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Vascular risk screening: possible or too much, too soon?
Background
Cardiovascular disease accounts for 36% of mortal-
ity and 20% of hospital admissions in the UK. It
has long been known that environmental factors
comprise the bulk of cardiovascular disease risk
and thus that screening for cardiovascular disease
may be desirable. The InterHEART study con-
firmed that 90% of population attributable risk
could be ascribed to lipids, hyper-
tension, smoking, central obesity,
diabetes, plus poor diet, lack of
exercise and psychosocial factors
(1). Over the years, there has been
a welcome decline in the rates of
cardiovascular disease that has been
because of environment and life-
style changes rather than any spe-
cific medical intervention. Rates of
cardiovascular disease started to
decline in the 1950–1960s and then
accelerated as air pollution reduced
because coal-fired heating was
replaced by cleaner fuels, rates of
smoking fell and pharmaceutical
intervention became more com-
mon, with initially aspirin and later
beta-blockers and finally angioten-
sin-converting enzyme inhibitors
and statins (2–4).
In the UK, the implementation of
the National Service Framework
for Coronary Heart Disease (NSF-
CHD) (5) further accelerated the
fall when it was implemented
through the primary care Quality
Outcomes Framework, and the
National Health Service (NHS)
targets were achieved 2 years early.
Yet, as noted in the official review
of the NSF, most of the inter-
ventions that were implemented
involved high-risk groups (i.e.
patients with established cardio-
vascular disease), while little had
been carried out in primary pre-
vention or to address high rates of
disease in deprived and immigrant
populations (6). It is an unfortu-
nate truth that 30–50% of patients
experience their first vascular
event as their last, so ideally intervention needs to be
targeted in patients prior to the presence of overt
disease, i.e. primary prevention. This has been recog-
nised and made an official priority through the
national screening strategy (cardiovascular) (7). The
challenge remains how to do this affordably even if
the overall health economics is favourable at a cost
of £20k ⁄ life year saved.
Extensive epidemiological data has identified the envi-
ronmental risk factors responsible for 90% of population
attributable risk for coronary heart disease. These studies
have identified age, gender, smoking, diabetes, hyperten-
sion and hyperlipidaemia as contributory factors and
allowed cardiovascular risk calculators to be developed.
With the availability of effective interventions, there is
an increasing focus on reducing cardiovascular disease
through early case identification by screening. However,
cardiovascular risk estimation is an imprecise art better
suited to exclude patients from treatment than the
unequivocal identification of high-risk individuals and
subject to numerous confounding factors. The impreci-
sion of risk calculators and the need to add additional
specific or novel risk factors have led to increased com-
plexity and sensitivity at the expense of specificity. It
may be necessary to add more specific secondary bio-
markers or imaging methods to truly identify high-risk
individuals. Even after identification, the issue of accept-
ability of long-term treatment for an asymptomatic con-
dition remains, and there are concerns about adherence
to therapy. Trials of screening allied with multiple risk
factor intervention on cardiovascular events are dated;
although many suggest small benefits, overall the results
were disappointing. More modern studies have been
small scale or only focused on surrogate markers. Large
scale cardiovascular end-point trials of screening inter-
vention are required to confirm the benefits of vascular
risk screening. The focus on screening for high-risk indi-
viduals should not obscure the need to reduce risk fac-
tor burdens in the whole population through public
health interventions.
PERSPECT IVE
ª 2009 Blackwell Publishing Ltd Int J Clin Pract, July 2009, 63, 7, 989–996doi: 10.1111/j.1742-1241.2009.02111.x 989
30–50% of
patients
experience
their first
vascular
event as their
last
Problems with cardiovascular riskscreening
There are two major problems with vascular screen-
ing. First, many of the risk factors are asymptomatic,
thus leading to a natural reluctance for potential
beneficiaries to present for screening or to continue
with therapy. The disappointing results of the Multi-
ple Risk Factor Intervention Study (MRFIT) (8)
proved that even in a well-motivated population,
after 7 years lifestyle measures, although accepted,
were not implemented and thus had no effect on
cardiovascular events. A decade later, similar results
were found for nurse-based intervention in the
OXCHECK study, which showed only a theoretical
benefit on cardiovascular disease (CVD) risk (relative
12% reduction in Framingham risk) (9). Thus, the
only effective intervention in a short time horizon
(within and not between generations) is drug ther-
apy.
