a higher score than
a randomly selected noncase 75% of the time. The C statistic
can vary from 1.0 (perfect discrimination) to 0.5 (random
chance, indicating that the score being applied is no better
than flipping a coin). C statistics ⬍0.70 are thought to
indicate inadequate discrimination by current convention,
whereas those between 0.70 and 0.80 are considered acceptable and those between 0.80 and 0.90, excellent.26
The C statistic has received some criticism as a metric for
assessment of CVD risk prediction models. It is fair to say
that reliance on the C statistic alone as a measure of test
performance has significant limitations.27,28 For example, the
C statistic simply indicates whether a risk score is providing
appropriate rank ordering of risk for cases and noncases, not
whether the estimated and observed risks are similar (which
is a function of calibration) or how much greater the estimated risk is between selected cases and noncases.28
A notable property of the C statistic is that extremely large
odds ratios (or relative risks) are required to achieve clinically
meaningful levels or increases in the C statistic. For example,
as demonstrated by Pepe et al,29 for a binary risk marker
considered in isolation, a univariate odds ratio of ⱖ9.0 would
be required for a C statistic that provides excellent discrimination of cases from noncases and similarly for a continuous
risk marker in which the distribution of risk scores differs by
ⱖ2 SDs. These magnitudes of relative risk are very rarely
observed in clinical practice. However, the combination of
multiple, independent risk markers, as in the FRS and
similar scores, does provide these magnitudes of relative
risk; hence, these scores typically have C statistics in the
range of 0.75 to 0.80.
Measures of Calibration
As opposed to measures of discrimination, which indicate the
ability of a risk model to rank order individuals’ risks,
measures of calibration assess the ability of a risk prediction
model to predict accurately the absolute level of risk that is
subsequently observed. For example, demonstrating that a
risk prediction model is well calibrated would require an
observed event rate of close to 7% if the model estimates that
the risk for a certain subgroup of individuals is 7% over 10
years. Calibration is often assessed visually by dividing the
population at risk into quantiles (eg, deciles) of predicted risk
and plotting the predicted risk versus the observed event rate
for each quantile (Figure 1). The statistical metric often used
to test for the calibration of a risk model is the HosmerLemeshow ␹2 test. A value of P⬍0.05 for such a test would
indicate poor calibration of the model for the population.
Note that a test may have poor calibration while still having
reasonable discrimination ability, as in Figure 1B, in which
the rank ordering of risk by the model is hierarchical but
calibration is visibly poor.
Measures of Model Fit and Informativeness
Other measures such as likelihood ratio tests and the Bayes
information criterion are now commonly used to assess the
utility of risk prediction models. Simply put, these tests, when
statistically significant, can indicate whether a risk model is
predicting disease incidence better than chance alone. They
Lloyd-Jones
Figure 1. Examples of 2 theoretical risk prediction models with
good (A) or poor (B) calibration. Black bars show predicted risk
levels; gray bars, observed events rates.
can further indicate whether the addition of new factors to a
base model provides better risk prediction than the base
model alone, provided that all of the same individuals are
being assessed by both models. The Bayes information
criterion adds a penalty for using more variables in the model,
so a price is paid for the addition of a variable to the model
unless it substantially improves risk prediction and overcomes the penalty.
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Risk Reclassification Analysis
One of the newest paradigms for assessing the utility of risk
prediction models involves measurement of the proportion of
individuals who are reclassified from 1 risk stratum (based on
estimated risk provided from a first model) to a different risk
stratum (based on estimated risk from a different model, or a
model that has additional variables compared with the first
CVD Risk Prediction
1771
model). Some of these risk reclassifications would be appropriate (based on subsequent observed events), meaning that
some individuals who have events are reclassified to higher
predicted-risk strata, and some who do not have events would
be reclassified to lower predicted-risk strata. However, some
reclassifications would be incorrect or inappropriate (eg, by
moving future cases to lower predicted-risk strata). This is
demonstrated in Table 3.
Pencina et al30 have proposed 2 indexes, the net reclassification improvement and the integrative discrimination index, to attempt to quantify the appropriateness and amount of
overall reclassification. In general, the net reclassification
improvement indicates how much more frequently appropriate reclassification occurs than inappropriate reclassification
with use of the new model; for this test, a value of P⬍0.05
suggests that a significantly greater number are being reclassified appropriately than are being reclassified inappropriately. The integrative discrimination index (which is equivalent to the difference in R2 between the 2 models being
compared)31 can be thought of as indicating how far individuals are moving on average along the continuum of predicted
risk. In many cases, models may have a significantly better
net reclassification improvement, but if the integrative discrimination index is small (even if significant), then a given
individual’s change in predicted risk with the new model will
be small on average (those at higher baseline risk will likely
change more than those at lower baseline risk). As an
example, consider a new risk prediction model that is being
compared with the FRS for stratifying a population into
ATP-III risk categories. The new model might have a
significant net reclassification improvement, reclassifying a
net of 10% of people more appropriately, meaning that people
who subsequently suffer events are upstaged in their predicted risk and people who do not suffer events are downstaged more often than cases are downstaged or noncases are
Table 3. Theoretical Example of Appropriate* and Inappropriate† Risk Reclassification By a New Compared With an Existing CVD
Risk Prediction Model
New Model Predicted Risk Stratum, %
Existing Model Predicted
Risk Stratum, %
0 –5
6-⬍10
10 –20
⬎20
No change
Reclassified up, appropriate
Reclassified up, appropriate
Reclassified up, appropriate
People who develop
events
0 –5
6-⬍10
Reclassified down, inappropriate
No change
Reclassified, up, appropriate
Reclassified up, appropriate
10–20
Reclassified down, inappropriate
Reclassified down, inappropriate
No change
Reclassified up, appropriate
⬎20
Reclassified down, inappropriate
Reclassified down, inappropriate
Reclassified down, inappropriate
No change
No change
Reclassified up, inappropriate
Reclassified up, inappropriate
Reclassified up, inappropriate
Reclassified down appropriate
No change
Reclassified up inappropriate
Reclassified up inappropriate
People who do not
develop events
0–5
6-⬍10
10–20
Reclassified down, appropriate
Reclassified down, appropriate
No change
Reclassified up, inappropriate
⬎20
Reclassified down, appropriate
Reclassified down, appropriate
Reclassified down, appropriate
No change
*Appropriate reclassification indicates that the new model reclassified some individuals who ultimately developed events into a higher predicted-risk group or
reclassified some individuals who ultimately did not have events into a lower predicted-risk group.
