VOLUME 10 NUMBER 2 • JUNE 2013
61
SA JOURNAL OF DIABETES & VASCULAR DISEASE
REVIEW
levels. However, the concept of diabetes as a CVD risk equivalent has
been losing ground in recent years, with the accumulating evidence
challenging its validity in all circumstances
11
and supporting the
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 macrovascular 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 those strategies. The discussion is
largely inspired by new knowledge gained from CVD risk modeling
in ADVANCE study.
3,14
Global cardiovascular risk assessment: overview
Global cardiovascular risk assessment is based on th=e 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 their
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 paper is on prognostic models.
Once developed, a cardiovascular risk model normally requires a
validation both on the sample population that was used to develop
the model (internal validation) and on 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 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 the calibration is the Hosmer-Lemeshow
statistic. Estimates of calibration are sensitive to differences in the
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 risk equivalent’
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 counterintuitive 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 both for 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 ‘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, 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 A1c (HbA
1c
), urinary
albumin excretion 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 risk prediction
models in the general population were dominated by models
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