For one major risk factor (tobacco addiction), the
only moderately successful treatment is a short-term
drug therapy – varenicline (10,11). Other factors
require long-term therapy: there are data showing
the efficacy of aspirin in primary prevention in both
men and older women (12), antihypertensive therapy
even in prehypertension (130 ⁄ 80 mmHg at age
40 years) (13) and statins in high (14), intermediate
(15) and low (16) risk populations. Thus, evidence
exists for the drug therapies required to treat high
CVD risk successfully once it has been identified.
The concept of blanket treatment for multiple risk
factors using a poly-pill has been advocated (17).
Yet, in this cynical age, concerns remain that the
benefits of drug therapy do not exceed the risks from
side effects and, especially for older off-patent drugs
combined in a poly-pill.
Cardiovascular risk screeningalgorithms
The greatest risk factor for atherosclerotic disease is
age, and therefore it will always trump other factors
in any analysis. However, identification of patients at
risk of atherosclerosis means that individuals must
be identified at a stage when only their relative risk
is increased. As no single risk factor shows a totally
definitive association with increased CVD risk, all
protocols rely on cardiovascular risk estimation algo-
rithms. The concept is that individual risk can be
derived from population studies using systematic
epidemiological methods and exponential risk equa-
tions (18). The oldest and best established of these is
the Framingham equation from 1991 (19), which is
still used in the UK as the basis of risk screening
even though it over-predicts CVD events by 30%.
Other risk scores have been derived from European
populations (Munster Heart Study) (20) or based on
World Health Organisation MONICA cohorts (21)
or analysis of national disease registers (22,23)
(Table 1). However, even self-defined reporting of
cardiovascular risk factors can be used satisfactorily
to identify patients at high cardiovascular risk (24).
The Framingham risk function has been developed
over the years into variants to determine the risk of
stroke, those which include low density lipoprotein
(LDL) cholesterol or glucose and one to determine
lifetime risk (18,25). The last concept is particularly
relevant. Analysis shows that individuals with fewer
than three risk factors at age 50 years have a minimal
chance (5% men; 8% women) of developing CVD
before their time of death (26). This finding dupli-
cates an identical result from the Multiple Risk Fac-
tor Intervention Trial and Nurses’ Heart Studies, but
unfortunately all too few individuals (< 5%) have
the correct risk-free profile to reduce their risks by
69–82% over 30 years (27,28). All of these studies
include large numbers of patients but have relatively
few events, so the statistical power of such risk pre-
diction algorithms is to rule out future disease, but
unfortunately they are often used in reverse to rule
individuals into therapy, as can be seen from any
national guidelines statement.
Over the years, the pressure to treat at lower risk
thresholds has been driven by recruitment of pro-
gressively lower risk population to drug studies,
which has led guideline committees to progressively
reduce the acceptable risk threshold. In the UK, this
has fallen from 30% ⁄ decade for coronary heart dis-
ease (CHD) (40–45% for CVD) to 20% cardiovascu-
lar disease (15% CHD risk ⁄ decade) between the
publication of the Joint British Societies guideline in
1998 (29) and the revised version in 2005 (30).
Although this gave a significant increase in sensitiv-
ity, it is bought at the cost of a major reduction
in specificity. Further complications are added when,
in good faith, risk factors that were not significant in
the original analyses are added back to deal with spe-
cific problems. Thus increments are added to risk
calculations to account for obesity, family history of
CHD (31) or ethnicity (32) but not in any consistent
manner between guidelines.
Over time, risk factor definitions and analytical
methods have changed. Thus, diabetes has now been
redefined at lower glucose levels and then excluded
altogether from the risk calculation; now it has
become clear that the Framingham risk calculator is
fundamentally flawed for this group where CVD risk
approximates that of normoglycaemic patients with
prior CHD because it grossly under-predicts risk in
The pressure
to treat at
lower risk
thresholds has
been driven by
recruitment of
progressively
lower risk
population to
drug studies
ª 2009 Blackwell Publishing Ltd Int J Clin Pract, July 2009, 63, 7, 989–996
990 Perspective
diabetes (33). The removal of diabetes from the risk
calculator has not been modelled, but given the
original data another factor would likely appear to
take its place – potentially body mass index or waist
circumference (34). Similarly, methods for mea-
suring lipids have changed: the reliability of high
density lipoprotein cholesterol (HDL-C) assays has
improved, but the consequences of these improve-
ments on risk screening have not been addressed and
the ‘standard’ method comparisons that would nor-
mally be carried out in routine pathology depart-
ments have not been performed satisfactorily (35).