†Inappropriate reclassification indicates that the new model reclassified some individuals who ultimately developed events into a lower predicted-risk group or
reclassified some individuals who ultimately did not have events into a higher predicted risk group.
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upstaged. But, if the integrative discrimination index is small
(say, ⬍1%), then most of the net reclassification is occurring
immediately adjacent to the thresholds such as a change from
a predicted risk of 19.6% with an old model to a predicted
risk of 20.3% with a new model. This change would cross the
ATP-III decision threshold for immediate lipid-lowering
therapy but have no real impact in understanding or forecasting the patient’s risk (especially in light of the relatively
arbitrary clinical decision thresholds imposed by ATP-III).
The clinical significance of such small movements is heavily
dependent on the threshold selected. Indeed, this scenario is
what is often observed in current studies comparing older and
newer CVD risk prediction scores.32
Adding New Markers to Risk Prediction
Models and Comparing Risk
Prediction Models
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Given the overall acceptable to excellent risk discrimination
provided by existing multivariable risk prediction models,
they remain the logical standard to which new risk markers
must be added to demonstrate improvement or against which
newer models must be compared. The methods that should be
used to evaluate novel markers of cardiovascular risk were
recently summarized and recommended by a special panel of
the American Heart Association.33 All of the metrics discussed above should be examined and assessed for statistically significant improvement, and then clinical judgment
should be applied to determine whether there is also clinically
meaningful improvement that would affect decision making
in a reasonable number of patients. That said, if the change in
the C statistic is small for a new compared with an existing
risk score or when a novel risk marker is added to an existing
risk score, it is extremely unlikely that the novel risk score or
marker will provide a clinically meaningful improvement as
a screening tool across the population. However, the novel
risk marker may still add value within certain subgroups. To
demonstrate these concepts, it is useful to consider some
published examples.
Assessments of the FRS
In most cohorts studied to date, the FRS provides very good
discrimination, as evidenced by C statistics that typically
range from 0.75 to 0.80.34 –38 Invariably, the FRS and similar
risk scores discriminate risk better for women than they do for
men, often having C statistics in excess of 0.80 for women. In
addition, the FRS has good discrimination in most populations (including those from outside the United States) in
which it has been studied because it contains age as a
covariate and because of the fairly universal associations
between CVD and the major traditional risk factors the FRS
also incorporates. The calibration of the FRS in diverse
populations does differ more substantially, however, because
of variable “background” CVD incidence rates in different
settings. Studies to date indicate that the FRS discriminates
CHD risk very well and is well calibrated for a wide range of
white and black populations in the United States. For other
populations such as Asian Americans, American Indians,
Hispanic Americans, and native Chinese, discrimination remains acceptable, but the FRS tends to overestimate risk.17,39
However, with simple steps to recalibrate the FRS model, it
performs quite well in both discrimination and calibration.17
One of the benefits of this approach is that recalibration of the
FRS to a local population currently can help avoid the effort
and expense of developing novel cohorts and awaiting information on risk factor relationships.
Examples of Attempts to Improve Risk Prediction
Through the Addition of Novel Markers to
Existing Risk Scores
A substantial body of literature over the last 5 years has been
devoted to examining the addition of newer risk markers to
traditional risk scores. Essentially all of the single additional
markers studied to date have yielded little additional clinical
benefit when considered as screening tests across the entire
spectrum of risk. Given that most novel markers are correlated with traditional risk factors and therefore do not have
excessively high independent odds ratios (or relative risks), it
is difficult for them to change the C statistic substantially
when added to traditional risk models.29 However, within
subgroups predicted to be at intermediate risk by traditional
models, the addition of new risk markers can help reclassify
some individuals, and this is often the group for which the
addition of information from a new test is most clinically
useful. In the Women’s Health Study,34 when C-reactive
protein (CRP) was added to a model with traditional risk
factors, the C statistic did not improve measurably for the
whole population (the reported change was from 0.81 to 0.81,
although this was statistically significant given the large
sample size). However, women in the middle of the predicted
risk spectrum in this cohort (FRS predicted risk of 5% to 9%)
with a CRP ⬎3.0 had an observed event rate that was equal
to or greater than that of some women with an FRS-predicted
risk of ⱖ10% (Figure 2). It should be noted, however, that the
traditional risk model (FRS) determines a far greater magnitude of risk than does CRP level, indicating the important
context provided by the traditional risk model. Similar results
have been observed with the addition of parental history
information to traditional risk scores,41 with minimal change
in overall discrimination but potential benefit in intermediaterisk groups. More recent data suggest a larger change in the
C statistic associated with the addition of coronary artery
calcium scores to the FRS, with increments in the C statistic
of 0.02 to 0.11 in different race/ethnic groups.38
Scores of novel biomarkers have undergone similar assessments in recent years. In essentially every case, the incremental value in discrimination and in calibration has been
very small. For example, in 1 study, Folsom et al42 examined
19 novel putative risk markers for CHD in the Atherosclerosis Risk in Communities cohort. Considered individually, the
vast majority of the markers added between 0.000 and 0.005
to the C statistic for the traditional risk model, and several
markers actually decreased the C statistic slightly compared
with that for the traditional model. Calibration was not
improved significantly by the addition of these markers.
