62
VOLUME 10 NUMBER 2 • JUNE 2013
REVIEW
SA JOURNAL OF DIABETES & VASCULAR DISEASE
performance of these popular CVD risk models in contemporary
populationswithdiabetesindiversesettingswerestilltobeestablished.
Therefore, one of the major initial steps was to conduct extensive
validation studies of the Framingham and UKPDS CVD risk models,
using the unique features of the ADVANCE cohort.
3
These validation
studies revealed that, in the cohort of ADVANCE participants who
had no known history of CVD at their enrolment in the trial, the
4-year absolute risk of cardiovascular events and components was
largely overestimated by the Framingham-Anderson
30
, Framingham-
D’Agostino
31
and UKPDS risk models
9,19
. This overestimation was also
observed in men and women, Caucasians and non-Caucasians, and
the double placebo cohort (i.e. those assigned to the placebo group
in the blood pressure lowering arm and the standard care group of
the blood pressure control arm).
3
Discrimination of the Framingham
and UKPDS risk models in predicting CVD events in ADVANCE was
poor for stroke, and modest-to-acceptable for coronary heart disease
and total CVD. Recalibration substantially attenuated the magnitude
of risk overestimation by the Framingham and UKPDS risk models
in ADVANCE. Discrimination was unaffected as expected, indicating
the need for new CVD risk models with improved predictive accuracy
for people with diabetes, particularly those who are receiving many
contemporary cardiovascular risk reducing therapies.
Development of the ADVANCE cardiovascular risk model
In developing a new model for risk prediction, it is critical to
account for the limitations of existing ones, in order to improve
performance. The inclusion in ADVANCE of participants from
many countries provided the opportunity to account for the
substantial variation in the care of diabetes and CVD around the
world. Available models so far had been derived from homogenous
populations. The ADVANCE model targets total CVD and therefore
captures the interrelation between components of CVD such as
CHD or stroke; unlike many existing models that have focussed
specifically on these components. The complexity of the relationship
between chronic hyperglycaemia and cardiovascular risk has been
less fully addressed in existing models. Some improvement was
achieved in the ADVANCE model through integration of risk factors
to capture both the exposure to chronic hyperglycaemia prior to
and after the clinical diagnosis of diabetes. Statistical method is
an important component of model development. Trusted statistical
methods were used to select the potential risk factors and test their
suitability for inclusion in the ADVANCE risk model.
14
Risk factors considered for inclusion in the ADVANCE model
were: age at clinical diagnosis of diabetes, duration of diagnosed
diabetes, sex, blood pressure (BP) indices (systolic BP, diastolic BP,
mean arterial (MAP) and pulse (PP) pressures), lipid variables (total,
HDL and non-HDL cholesterol, ratio total/HDL cholesterol and
triglycerides), body mass index (BMI), waist circumference, waist-
to-hip ratio, BP lowering medication (i.e. treated hypertension),
statin use, current smoking, retinopathy, atrial fibrillation (past
or present), urinary albumin/creatinine ratio (ACR) and serum
creatinine (
cr
S
), HbA
1c
, fasting blood glucose and randomised
treatments (BP lowering and glucose control regimens). Ten of
these candidate risk factors were included in the final ADVANCE risk
model. Age at diabetes diagnosis and known duration of diabetes
were preferred to age at baseline to improve the applicability of the
ADVANCE risk model to other populations. The beta coefficients
and accompanying standard error for risk factors in the ADVANCE
risk model are shown in Table 1.
14
Table 1.
Beta coefficients (95% confidence interval) and standard errors for
predictors in the ADVANCE CVD prediction model.14
Variable
Parameter
estimate
(standard error)
P value*
Age at diagnosis (per 1 year increase)
0.062 (0.008)
< 0.001
Sex (women vs. men)
-0.474 (0.098)
< 0.001
Known duration of diabetes
(per 1 year increase)
0.083 (0.010)
< 0.001
Pulse pressure (per 1 mmHg increase)
0.007 (0.003)
0.016
Retinopathy (yes vs. no)
0.383 (0.101)
< 0.001
Atrial fibrillation (present vs. absent)
0.601 (0.154)
< 0.001
HbA
1c
(per 1% increase)
0.099 (0.027)
< 0.001
Log of urinary albumin/creatinine
ratio (per 1 log mg/g increase)
0.193 (0.033)
< 0.001
Non-HDL cholesterol (per 1 mmol/l
increase)
0.126 (0.034)
< 0.001
Treated hypertension (yes vs. no)
0.242 (0.106)
0.022
Performance of the ADVANCE risk model
14
The applicability of the ADVANCE risk model tested on the same
population used to develop the model (i.e. internal validation) and
on an independent external sample for which the DIAB-HYCAR
cohort
32
was used. In both internal and external validations,
the discrimination of the ADVANCE model was acceptable. In
comparison with existing total CVD models, the ADVANCE model
largely outperformed the Framingham-Anderson and Framingham-
D’Agostino models. The calibration of the ADVANCE model was
excellent in internal validation and good in external validation, with
only a modest risk underestimation. This is likely explained by the
difference in the levels of preventive therapies between ADVANCE
and DIAB-HYCAR population. Interestingly, the agreement between
predictions by the ADVANCE models and the observed CVD events
was consistent across different cut-offs or predicted risk of CVD.
For comparison, the two Framingham equations overestimated
the risk of CVD in the DIAB-HYCAR cohort by 65% (Anderson
equation) and 99% (D’Agostino equation). Using a cut-off for
4-year predicted risk of ≥ 8% (which is approximately equivalent to
a 10-year predicted risk of 20% and above), the ADVANCE model
would reliably identify the 22% of the ADVANCE participants and
39% of the DIAB-HYCAR participants in whom 48% and 66%
of CVD events respectively occurred during follow-up. Further
intensifying treatment in such groups on top of any baseline therapy
could achieve significant gain in terms of CVD risk reduction.