The original manganese dextran assay for HDL-
cholesterol used in Framingham was subject to inter-
ference by triglycerides and was sensitive to collection
container levels of ethylene diamine-tetra-acetic acid
(EDTA) (the reference specimen type for determina-
tion of lipids). Modern methods using serum-separa-
tor tubes and a variety of techniques to precipitate
triglyceride-rich and LDL particles report higher
HDL-C levels with the bias ranging from 10 to 25%
(36). The effect of this is to significantly blunt the
risk attributable to lipids as the denominator in the
total cholesterol : HDL ratio is reduced.
The switch from mercury sphygmomanometer to
aneroid blood pressure measurement techniques
and the change in Korotkov sound phase also raise
problems of bias in the equations even if the meth-
ods are supposedly matched (37), Consequently,
Deming regression-correction term should be intro-
duced into the risk calculators and appropriate large
well distributed large samples used (38). Addition-
ally, the statistics behind the calculators assume that
all risk factors are independent. Given the clustering
of risk factors in the metabolic syndrome, the
assumption of independence is false meaning that
alternative statistical methods, perhaps factor analy-
sis, would be a preferable statistical technique to
identify key variables if there were any consensus
about how to do it.
The problems of risk factor redefinition and
uncontrolled assay modification pale into significance
when a closer view is taken of CVD risk equations.
All of the equations rely on exponents for individual
risk factors, and each exponent has a confidence
interval. Additionally, each measurement is subject
to recording bias and biological variation. Thus, ages
are approximated to 1 year (2.5% difference per year
at age 40 years); blood pressure measurements often
show digit bias and lipid and blood pressure mea-
surements also show a > 10% daily variation. Even
assuming a perfect statistical relationship to derive
the exponents, this leads to the 95% confidence
interval for risk for patients with a ‘true’ CVD risk
Table 1 Comparison of baseline variables required for CHD risk assessment
Risk factor
Framingham
PROCAM (20) SCORE (67) QRISK-2 (23)
Reynolds
(68,69) UKPDS (70)1991 (19) 1999 (71) 2007 (72)
Age Y (30–75) Y (30–75) Y (30–75) Y (35–65) Y (40–65) Y (35–75) Y (50–80) Y (25–65)
Gender Y Y Y Y Y Y Y Y
Smoking Y Y Y Y Y Y Y Y
SBP Y Y Y; BP treatment
added
Y Y Y; BP treatment
added
Y Y
LVH (ECG) Y N N N N N N N
DM Y Y Y Y N ⁄ A Y BMI added N ⁄ A Specific for NIDDM
Glycaemia (HbA1c)
eGFR N N N N N Chronic disease N N
TC : HDL Y Y Y HDL only Y Y Y Y
TG N N N Y N N N N
LDL-C N N N Y N N N N
CRP N N N N N N Y N
Family history
CHD
N (UK- post hoc) N N Y (< 65 years) N N Y (< 60 years) N
Ethnicity N (UK- post hoc) N (post hoc) N (post hoc) N (post hoc-
specific set)
N Integrated N Integrated
Deprivation N N N N N Y N N
Validation NHANES, WOSCOPS N N N N UK GP database N N
CRP, C-reactive protein – high sensitivity; DM, diabetes mellitus; eGFR, estimated glomerular filtration rate; LVH, left ventricular hypertrophy; NHANES, National
Health and Nutrition Survey; SBP, systolic blood pressure; TC, total; cholesterol; TG, triglycerides; WOSCOPS, West of Scotland coronary outcomes prevention study.
Type 2 diabetes is added as a variable in many calculators although this is not applied in practice.
ª 2009 Blackwell Publishing Ltd Int J Clin Pract, July 2009, 63, 7, 989–996
Perspective 991
of 20% being 14–26%, based on a typical single
determination of risk factors (39). Increasing the
number of measurements to three only reduces this
confidence interval to 16–24%.
Another misconception is that adding risk factors
adds to specificity, but it actually increases error faster,
leading to apparently increased sensitivity but signifi-
cantly reduced specificity. For logistical reasons, many
guidelines recommend the use of posttreatment blood
pressures (not validated in Framingham except for
stroke) or suggest even simpler stratification tech-
niques based on prior risk factor identification. Unfor-
tunately, risk calculation equations are not
commutative and even minor deviations using prior
classifications can lead to many patients being incor-
rectly assigned (40). Therefore, despite the appearance
of a strong scientific foundation, risk calculation is a
complex tool that needs to be used carefully.
