Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study
BMJ 2007; 335 doi: https://doi.org/10.1136/bmj.39261.471806.55 (Published 19 July 2007) Cite this as: BMJ 2007;335:136
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Readers may be interested to know that the performance of QRISK
compared with Framingham has been evaluated in the THIN database - a
sample of about 1.1 million primary care patients from practices that use
the In Practice Software system. The paper has just been published in
Heart and can be found at:
http://heart.bmj.com/cgi/rapidpdf/hrt.2007.134890v2?
Competing interests:
I am a co-author of QRISK
Competing interests: No competing interests
With the Department of Health moving UK Primary Care into population
CVD risk stratification, any tool that allows practitioners to focus
resource to need is valued. Modelling of potential savings if this tool
were adopted for this process would be welcomed.
On a practical level within the consultation, the tool would be most
valuably integrated within the practice based clinical system, so that
deprivation indices down to a postcode or individual level could be used
rather than practice aggregates.
Competing interests:
None declared
Competing interests: No competing interests
Dear Sir
We congratulate Julia Hippisley-Cox and colleagues on this important and
innovative article and have followed with interest the subsequent
responses regarding the derivation and validation of a new risk score –
QRISK.(1) One aspect of QRISK that has not been discussed is the use of
such a score in daily practice. There are broadly two ways in which
scores such as this or the Framingham equation are likely to be used –
firstly population screening using a practice clinical system to trawl for
individuals likely to be at high risk of cardiovascular disease and
secondly on an individual basis where a practitioner and his or her
patient decide on the most appropriate form of primary prevention.
In the former case, any risk estimation tool needs to take into
account likely missing data and provide a reasonable assessment of risk
prior to calling up an individual for the latter consultation and
accurately assessing risk with a full dataset of risk factors from
history, examination and investigation. QRISK has been developed and
validated on a dataset that provides good evidence of its utility in the
case of the kind of data routinely available in EMIS practices and we
agree with Macleod et al that this is likely to be similar in practices
using other clinical systems.(2)
However, in the case of an individual for whom full information is
available about current risk factors (blood pressure, cholesterol/HDL
ratio etc) we do not believe that the superiority of QRISK has been
proven. This is exemplified by the issue of cholesterol/HDL ratio for
which data were available on only approximately 30% of the cohort and
which despite recalculation of the model may still be undervalued as a
risk factor by QRISK.(3)(4) Furthermore, QRISK used the first measurement
of each risk factor available after the age of 35 for each individual but
it seems more appropriate to use currently measured risk factors to assess
current risk in the setting of a face to face consultation.
Whilst QRISK appears an excellent tool for a strategy of population
level CVD screening using routinely collected clinical data, it is likely
that other scores derived from complete measurements (such as Framingham
or ASSIGN) will be more appropriate for use with individual patients.
Yours faithfully
Dr Richard McManus & Dr Jonathan Mant
References
1 Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, May M, Brindle P.
Derivation and validation of QRISK, a new cardiovascular diseases risk
score for the United Kingdom: a prospective open cohort study. BMJ
2007;335: 136-141.
2 Macleod J, Metcalfe C, Davey Smith G. QRISK and Health Inequalities.
BMJ Rapid response. http://www.bmj.com/cgi/eletters/335/7611/136
[accessed 21 8 2007]
3 Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, May M, Brindle P.
Authors Response. BMJ rapid response.
http://www.bmj.com/cgi/eletters/335/7611/136 [accessed 21 8 2007]
4 Ingelsson E, Schaefer EJ, Contois JH, McNamara JR, Sullivan L, Keyes
MJ, Pencina MJ, Schoonmaker C, Wilson PW, D'Agostino RB, Vasan RS.
Clinical utility of different lipid measures for prediction of coronary
heart disease in men and women. JAMA. 2007 Aug 15;298(7):776-85.
Competing interests:
None declared
Competing interests: No competing interests
As other correspondents have noted1, the published results
describing the QRISK prediction model for cardiovascular
risk2 indicated that cardiovascular risk is unrelated to
cholesterol level (coded as the ratio of total to HDL
cholesterol). This result cast doubt on the validity
of the reported analyses. The analysis was performed using the method
of multiple imputation to allow for missing data. We believe that
flawed application of the method caused the anomalous
findings on cholesterol. The authors have subsequently clarified that
when their analysis was performed in the usual way by restricting to
individuals with complete information (no missing data) there was a
clear association between cholesterol and cardiovascular
risk. Furthermore, a similar result appears to have been obtained
after using a revised, improved, imputation procedure3.