Because single risk markers have not added substantially to
risk prediction, a number of investigators have examined the
addition of multiple markers simultaneously to traditional
risk equations. In the Cardiovascular Health Study, Shlipak et
Lloyd-Jones
CVD Risk Prediction
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Figure 2. Observed risks for CHD in women
from the Women’s Health Study according to
baseline predicted Framingham risk score and
CRP level. Reprinted from Lloyd-Jones et al40
with permission from the publisher. Copyright
© 2006, American College of Physicians.
Downloaded from http://ahajournals.org by on July 8, 2022
al43 reported that the addition of 6 novel biomarkers [including interleukin-6, CRP, fibrinogen, lipoprotein(a) and factor
VIII levels, plus presence of anemia] to a traditional risk
model improved the C statistic from 0.73 to 0.74 in older
individuals with chronic kidney disease but decreased the C
statistic from 0.73 to 0.72 in those with normal renal function.
In the Framingham study, the addition of brain natriuretic
peptide levels plus microalbuminuria to a traditional risk
model for CVD events yielded an increase in the C statistic
from 0.76 to 0.77.44
More recent studies have extended these types of analyses
to assess reclassification as well. In a cohort of 5067 Swedish
individuals free of CVD at baseline who were followed up for
a median of 12.8 years, models with traditional risk factors
had C statistics of 0.758 and 0.760 for CVD and CHD events,
respectively. When 2 independently significant novel biomarkers were added to the traditional models, the C statistics
improved by 0.007 and 0.009 for CVD and CHD events,
respectively, indicating minimal clinical utility. Likewise, the
proportion of participants reclassified was modest (8% overall for CVD risk and 5% for CHD risk). The net reclassification improvement was 0% for CVD events and 4.7% for
CHD events, indicating no or only minimal net appropriate
improvement in risk classification. Greater improvements in
reclassification were observed in analyses restricted to
intermediate-risk individuals, but any correct reclassification
was due almost entirely to downward reclassification of
individuals who did not experience events (yet). Greater
amounts of net reclassification have been observed with the
addition of some novel biomarkers in other cohorts, especially when age has been effectively removed from the risk
model (either through matching in case-control studies or the
use of cohorts with very narrow age ranges).45
Examples of Newer Risk Prediction Models
As noted above, a surfeit of published risk prediction scores
for various CVD or CHD end points exist that have been
derived from a variety of different populations. As expected,
CVD risk scores tend to perform best in populations similar
to those from which they were derived. One recent example,
the Reynolds risk score for women, used state-of-the-art
statistical methodologies to derive and compare a new risk
score in women health professionals enrolled in the Women’s
Health Study clinical trial. Although an unbiased approach
was used to select variables for this model, it is noteworthy
that the traditional risk factors were still the strongest predictor variables for a broad CVD end point. In addition, CRP
levels and parental history also were selected for inclusion in
the model on the basis of improvement in the Bayes information criterion. When compared with a Framingham-like
model recalibrated for the end point of this study, overall
discrimination and calibration of the 2 models were similar.
Internal validation of the Reynolds risk score revealed that
5.8% of 8149 women were reclassified when the Reynolds
risk score was applied (compared with the FRS), with about
half of those being reclassified upward and half downward.
The clinical implications of downward reclassification are
unclear because withholding therapy from those potentially at
higher risk based on traditional risk factors does not seem
warranted given available clinical trial data. Likewise, statin
medications now have a long track record of safety and have
been estimated to be very cost-effective or even cost-saving
to levels of predicted risk as low as ⬇10%.46,47 Thus, the
generalizability and utility of this score and many others are
still being assessed.
Limitations of 10-Year Risk
Prediction Models
Ten-year risk estimation represents a substantial improvement over clinician judgment alone for appropriate risk
stratification.48,49 However, it has some acknowledged limitations. For example, because age is the most heavily
weighted variable in 10-year risk models derived from
populations that span the adult age spectrum, in younger
adults (men ⬍45 years of age and women ⬍65 years of age),
modest elevations in risk factors have little effect on 10-year
risk.50,51 Even younger adults with substantial risk factor
burden may still have 10-year risk estimates well below 10%,
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Figure 3. Remaining lifetime risks for atherosclerotic
CVD in men and women at 50 years of age. Optimal
risk factors (RFs) are defined as untreated total cholesterol ⬍4.65 mmol/L (⬍180 mg/dL), untreated
blood pressure ⬍120/⬍80 mm Hg, nonsmoker, and
nondiabetic. Not optimal risk factors are defined as
untreated total cholesterol 4.65 to 5.15 mmol/L (180
to 199 mg/dL), untreated systolic blood pressure 120
to 139 mm Hg or diastolic blood pressure 80 to
89 mm Hg, nonsmoker, and nondiabetic. Elevated
risk factors are defined as untreated total cholesterol
5.16 to 6.19 mmol/L (200 to 239 mg/dL), untreated
systolic blood pressure 140 to 159 mm Hg or diastolic blood pressure 90 to 99 mm Hg, nonsmoker,
and nondiabetic. Major risk factors are defined as
total cholesterol ⱖ6.20 mmol/L (ⱖ240 mg/dL) or
treated; systolic blood pressure ⱖ160 mm Hg, diastolic blood pressure ⱖ100 mm Hg, or treated;
smoker; or diabetic. Reprinted from Lloyd-Jones et
al25 with permission from the publisher. Copyright ©
2006, the American Heart Association.