The practicalities of screening
Many guidelines recommend that cardiovascular risk
screening should be conducted using a battery of risk
factors including demographics, blood pressure, lip-
ids, glucose, renal dysfunction and obesity in patients
aged > 40 years. If risk assessment is performed sys-
tematically, then 18% have identified CHD, risk
equivalents or have been already treated, 22–78% of
men and 6–74% of women have a CVD risk
> 20% ⁄ decade in the 10-year age ranges from 40 to
80 years (Figure 1) (41). However, they do not con-
sider the cost of such an approach given that the
likely expenditure for such screening is at least
£23.70 ⁄ patient (7). This may be cost effective in the
long-term, but is of no help in a financially chal-
lenged climate, as it implies a cost of millions of
pounds for each Primary Care Trust. An alternative
cheaper approach has to be found.
The first principle that can be used is the Bayesian
one of prior risk. Registers already exist for patients
with established disease. Age is the greatest risk fac-
tor and a vascular risk policy involves selection of an
age group to target. Patients with risk factors
identified as a result of opportunistic screening or
self-reported risk factors represent preexisting
investments (42) or simple additional data gathering
(24). Initial approaches should target the more
elderly people and then move on to younger age
groups (42). Patients with a family history of early
coronary heart disease or stroke also have a raised
prior probability with the effect increasing risk by
1.5–2.8-fold depending on the definition used (43),
and this approach would start to identify patients
with genetic hyperlipidaemias as well as other possi-
ble inflammatory causes of CVD. In addition, many
patients with early CHD have dyslipidaemia as an
underlying cause (44). Many cardiovascular risk fac-
tors cluster in the metabolic syndrome, and it is
notable that six of the nine risk factors in Inter-
HEART relate to this complex [diabetes, total choles-
terol : HDL ratio; central obesity, hypertension, low
levels of exercise and poor diet (low fruit and vegeta-
ble intake)] (1). Thus, identification of patients with
the metabolic syndrome or its components will raise
the chance of identifying patients with CVD. As the
metabolic syndrome is a better predictor of future
risk of diabetes than cardiovascular disease (45),
especially if the International Diabetes Federation
definition is used, this strategy will also identify
patients with a high risk of diabetes, a cardiovascular
0
10
20
30
40
50
60
70
80
18 – 29 30 – 39 40 – 49Age (years)
Pre
vela
nce
(%
)
Men 10 – 20% Men > 20% Women 10 – 20% Women > 20%
50 – 59 60 – 69 70 – 79 > 80
Figure 1 Prevalence of patients with differing degrees of vascular risk from the HEART-UK Unilever cardiovascular
screening study (41)
ª 2009 Blackwell Publishing Ltd Int J Clin Pract, July 2009, 63, 7, 989–996
992 Perspective
risk equivalent, and thus future CVD. It can also be
used to identify patients at future risk of hyperten-
sion if a family history of hypertension is available
(46). As not all obese patients display the metabolic
abnormalities of the metabolic syndrome (47),
maybe a metabolic syndrome score would be a better
input into the risk equations. It may be that as a
result of their reduced variance, chronic measures of
insulin resistance such as sex hormone binding glob-
ulin or long-term dysglycaemia such as HbA1c may
be better than the homeostasis index (HOMA),
which is based on highly variable measurement of
insulin and glucose. Studies of risk of diabetes or
cardiovascular disease suggest that models based
on age, gender, body mass index, family history of
diabetes and ethnicity with the addition of blood
glucose may have promise as simple methods of
approximation (48,49).
Looking for patients already identified by opportu-
nistic methods in primary care databases is likely to
yield a disproportionate number of future cases of
CVD (42). Thus, the priorities ought to be older
patients, men, those with obesity or hypertension
or those living in areas of deprivation. Based on
primary care data and modelled in the National
Institute for Health and Clinical Excellence guide-
line for lipid modification, this approach has been
proposed and found to be cost effective (50). The
main problem is that this neglects the 55% of risk
attributable to dyslipidaemia, but it should be noted
that at least some of the risks identified as being
because of lipids relate to low HDL-cholesterol,
which has a prevalence of 25% in primary preven-
tion populations and forms part of the metabolic
syndrome (51,52). In a risk algorithm derived from
the National Health and Nutrition Evaluation Survey
(NHANES), lipids and body mass index are inter-
changeable which is intriguing, especially given the
removal of diabetes from the Framingham algorithm
as currently applied (53). The efficacy of these strate-
gies is confirmed by the C-statistics of 0.78–0.83
on receiver–operator characteristic analysis although
concordances for identification are not stated (40).