Briefly, multiple imputation aims to allow for the uncertainty about
missing data by creating multiple copies of the dataset under analysis
in which the missing values are replaced by imputed values drawn using
a statistical prediction based on the observed data. Standard
statistical analyses are performed on each of the imputed datasets and
the results are combined in an appropriate way to obtain final
conclusions. Valid inferences are obtained because the method accounts
for the distribution of the missing data, under the technical
assumption that the data are Missing at Random
(MAR)4.
There is clear potential for multiple imputation to improve the
validity of medical research. However, the validity of results depends
on the imputation modelling being done carefully and appropriately,
and it is not possible to automate the procedure. A number of pitfalls
can arise. In particular, when an analysis explores the association
between one or more predictors and an outcome, but some of the
predictors have missing values, the outcome carries information about
the missing values of the predictors, and this information must be
used in the imputation procedure5. For example, when
imputing missing cholesterol values, individuals who go on to develop
cardiovascular disease should have larger imputed cholesterol values,
on average, than those who remain disease-free. Failure to allow for
the cardiovascular outcome in imputing the missing cholesterol values
would falsely weaken the association between cholesterol and
cardiovascular disease, as appears to have happened in the QRISK analysis.
In a separate longer article that we have submitted for publication,
we provide a brief non-technical review of the reasons that missing
data may lead to bias in epidemiological and clinical research, and
discuss the circumstances in which multiple imputation may reduce such
biases. We suggest guidelines for the conduct and reporting of
analyses based on multiple imputation, which aim to enhance the
benefits that it potentially offers while reducing the risk of errors
in its application.
- http://www.bmj.com/cgi/eletters/335/7611/136#172067,
accessed 30 July 2007. - 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. British Medical Journal 2007; page bmj.39261.471806.55.
- http://www.bmj.com/cgi/eletters/335/7611/136#174181,
accessed 8 August 2007. - Rubin DB. Multiple imputation for nonresponse in surveys. John Wiley and Sons; New York; 1987.
- Moons K.G., Donders R.A., Stijnen, T., Harrell, F.E. Using the outcome
for imputation of missing predictor values was
preferred. Journal of Clinical Epidemiology 2006; 59:
1092-1101.
Competing interests:
None declared
Competing interests: No competing interests
Dear Editor
Re: Derivation and validation of QRISK, a new cardiovascular disease
risk score for the United Kingdom: prospective open cohort study.
Thank you for giving us the opportunity to respond to letters on
QRISK. We have tried to address all the substantive points in full and
also present additional analyses as requested.
1.1 Additional analyses on statin usage and cholesterol
Milne, Lewis, Simpson and Cooney all raise concerns about the
inclusion of patients on statins in our analysis and ask whether this
might have biased our results. Peto, Cooney and Tunstall-Pedoe all
highlight the low hazard ratio for cholesterol ratio. Several people also
contacted us individually to request additional analyses to investigate
this in more detail. We found the discussions helpful and have now
conducted those analyses.
Of the 1,283,174 patients in the original derivation cohort, only
14,005 patients (1.09%) had already been prescribed statins at baseline.
After removing these patients from the cohort, there were a further
118,009 (9.3%) patients who were prescribed statins during the ten year
study period. However the vast majority of these patients started
treatment towards the end of the observation period. For example, of the
total 8,182,404 person years of observation, only 256,387 person years
(3.13%) were exposed to statin treatment. This means that more than 96% of
our study observation time was uncontaminated by statin usage and so our
population was largely statin naïve.
Table 1 shows the adjusted hazard ratios for model A (the additional
models to appear in the BMJ print version or from julia.hippisley-
cox@nottingham.ac.uk)
• Model A includes all patients apart from those prescribed statins
at baseline. Patients who began statins during follow up are included.
This is our preferred model.