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although their remaining lifetime risks may exceed 50% on
the basis of these risk factors.25 This is not a problem of the
10-year risk score per se nor incorrect risk prediction but
rather a function of the decision thresholds that are imposed
on the risk estimates and the short time horizon imposed by
10-year risk estimation.
The magnitude of risk factor levels needed to reach
moderately high (⬎10% 10-year predicted risk) and high-risk
(⬎20%) thresholds in the ATP III risk assessment algorithm
has recently been investigated. Cavanaugh-Hussey et al50
entered ranges of risk factor levels into the ATP III risk
assessment tool for men and women from 30 to 75 years of
age. For almost all combinations of risk factors, even with
extreme values, nonsmoking men ⬍45 years of age and
essentially all women ⬍65 years of age have 10-year predicted risks below 10%. Thus, many younger patients with
significant risk factor burden do not reach treatment thresholds based on current ATP-III recommendations. A similar
study51 has recently evaluated the updated Framingham risk
profile23 for global CVD (not just CHD) risk with somewhat
similar findings, although the use of an expanded CVD end
point allows more men and particularly more women in their
50s to exceed 10% and 20% predicted 10-year risk.51
Several alternatives exist to address some of the limitations
of 10-year risk estimates, particularly for younger adults. One
possible solution would be to lower treatment thresholds for
younger adults (eg, treat those ⬍50 years of age with 10-year
risk ⬎5%). However, 10-year risk equations do not stratify or
identify those at greater long-term risk well, particularly for
men.52A second alternative that has been suggested53 is to
perform subclinical disease imaging in essentially all middleaged adults to identify those with premature atherosclerosis.
Whereas this option is attractive at first glance, because one
is identifying the actual disease of interest, issues of cost,
radiation exposure (for coronary calcium assessment), technician and reader reliability (for some modalities such as
carotid intima-media thickness scanning), and uncertainty
about the need for repeat imaging make this strategy a matter
for continued active investigation. Recent statements from the
American Heart Association and the American College of
Cardiology54 recommend reserving imaging for those at
intermediate risk when there is uncertainty about the need or
desirability of starting lipid-lowering therapy. A final alternative is to change the horizon for risk estimation to provide
long-term or lifetime risk estimates for CVD end points.
Long-Term and Lifetime Risk Estimation as
an Adjunct to 10-Year Risk Estimation
Because of the limitations of a sole focus on 10-year risk,
various guidelines1,55 have recommended consideration of
lifetime risks when making decisions about preventive efforts
for CVD. More recently, Canadian2 and AHA3 guidelines
suggest that clinicians consider risk factor burden in the
context of patients’ lifetime risks for CVD. Thus, there is now
enhanced interest in lifetime CVD risk prediction, but the
methods for producing robust lifetime risk estimates and the
available data have been limited until recently.
Rationale for Lifetime Risk Estimation
Traditional statistical and epidemiological methods for estimating risks such as those used in currently available 10-year
CVD risk scores typically use Kaplan-Meier56 and Cox
proportional-hazards57 methods. However, these methods do
not account for competing risks (eg, death from non-CVD
causes) that become increasingly relevant over longer-term
follow-up. Methods of estimating lifetime risk do account for
competing risks and therefore can provide more “real-life”
risk prediction over the long term.58 Lifetime risk estimates
are useful in assessing the burden of disease in a population,
predicting the future burden of disease, and directly comparing lifetime risks between common diseases.58,59
Recent Studies
A number of recent studies25,52,60 – 66 have used lifetime risk
estimation or competing-risk methods to examine long-term
and lifetime risks for CHD and CVD. In the Framingham
Heart Study, lifetime risks for atherosclerotic CVD were
estimated by overall risk factor burden at 50 years of age.25
Participants were stratified into 5 mutually exclusive categories, as shown in Figure 3. Compared with participants with
Lloyd-Jones
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ⱖ2 major risk factors, those with optimal levels at 50 years of
age had substantially lower lifetime risks (5.2% versus 68.9%
in men; 8.2% versus 50.2% in women) and markedly longer
median survivals (⬎39 versus 28 years in men; ⬎39 versus
31 years in women). In fact, for those with any risk factor
levels above optimal, 10-year risks were generally ⬍10%,
whereas remaining lifetime risks were substantial.25 On the
basis of current ATP-III recommendations, such patients may
not receive pharmacological lipid-lowering therapy despite
the high likelihood that they will develop end-organ damage
from atherosclerotic disease during their remaining lifespan.
Even if they do receive lipid-lowering therapy, the message
that they have a low 10-year risk may be insufficient to
motivate them to adhere to lifestyle recommendations or to
comply with therapy.
The concept of identifying younger individuals at low
short-term but high lifetime risk was recently validated by
comparison with subclinical atherosclerosis imaging data. In
the Coronary Artery Risk Development in Young Adults
Study,67 ⬎90% of participants 32 to 47 years of age had a
10-year predicted risk ⬍10%. However, among this group,
approximately half had high predicted lifetime risk (ⱖ39%).