It would appear therefore that the optimal strategy
would be to determine and record the following risk
factors:
• Age
• Gender
• Smoking
• Body mass index (or waist circumference)
• Blood pressure
• Family history of CVD prior to age 65 years
• Family history of diabetes
• Family history of hypertension
Opportunistic use could be made of previously
measured data on lipids and glucose or other risk
factors in any risk calculation. In patients with a
family history of early CHD, then risk testing for
cholesterol to identify potential cases of familial
hyperlipidaemia is warranted (44). The primary
objective of risk calculation would be to exclude
patients not requiring detailed review as methods
have a 90% negative predictive value (53). At a 15%
threshold (to allow for variances), this would likely
exclude 50–80% of men and 75–95% of women at
ages 40–60 years (41). It would also be a predomi-
nantly data-based exercise or only require brief cor-
respondence rather than formal clinical assessment
visits. These patients could be reassured that their
risk was likely to be low. In patients identified as
moderate risk (> 15%), the second stage would be to
conduct a more formal risk assessment including
biochemical analyses. For practical purposes, the fol-
lowing tests would give the best data:
• Total cholesterol : HDL ratio (non-fasting)
• Glucose (non-fasting)
• Creatinine
The cholesterol ratio shows a < 10% effect because
of fasting, while measurement of postprandial glyca-
emia is a better index for exclusion of frank diabetes,
as it captures the fraction of patients with a normal
fasting glucose but abnormal postprandial metabolism
in whom more specific tests should be conducted (54).
As blood pressure is already being measured and a low
estimated glomerular filtration rate (eGFR) is an inde-
pendent risk factor, the identification of higher risk
groups with early stages of renal dysfunction would
allow better targeting of therapy and might substitute
for the lack of measurement of electrocardiographical
evidence of target organ damage as recommended in
the original Framingham algorithm (55). Again the
principle is to exclude patients with risk < 15%. The
effectiveness of such a two-stage screening strategy has
never been modelled but may exclude another 10%.
The remaining individuals would be enriched in those
of high cardiovascular risk (> 20%) in whom defini-
tive tests are necessary. At the simplest level, this
involves fasting blood samples to repeat the factors
listed above, but it is well known that the risk algo-
rithms over-identify patients at potential risk. Consid-
eration should therefore be given to other methods of
risk stratification if these prove cost effective.
Further risk stratification strategies
The choices for tertiary risk stratification are varied. At
the simplest, the candidate indices include deprivation
Despite the
appearance of
a strong
scientific
foundation,
risk calculation
is a complex
tool that needs
to be used
carefully
ª 2009 Blackwell Publishing Ltd Int J Clin Pract, July 2009, 63, 7, 989–996
Perspective 993
and ethnicity, but these are known to have poor
specificity, and indeed may not be independent (56).
Other candidates include biochemical markers of
inflammation which have been validated in prospec-
tive studies for markers such as C-reactive protein or
lipoprotein-associated phospholipase A2 (LpPLA2);
markers of endothelial dysfunction such as asymmet-
ric dimethylarginine, flow-mediated dilation or pulse
wave velocity and radiographical markers of athero-
sclerosis burden, including carotid intima media
thickness and coronary calcium scores (57). Which
test is best is yet unclear as they all vary in utility
depending on whether they identify prior disease or
can also be used to track response to treatment. If
these tests can be delivered at low unit cost, they could
be used to determine exclusion of lower risk individu-
als from life-long treatment. These extra tests may also
function to reassure patients who are concerned about
the implications of isolated high levels of single risk
factors, which are not caused by genetic dyslipidae-
mias or may identify for treatment those with aggres-
sive primary hypertension and those who require
treatment because of their specific additional risks.
Evidence base for vascular screening
However, the most crucial question is how accept-
able would this degree of screening for asymptomatic
disease be? Almost all the data on population screen-
ing relate to the emotive field of cancer. Vascular
programmes although effective in the long-term
(9,58) show a lower response rate to invitations with
an uptake of 47% to postal communications with
repeated reinforcement (59), and in other studies
29% for postal self-assessment (60). Some diabetes
studies show better recruitment by telephone (61).