• Model B is the same as model A except it adjusts for statin usage
as a time varying covariate. The issue with this approach is how such a
model with time varying covariates could be applied to a real prospective
population.
• Model C is the same as model A except the outcome is coronary heart
disease
• Model D is the same as model A except that the outcome is
stroke/TIA
Table: Comparison of hazard ratios for cardiovascular disease using four different models. All models exclude statin users at baseline.
We have improved our implementation of multiple imputation for
missing data. Our revised model for imputation includes more variables in
addition to those originally included. We have added binary variables for
diagnosis of hypertension and incident diabetes and continuous variables
for the number of prescriptions for aspirin, statins and antihypertensive
treatments for each patient during the study period. When the revised
imputed datasets were used in the main analysis, the estimated hazard
ratios for the cholesterol ratio term in males and females increased to
more biologically plausible values given our combined outcome of
cardiovascular disease. We think this addresses the concerns raised by
Peto, Cooney and Tunstall-Pedoe and improves the analysis. Multiple
imputation has rarely been used in derivation of risk scores and has a
distinct advantage in terms of using a larger and more representative
sample than the analysis of only those subjects who had complete records
of all data including serum cholesterol. The adjusted hazard ratios for
the ratio of cholesterol/HDL in the complete case analysis was 1.20 (95%
CI 1.17 to 1.22) in females and 1.25 (95% CI 1.23 to 1.27) in males. The
analysis using only subjects with complete records is available from the
authors on request.
There was little difference between models for any of the hazard
ratios for cholesterol/HDL ratio for males or females which suggests that
use of statins has not significantly biased our hazard ratios. Models D
shows the hazard ratios for coronary heart disease as an outcome and
demonstrates that these are broadly similar to those obtained in other
studies for prediction of coronary heart disease mortality1 including
Framingham. The calibration statistics and the predictions of those at
risk by age, sex and deprivation were very similar to those in our
original analysis (results available from the authors). Overall, the
exposure to statin was low (3% of the person years) and the results from
each of the different models are very similar suggesting that this is not
an important source of bias in our analysis. Model A is our preferred
final model which will be used in our risk calculator http://www.qrisk.org
1.2 Discrimination
In their own research MacLeod et al. found that the addition of
material deprivation didn’t improve discrimination of Framingham and
consider the ROC curve areas for QRISK and Framingham are similar.
Romanens and Ferns make the same point. However, the deficiencies of the
ROC statistic are well known2 which is why we presented the D statistic
which is more appropriate for use with survival data3. This does
demonstrate a significant improvement for QRISK compared with the other
scores.
The performance of a risk prediction equation will always be specific
to contexts of time, place and means and definitions of data collection
and outcomes. Our aim is to provide a means to equitably implement a
cardiovascular disease prevention programme in the context of contemporary
UK practice, rather than to elucidate the aetiology of cardiovascular
disease. QRISK gives us a means for deriving, implementing and if
necessary updating an ‘in-house’ equation based on general practitioners
contemporary data which is at least as good if not better than Framingham
in it’s discrimination and better calibrated to identify major
cardiovascular events in the United Kingdom. It will also address problems
with Framingham which under- estimates risk in people from deprived
backgrounds4 5, those on anti-hypertensive treatment and people with
positive family histories.
1.3 Equity and over-prediction
Lewis has misunderstood – we modelled Townsend scores as a continuous
variable in the risk score and only presented the results by fifth for
ease of interpretation. We used area based deprivation scores derived at
output area (125 households) since there is no generally accepted and
reliably recorded individual measure of deprivation.
MacLeod et al. consider that over-prediction by Framingham is not a
problem because the inclusion of more people is likely to result in more
benefit than harm. The bar for intervention has been fixed nationally at a
20% 10 year cardiovascular disease risk by the statin Health Technology
Assessment http://guidance.nice.org.uk/TA94#documents . This threshold
effectively sets the additional national resource (workload, advice,
treatment) that will be allocated to people at increased risk. The issue
becomes how best to allocate this resource on an equitable basis taking
account of health needs – in this case indicated by cardiovascular risk –
which is determined by age, sex, blood pressure and social deprivation
amongst other things. We agree all new technologies run the risk of
exacerbating relative inequalities, but this is not a reason for not
seeking to reduce this by our social interventions.