Even at these younger ages, participants with low 10-year but
high lifetime risk estimates had significantly greater burden
of carotid artery intima-media thickness and coronary artery
calcification and greater progression of subclinical atherosclerosis compared with participants with low 10-year and
low lifetime predicted risk.67 Data from the National Health
and Nutrition Examination Survey 2003 to 2006 reveal that a
majority (56%) of US adults, or 87 000 000 people, have such
a low 10-year but high lifetime predicted risk for CVD.68
Pencina et al69 recently published a quantitative method for
estimating long-term (30-year) risks for CVD that also
accounts for competing risks. Validation of these equations in
other settings is needed, but they may prove to be a powerful
tool for adjunctive risk assessment over and above current
10-year risk estimates.
Thus, long-term and lifetime risk estimation may represent
a potentially important new tool in our ability to identify
patients at risk for CVD and to provide more complete
information for risk communication. They should be considered for inclusion in future clinical prevention guidelines as
an adjunct to current 10-year risk estimates. However, further
study is needed to understand the clinical utility and impact of
both short-and long-term risk estimation and communication
strategies.
New Directions
Research in CVD risk prediction is actively ongoing. One
area of interest that is being pursued is the derivation of
age-specific risk equations for groups in narrow windows of
age rather than the current models derived from populations
with a wide range of ages in which age is included as a
predictor variable. Without age in the risk model and with a
focus on a specific age group, other risk markers may prove
to be more potent predictors of events, as was recently shown
in a cohort of Swedish men all of the same age.45 Other recent
developments include the general consensus that risk models
should focus on predicting global CVD events, not just CHD
CVD Risk Prediction
1775
events. Such a focus will serve to identify more individuals at
risk in the short term, overcoming some of the limitations of
10-year CHD risk prediction models noted above. This will
be true for women especially, given their propensity to be at
risk for stroke and heart failure earlier than CHD, in contrast
with men.51
The Future of Risk Prediction in
CVD Prevention
The field of cardiovascular risk prediction has now arrived at
a crossroads. It is currently unclear whether further efforts to
refine risk estimation models and use more complex, and
individualized, risk-based prevention schemes will yield substantial improvements toward “personalized risk assessment.”
The extreme alternative to personalized medicine may be to
adopt population-level approaches in which all individuals
above a certain age are treated to prevent CVD because
almost all individuals are at substantial lifetime risk.25 Active
investigations of polypill strategies based on age alone70 or
based on estimated risk are ongoing. In these paradigms, risk
assessment could still be a useful tool to improve communication and motivate physician and patient efforts in CVD
prevention, to promote understanding of the need for adherence to preventive medication strategies, and to highlight and
sustain efforts at therapeutic lifestyle modification.
Ultimately, it is clear that both population-level approaches
and high-risk approaches, which depend on robust and
accurate risk prediction models, are needed to curb the
ongoing epidemics of CVD and CHD, which may be worsening for the first time in 4 decades.71 Substantial efforts are
needed to optimize risk prediction models and prevention
algorithms for risk communication, patient motivation, and
clinical decision making. Whether and how the use of such
models influences physician and patient behavior and improves patient outcomes is the subject of a future article in
this series.
Disclosures
None.
References
1. Third Report of the National Cholesterol Education Program (NCEP) Expert
Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in
Adults (Adult Treatment Panel III) Final Report. Circulation. 2002;106:
3143–3421.
2. McPherson R, Frohlich J, Fodor G, Genest J. Canadian Cardiovascular
Society position statement: recommendations for the diagnosis and
treatment of dyslipidemia and prevention of cardiovascular disease. Can
J Cardiol. 2006;22:913–927.
3. Mosca L, Banka CL, Benjamin EJ, Berra K, Bushnell C, Dolor RJ,
Ganiats TG, Gomes AS, Gornik HL, Gracia C, Gulati M, Haan CK,
Judelson DR, Keenan N, Kelepouris E, Michos ED, Newby LK, Oparil S,
Ouyang P, Oz MC, Petitti D, Pinn VW, Redberg RF, Scott R, Sherif
K, Smith SC Jr, Sopko G, Steinhorn RH, Stone NJ, Taubert KA, Todd
BA, Urbina E, Wenger NK. Evidence-based guidelines for cardiovascular
disease prevention in women: 2007 update. Circulation. 2007;115:
1481–1501.
4. Wood D, De Backer G, Faergeman O, Graham I, Mancia G, Pyorala K.
Prevention of coronary heart disease in clinical practice: recommendations of the Second Joint Task Force of European and Other Societies
on Coronary Prevention. Atherosclerosis. 1998;140:199 –270.
5. Wood D, De Backer G, Faergeman O, Graham I, Mancia G, Pyorala K.
Prevention of coronary heart disease in clinical practice: summary of
1776
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
Downloaded from http://ahajournals.org by on July 8, 2022
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
Circulation
April 20, 2010
recommendations of the Second Joint Task Force of European and Other
Societies on Coronary Prevention. J Hypertens. 1998;16:1407–1414.
Matching the intensity of risk factor management with the hazard for
coronary disease events: Bethesda Conference 27. J Am Coll Cardiol.
1996;27:957–1047.
Report of the National Cholesterol Education Program Expert Panel on
Detection, Evaluation and Treatment of High Blood Cholesterol in
Adults. Arch Intern Med. 1988;148:36 – 69.
The 1980 report of the Joint National Committee on Detection, Evaluation, and Treatment of High Blood Pressure. Arch Intern Med. 1980;
140:1280 –1285.
The 1984 report of the Joint National Committee on Detection, Evaluation, and Treatment of High Blood Pressure. Arch Intern Med. 1984;
144:1045–1057.