The uptake of screening for established atheroscle-
rotic disease showed pronounced reductions in effi-
ciency for men, the elderly people and lower
socioeconomic classes – the highest risk groups (62).
Rates of adherence to follow-up visits at 1-year fall
to 50% (60) even before considering the pronounced
(similar) reductions in drug adherence for asymp-
tomatic conditions (63,64).
In addition, randomised clinical trials are rare in
this field and decision making seems to be driven by
health economics rather than evidence. The original
study in the field was the South East London Screen-
ing study of 7229 patients in 1967 of patients aged
40–64 years who were randomised to screening or
usual care (65). Initially, 20% refused cardiovascular
screening, and after 5 years there were no significant
differences in mortality or admissions between the
two groups. However, few effective lipid lowering or
antihypertensive interventions were available at the
time, so the results of this study may mimic the pre-
dominantly lifestyle effects seen in the Multiple Risk
Factor Intervention Trial (MRFIT) of 256,000
patients in the USA (8). The Stockport study
screened 8607 patients, three on occasions between
1989 and 1999, having initially screened 50,788 indi-
viduals at entry to the study (58). After 10 years,
smoking rates fell by 30% in high-risk patients, sys-
tolic blood pressure fell by 10 mmHg in high-risk
individuals (> 150 mmHg) compared with a
5 mmHg rise in lower risk, and cholesterol by 0.5–
0.7 mmol ⁄ l in high risk (> 6.5 mmol ⁄ l) compared
with a 0.2 mmol ⁄ l rise in lower risk individuals.
Unfortunately, no cardiovascular event data are
available for this study. More recently, the Sandwell
study randomised 11,901 patients in four primary
care practices compared with 8515 controls in two
practices to screening or opportunistic detection
(59). Although cardiovascular end-points are absent
from this study, cardiovascular risk estimation rates
doubled (61.9% vs. 27.9%) and so did rates of aspi-
rin (45.7% vs. 19.2%), antihypertensive (28.4% vs.
12.5%) and statin (49.0% vs. 21.6%) prescribing.
Data are awaited in whether cardiovascular event
benefits will follow from this intervention. Results
from the national familial hypercholesterolaemia
(FH) screening project in the Netherlands suggest
that it should be possible to identify the benefits of
risk screening on cardiovascular events, as FH
screening promotes improved treatment and has
demonstrated a 76% reduction in mortality with
linked screening and intervention approach (66).
Conclusions
Cardiovascular screening looks to be possible given
the existence of epidemiologically derived risk calcu-
lators and to have favourable health economics. Yet,
in practice, there may be considerable barriers to the
implementation of systematic screening, however,
useful in public health terms. There is an absence of
recent long-term clinical end-point as opposed to
surrogate marker ata, but the historical data are not
reassuring. There is a need for proper clinical trials
in this field.
Most cardiovascular disease occurs in patients at
low risk simply because the population with that
degree of risk is large which translates into a large
number of events overall even though the risk in any
individual is low. All a targeted risk approach can do
is to attempt to reduce the risk in the small popula-
tion at relatively high risk. Thus, only public health
interventions to promote healthier lifestyles, improve
diets, reduce smoking and reverse obesity will change
morbidity significantly overall, as this approach
How acceptable
would this
degree of
screening for
asymptomatic
disease be?
ª 2009 Blackwell Publishing Ltd Int J Clin Pract, July 2009, 63, 7, 989–996
994 Perspective
reduces the risk factor burden by small amounts in
large numbers of people. Unfortunately, these
approaches are slow to take effect, are relatively
unglamorous and although the principles are well
known, the political temptation to a quick fix solu-
tion for cardiovascular disease through risk screening
with neglect of longer term approaches is very allur-
ing. Expensive unproven interventions should be
resisted until proper clinical trials are performed,
and policymakers need to remember that only
changes in public health will deliver the long-term
outcomes to lower risk individuals.
Disclosure
Dr Wierzbicki is a member of the South East Lon-
don Cardiac Network group on cardiovascular dis-
ease prevention.
A. S. Wierzbicki,1 T. M. Reynolds2
1Consultant in Metabolic Medicine andChemical Pathology,
St. Thomas’ Hospital,London SE1 7EH, UK
2Professor of Chemical Pathology,Queen’s Hospital, Belvedere Road,
Burton-on-Trent, Staffordshire DE13 0RB, UKEmail: [email protected]
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