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VOLUME 11 NUMBER 3 • SEPTEMBER 2014
REVIEW
SA JOURNAL OF DIABETES & VASCULAR DISEASE
importance of absolute risk estimation in people with diabetes
as the appropriate basis for CVD risk-factor modification. Such
an approach is further supported by the gradual shift in the
management of diabetes mellitus from a glucocentric focus to an
intensive multifactorial strategy targeting reduction in the risk of
both macro- and microvascular complications of diabetes.
12,13
The growing recognition of the importance of global CVD risk in
people with diabetes has generated interest among researchers to
develop tools with improved performance to estimate absolute risk
in people with diabetes, or to establish the validity of the existing
ones and refine their performance.
7
The following development
is a discussion on the rationale and strategies for global CVD
risk estimation in people with diabetes, with emphasis on the
specificities and limitations of these strategies. The discussion is
largely inspired by new knowledge gained from CVD risk modelling
in the ADVANCE study.
3,14
Overview of global cardiovascular risk assessment
Global cardiovascular risk assessment is based on the combination of
predictive information from several cardiovascular risk factors using
mathematical equations (also called models). In those models,
the coefficient of each included risk factor indicates its relative
contribution to the overall (global) CVD risk.
2,15
A model can be
used to estimate the risk that a disease is present (diagnostic model)
or to estimate the risk that a particular disease or health event will
occur within a given time period (prognostic models). The focus of
the current article is on prognostic models.
Once developed, a cardiovascular risk model normally requires a
validation in both the sample population that was used to develop
the model (internal validation) and in independent populations
(external validation). Validation consists of testing whether the
prognostic model accurately estimates the risk of future events in
one or several populations.
2,15
The performance of absolute cardiovascular risk models in
validation studies is commonly assessed in terms of discrimination,
calibration and, more recently, reclassification.
2,15
Discrimination is
the ability of the model to distinguish people who go on to develop
a cardiovascular event and those who remain event free.
2,15
For
example, for two individuals with diabetes with one developing
a cardiovascular event after 10 years of follow up and the other
remaining CVD free within that same time period, a discriminating
model will systematically assign, at the start of the follow up, a
higher absolute risk to the first subject compared to the second.
Discrimination is commonly assessed using the C-statistic,
which ranges from 0.5 (lack of discrimination) to 1.0 (perfect
discrimination).
1,2,15
In general, a C-statistic of 0.7 or greater is
considered acceptable.
Calibration describes the agreement between estimated and
observed risks. It is assessed by comparing absolute risk estimates
from the model with the actual event rates in the test population.
1,2,15
For illustration, a 10-year estimated absolute risk of CVD of 20%
for a patient indicates that, in a given group of patients with
similar characteristics, 20% will experience a cardiovascular
event within a 10-year period of follow up. The most commonly
reported measure of calibration is the Hosmer-Lemeshow statistic.
Estimates of calibration are sensitive to differences in background
levels of risk across populations. For example, if a given CVD risk
model is developed in a high-risk population but tested in a low-
risk population, the estimated absolute risks will be unreliably
high. Recalibration of the risk model by adjusting the baseline risk
estimates to fit the target population may help correcting the over-
or underestimation of risk.
1,15
Global cardiovascular risk estimation in people with
diabetes
Global CVD risk has been estimated in people with diabetes using
essentially three main approaches.
16
In the ‘CVD riskequivalent’
approach described above, the presence of diabetes mellitus is
considered to confer a 10-year absolute CVD risk of 20% or more,
which is approximately the 10-year CVD event rate observed
in non-diabetic individuals with a prior history of CVD. Such an
approach appears to be counter-intuitive as the CVD risk is not
uniformly distributed among people with diabetes. This is further
supported by many studies showing multivariable risk estimation
to be significantly better than classification of diabetes as a
cardiovascular risk equivalent.
17,18
In the second approach, also termed ‘step approach’, unifying
CVD risk-estimation models are developed for both people with
diabetes and those without the condition. This approach assumes
that major risk factors for CVD are related to future occurrence of
CVD in a similar way, regardless of the status for diabetes mellitus.
Stated otherwise, everything else being equal, an individual with
diabetes will always have a higher risk of CVD (by a constant
amount) than the non-diabetic subject with the same level of other
risk factors (e.g. blood pressure or lipid levels). This has been the
basis for models such as the popular Framingham cardiovascular
absolute-risk models.
16
In the last approach, also known as the ‘interaction approach’,
CVD risk models are constructed separately for people with and
without diabetes. This approach suggests that risk factors are related
to future CVD risk in different ways in people with and without
diabetes. This approach in people with diabetes was initially used
by the UKPDS investigators.
9,19
Available studies largely suggest
that classical cardiovascular risk factors (including smoking, blood
pressure and lipid variables) and even some novel risk factors,
16,20-23
affect the risk of CVD in similar ways in people with and without
diabetes with no evidence of interaction.
Some risk factors or characteristics are likely to be more frequent
in people with diabetes and may justify separate cardiovascular
risk models for people with diabetes. These diabetes-specific
characteristics include prescriptions of cardiovascular risk-reducing
therapies, which may differ in people with and without diabetes.
Additional specific factors are haemoglobin A
1c
(HbA
1c
) levels, urinary
albumin excretion rate and markers of microvascular complications
of diabetes in general (especially retinopathy). These have been
demonstrated to be associated with CVD risk and can contribute
useful information to predictions.
24-29
Performance of popular CVD risk models and the
ADVANCE study
At the time the ADVANCE study was conducted, CVD riskprediction
models in thegeneral populationweredominatedbymodels developed
from the Framingham Heart study, which for many could also be used
in people with diabetes.
7
CVD risk models specific to people with
diabetes were also available, particularly those from the UKPDS study.
7
However, the clinical utility and comparative performance of these
popular CVD risk models in contemporary populations with diabetes
in diverse settings were still to be established.