The 1988 report of the Joint National Committee on Detection, Evaluation, and Treatment of High Blood Pressure. Arch Intern Med. 1988;
148:1023–1038.
Jackson R. Guidelines on preventing cardiovascular disease in clinical
practice: absolute risk rules– but raises the question of population
screening. BMJ. 2000;320:659 – 661.
Rose G. Environmental health: problems and prospects. J Roy Coll Phys
Lond. 1991;25:48 –52.
Vine DL, Hastings GE. Ischaemic heart disease and cholesterol: absolute
risk more informative than relative risk. BMJ. 1994;308:1040 –1041.
De Backer G, Ambrosioni E, Borch-Johnsen K, Brotons C, Cifkova R,
Dallongeville J, Ebrahim S, Faergeman O, Graham I, Mancia G, Cats
VM, Orth-Gomer K, Perk J, Pyorala K, Rodicio JL, Sans S, Sansoy V,
Sechtem U, Silber S, Thomsen T, Wood D. European guidelines on
cardiovascular disease prevention in clinical practice: Third Joint Task
Force of European and Other Societies on Cardiovascular Disease Prevention in Clinical Practice. Atherosclerosis. 2004;173:381–391.
Jackson R. Updated New Zealand cardiovascular disease risk-benefit
prediction guide. BMJ. 2000;320:709 –710.
Wilson PW, D’Agostino RB, Levy D, Belanger AM, Silbershatz H,
Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation. 1998;97:1837–1847.
D’Agostino RB, Grundy SM, Sullivan LM, Wilson P, for the CHD Risk
Prediction Group. Validation of the Framingham coronary heart disease
prediction scores: results of a multiple ethnic groups investigation. JAMA.
2001;286:180 –187.
Conroy RM, Pyorala K, Fitzgerald AP, Sans S, Menotti A, De Backer G,
De Bacquer D, Ducimetiere P, Jousilahti P, Keil U, Njolstad I, Oganov
RG, Thomsen T, Tunstall-Pedoe H, Tverdal A, Wedel H, Whincup P,
Wilhelmsen L, Graham IM. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J. 2003;24:
987–1003.
Assmann G, Cullen P, Schulte H. Simple scoring scheme for calculating
the risk of acute coronary events based on the 10-year follow-up of the
Prospective Cardiovascular Munster (PROCAM) study. Circulation.
2002;105:310 –315.
Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, May M, Brindle
P. Derivation and validation of QRISK, a new cardiovascular disease risk
score for the United Kingdom: prospective open cohort study. BMJ.
2007;335:136 –147.
Ridker PM, Buring JE, Rifai N, Cook NR. Development and validation of
improved algorithms for the assessment of global cardiovascular risk in
women: the Reynolds risk score. JAMA. 2007;297:611– 619.
Ridker PM, Paynter NP, Rifai N, Gaziano JM, Cook NR. C-reactive
protein and parental history improve global cardiovascular risk prediction: the Reynolds risk score for men. Circulation. 2008;118:
2243–2251.
D’Agostino RB Sr, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro
JM, Kannel WB. General cardiovascular risk profile for use in primary
care: the Framingham Heart Study. Circulation. 2008;117:743–753.
Grundy SM, Cleeman JI, Merz CNB, Brewer HB Jr, Clark LT, Hunninghake DB, Pasternak RC, Smith SC Jr, Stone NJ, for the Coordinating
Committee of the National Cholesterol Education Program. Implications
of recent clinical trials for the National Cholesterol Education Program
Adult Treatment Panel III guidelines. Circulation. 2004;110:227–239.
Lloyd-Jones DM, Leip EP, Larson MG, D’Agostino RB, Beiser A,
Wilson PW, Wolf PA, Levy D. Prediction of lifetime risk for cardiovascular disease by risk factor burden at 50 years of age. Circulation.
2006;113:791–798.
Hosmer DW, Lemeshow S. Applied Logistic Regression. 2nd ed. New
York, NY: John Wiley & Sons; 2000.
27. Hilden J. The area under the ROC curve and its competitors. Med Decis
Making. 1991;11:95–101.
28. Cook NR. Use and misuse of the receiver operating characteristic curve
in risk prediction. Circulation. 2007;115:928 –935.
29. Pepe MS, Janes H, Longton G, Leisenring W, Newcomb P. Limitations
of the odds ratio in gauging the performance of a diagnostic, prognostic,
or screening marker. Am J Epidemiol. 2004;159:882– 890.
30. Pencina MJ, D’Agostino RB Sr, D’Agostino RB Jr, Vasan RS. Evaluating
the added predictive ability of a new marker: from area under the ROC
curve to reclassification and beyond. Stat Med. 2008;27:157–172.
31. Pepe MS, Feng Z, Gu JW. Comments on “Evaluating the added predictive
ability of a new marker: from area under the ROC curve to reclassification and beyond” by M.J. Pencina et al. Stat Med. 2008;27:207–212.
32. Melander O, Newton-Cheh C, Almgren P, Hedblad B, Berglund G,
Engstrom G, Persson M, Smith JG, Magnusson M, Christensson A,
Struck J, Morgenthaler NG, Bergmann A, Pencina MJ, Wang TJ. Novel
and conventional biomarkers for prediction of incident cardiovascular
events in the community. JAMA. 2009;302:49 –57.
33. Hlatky MA, Greenland P, Arnett DK, Ballantyne CM, Criqui MH, Elkind
MSV, Go AS, Harrell FE Jr, Hong Y, Howard BV, Howard VJ, Hsue PY,
Kramer CM, McConnell JP, Normand S-L, O’Donnell CJ, Smith SC Jr,
Wilson PWF. Criteria for evaluation of novel markers of cardiovascular risk:
a scientific statement From the American Heart Association. Circulation.
2009;119:2408–2416.
34. Ridker PM, Rifai N, Rose L, Buring JE, Cook NR. Comparison of
C-reactive protein and low-density lipoprotein cholesterol levels in the
prediction of first cardiovascular events. N Engl J Med. 2002;347:
1557–1565.
35. Rutter MK, Meigs JB, Sullivan LM, D’Agostino RB Sr, Wilson PWF.
C-reactive protein, the metabolic syndrome, and prediction of cardiovascular events in the Framingham Offspring Study. Circulation. 2004;110:
380 –385.
36. van der Meer IM, de Maat MPM, Kiliaan AJ, van der Kuip DAM,
Hofman A, Witteman JCM. The value of C-reactive protein in cardiovascular risk prediction. Arch Intern Med. 2003;163:1323–1328.
37. Wilson PWF, Nam B-H, Pencina M, D’Agostino RB Sr, Benjamin EJ,
O’Donnell CJ. C-Reactive protein and risk of cardiovascular disease in
men and women from the Framingham Heart Study. Arch Intern Med.
2005;165:2473–2478.
38. Detrano R, Guerci AD, Carr JJ, Bild DE, Burke G, Folsom AR, Liu K,
Shea S, Szklo M, Bluemke DA, O’Leary DH, Tracy R, Watson K, Wong
ND, Kronmal RA. Coronary calcium as a predictor of coronary events in
four racial or ethnic groups. N Engl J Med. 2008;358:1336 –1345.
39. Liu J, Hong Y, D’Agostino RB Sr, Wu Z, Wang W, Sun J, Wilson PWF,
Kannel WB, Zhao D. Predictive value for the Chinese population of the
Framingham CHD risk assessment tool compared with the Chinese MultiProvincial Cohort Study. JAMA. 2004;291:2591–2599.
40. Lloyd-Jones DM, Liu K, Tian L, Greenland P. Narrative review:
assessment of C-reactive protein in risk prediction for cardiovascular
disease. Ann Intern Med. 2006; 145: 35– 42.
41. Lloyd-Jones DM, Nam B-H, D’Agostino RB Sr, Levy D, Murabito JM,
Wang TJ, Wilson PWF, O’Donnell CJ. Parental cardiovascular disease as
a risk factor for cardiovascular disease in middle-aged adults: a prospective study of parents and offspring. JAMA. 2004;291:2204 –2211.
42. Folsom AR, Chambless LE, Ballantyne CM, Coresh J, Heiss G, Wu KK,
Boerwinkle E, Mosley TH Jr, Sorlie P, Diao G, Sharrett AR. An
assessment of incremental coronary risk prediction using c-reactive
protein and other novel risk markers: the Atherosclerosis Risk in Communities Study. Arch Intern Med. 2006;166:1368 –1373.
43. Shlipak MG, Fried LF, Cushman M, Manolio TA, Peterson D,
Stehman-Breen C, Bleyer A, Newman A, Siscovick D, Psaty B. Cardiovascular mortality risk in chronic kidney disease: comparison of traditional and novel risk factors. JAMA. 2005;293:1737–1745.
44. Wang TJ, Gona P, Larson MG, Tofler GH, Levy D, Newton-Cheh C,
Jacques PF, Rifai N, Selhub J, Robins SJ, Benjamin EJ, D’Agostino RB,
Vasan RS. Multiple biomarkers for the prediction of first major cardiovascular events and death. N Engl J Med. 2006;355:2631–2639.
45. Zethelius B, Berglund L, Sundstrom J, Ingelsson E, Basu S, Larsson A,
Venge P, Arnlov J. Use of multiple biomarkers to improve the prediction
of death from cardiovascular causes. N Engl J Med. 2008;358:2107–2116.
46. Heart Protection Study Collaborative Group. Statin cost-effectiveness in
the United States for people at different vascular risk levels. Circ Cardiovasc Qual Outcomes. 2009;2:65–72.
Lloyd-Jones
Downloaded from http://ahajournals.org by on July 8, 2022
47. Cost-effectiveness of simvastatin in people at different levels of vascular
disease risk: economic analysis of a randomised trial in 20536 individuals. Lancet. 2005;365:1779 –1785.
48. Grover SA, Lowensteyn I, Esrey KL, Steinert Y, Joseph L, Abrahamowicz M. Do doctors accurately assess coronary risk in their patients?
Preliminary results of the Coronary Health Assessment Study. BMJ.
1995;310:975–978.
49. Montgomery AA, Fahey T, MacKintosh C, Sharp DJ, Peters TJ. Estimation of cardiovascular risk in hypertensive patients in primary care.
Br J Gen Pract. 2000;50:127–128.
50. Cavanaugh-Hussey MW, Berry JD, Lloyd-Jones DM. Who exceeds
ATP-III risk thresholds? Systematic examination of the effect of varying
age and risk factor levels in the ATP-III risk assessment tool. Prev Med.
2008;47:619 – 623.
51. Marma AK, Lloyd-Jones DM. Systematic examination of the updated
Framingham Heart Study general cardiovascular risk profile. Circulation.
2009;120:384 –390.
52. Lloyd-Jones DM, Wilson PWF, Larson MG, Beiser A, Leip EP,
D’Agostino RB, Levy D. Framingham risk score and prediction of
lifetime risk for coronary heart disease. Am J Cardiol. 2004;94:20 –24.
53. Naghavi M, Falk E, Hecht HS, Jamieson MJ, Kaul S, Berman D, Fayad
Z, Budoff MJ, Rumberger J, Naqvi TZ, Shaw LJ, Faergeman O, Cohn J,
Bahr R, Koenig W, Demirovic J, Arking D, Herrera VLM, Badimon J,
Goldstein JA, Rudy Y, Airaksinen J, Schwartz RS, Riley WA, Mendes
RA, Douglas P, Shah PK. From vulnerable plaque to vulnerable patient,
part III: executive summary of the Screening for Heart Attack Prevention
and Education (SHAPE) Task Force Report. Am J Cardiol. 2006;98:2–15.
54. ACCF/AHA 2007 Clinical expert consensus document on coronary artery
calcium scoring by computed tomography in global cardiovascular risk
assessment and in evaluation of patients with chest pain. Circulation.
2007;115:402– 426.
55. Grundy SM, Bazzarre T, Cleeman J, D’Agostino RB, Hill M, HoustonMiller N, Kannel WB, Krauss R, Krumholz HM, Lauer RM, Ockene IS,
Pasternak RC, Pearson TA, Ridker PM, Wood D. Prevention Conference V:
beyond secondary prevention: identifying the high-risk patient for primary
prevention: medical office assessment: Writing Group 1. Circulation. 2000;
101:e3–e11.
56. Kaplan EL, Meier P. Nonparametric estimation from incomplete observations. J Am Stat Assoc. 1958;53:457– 481.
57. Cox DR. Regression models and life tables (with discussions). J R Stat
Soc B. 1972;34:187–220.
58. Beiser A, D’Agostino RB, Seshadri S, Sullivan LM, Wolf PA. Computing
estimates of incidence, including lifetime risk. Stat Med. 2002;19:
1495–1522.
59. Gaynor JJ, Feuer EJ, Tan CC, Wu DH, Little CR, Straus DJ, Clarkson
BD, Brennan MF. On the use of cause-specific failure and conditional
failure probabilities. J Am Stat Assoc. 1993;88:402– 409.
CVD Risk Prediction
1777
60. Lloyd-Jones DM, Larson MG, Beiser A, Levy D. Lifetime risk of
developing coronary heart disease. Lancet. 1999;353:89 –92.
61. Lloyd-Jones DM, Larson MG, Leip EP, Beiser A, D’Agostino RB,
Kannel WB, Murabito JM, Vasan RS, Benjamin EJ, Levy D. Lifetime
risk for developing congestive heart failure: the Framingham Heart Study.
Circulation. 2002;106:3068 –3072.
62. Lloyd-Jones DM, Wang TJ, Leip EP, Larson MG, Levy D, Vasan RS,
D’Agostino RB, Massaro JM, Beiser A, Wolf PA, Benjamin EJ. Lifetime
risk for development of atrial fibrillation: the Framingham Heart Study.
Circulation. 2004;110:1042–1046.
63. Lloyd-Jones DM, Wilson PWF, Larson MG, Leip EP, Beiser A,
D’Agostino RB, Cleeman JI, Levy D. Lifetime risk for coronary heart
disease by cholesterol levels at selected ages. Arch Intern Med. 2003;
163:1966 –1972.
64. Mamun AA, Peeters A, Barendregt J, Willekens F, Nusselder W,
Bonneux L. Smoking decreases the duration of life lived with and without
cardiovascular disease: a life course analysis of the Framingham Heart
Study. Eur Heart J. 2004;25:409 – 415.
65. Bleumink GS, Knetsch AM, Sturkenboom MCJM, Straus SMJM,
Hofman A, Deckers JW, Witteman JCM, Stricker BHC. Quantifying the
heart failure epidemic: prevalence, incidence rate, lifetime risk and
prognosis of heart failure: the Rotterdam Study. Eur Heart J. 2004;25:
1614 –1619.
66. Franco OH, Peeters A, Bonneux L, de Laet C. Blood pressure in
adulthood and life expectancy with cardiovascular disease in men and
women: life course analysis. Hypertension. 2005;46:280 –286.
67. Berry JD, Liu K, Folsom AR, Lewis CE, Carr JJ, Polak JF, Shea S,
Sidney S, O’Leary DH, Chan C, Lloyd-Jones DM. Prevalence and progression of subclinical atherosclerosis in younger adults with low
short-term but high lifetime estimated risk for cardiovascular disease: the
CARDIA and MESA studies. Circulation. 2009;119:382–389.
68. Marma AK, Berry JD, Ning H, Persell SD, Lloyd-Jones DM. Distribution
of 10-year and lifetime predicted risks for cardiovascular disease in US
adults: findings from the National Health and Nutrition Examination
Survey 2003 to 2006. Circ Cardiovasc Qual Outcomes. 2010;3:8 –14.
69. Pencina MJ, D’Agostino RB Sr, Larson MG, Massaro JM, Vasan RS.
Predicting the 30-year risk of cardiovascular disease: the Framingham
Heart Study. Circulation. 2009;119:3078 –3084.
70. Effects of a polypill (Polycap) on risk factors in middle-aged individuals
without cardiovascular disease (TIPS): a phase II, double-blind, randomised trial. Lancet. 2009;373:1341–1351.
71. Ford ES, Capewell S. Coronary heart disease mortality among young
adults in the U.S. from 1980 through 2002: concealed leveling of mortality rates. J Am Coll Cardiol. 2007;50:2128 –2132.
KEY WORDS: epidemiology
䡲
prevention
䡲
risk factors