We disagree with Macleod et al who consider that simply including
more people, including socially deprived men, will reduce inequality.
Equity is a relative not an absolute issue. In the most deprived compared
to the least deprived fifth of social deprivation, there is a three-fold
increase in the proportion of women identified at high risk and a 30%
excess among men. In the most affluent fifth, QRISK indicates that
Framingham overestimates risk by a factor of 2. These are substantial and
important differences particularly for women and they will be to some
extent ameliorated by a prevention programme driven by QRISK but are
likely to be further exacerbated if Framingham is retained. QRISK ensures
a more equitable allocation of the national resource than Framingham.
QRISK prioritises the more socially deprived, particularly women, older
ages and people with on antihypertensive treatment more equitably than
Framingham.
1.4 Home advantage
We agree with Macleod et al that QRISK has a home advantage but one
which is likely to be shared by other EMIS practices which together cover
60% of the practices in England and Wales. If QRISK performs as well in a
similar database from another major computer system (THIN) it will
demonstrate generalisability to most of the national population. MacLeod
et al suggest that THIN is also home territory, it is, but only in the
sense that primary care is the very territory that a cardiovascular
disease risk score is meant to be used in.
1.5 Scotland
We included ASSIGN in our analysis as its use of social deprivation
in risk estimation led us to consider whether it was suitable for use in
England and Wales. The reason why ASSIGN may overestimate risk in England
and Wales is because the Scottish ASSIGN cohort represents a higher risk
population. It had a mean serum cholesterol of 6.2mmols/l which is
higher than the mean of 5.4mmols/l in the Health Survey for England and
Wales (1998) and 5.5mmols/l in the Health Survey for Scotland (1998) and
5.7mmols/l in the QResearch dataset. Similarly the proportion of smokers
was much higher in ASSIGN (42.4% of men) compared to 34% and 30%
respectively in the Health Surveys for England and Scotland and 28% for
QResearch. ASSIGN may well be representative of the Scottish Heart Health
Extended Cohort population – a cohort recruited in the 1980’s because
Scotland had particularly high cardiovascular mortality rates. However it
may not represent the whole population of modern-day Scotland since most
of the participants came from urban areas of North Glasgow and Edinburgh,
with baseline measurements collected between 1986-2001 compared with 1995
to 2007 in our study.
Acknowledgements
We thank Professors Richard Peto and Patrick Royston for advice on
additional analyses presented here and their interpretation.
References
1. Diverse Populations Collaborative Group. Prediction of mortality
from coronary heart disease among diverse populations: is there a common
predictive function? Heart 2002;88:222-228.
2. Cook N. Use and Misuse of the Receiver Operating Characteristic Curve
in Risk Prediction. Circulation 2007;115:928-935.
3. Royston P, Sauerbrei W. A new measure of prognostic separation in
survival data. Statistics in Medicine 2004;23:723-748.
4. Brindle P, McConnachie A, Upton M, Hart C, Davey-Smith G, Watt G. The
accuracy of the Framingham risk-score in different socioeconomic groups: a
prospective study. British Journal of General Practice 2005;55(520):838-
45.
5. Tunstall-Pedoe HW, M for the SIGN group on risk estimation. By
neglecting deprivation, cardiovascular risk scoring will exacerbate social
gradients in disease. Heart 2006;92:307-310.
Competing interests:
The authors of the original paper
Competing interests: No competing interests
Hippisley-Cox has recast cardiovascular risk assessment but has
ignored one of the strongest and most easily measured items. In Australian
data the waist-hip ratio is a much stronger predictor of cardiovascular
mortality or total mortality than BMI. This was shown in 11 year survival
follow up of the 1989 risk factor prevalence survey. The hazard ratio for
a 1 standard deviation increase in BMI was 1.23 while the same measure for
waist-hip ratio in men was 1.63.
It will be fascinating when girth or waist-hip ratio is included in
cardiovascular prediction models to see which other risk factors have
reduced hazard ratios.
Reference
Timothy A Welborn, Satvinder S Dhaliwal and Stanley A Bennett. Waist–hip
ratio is the dominant risk factor predicting cardiovascular death in
Australia. MJA 2003; 179 (11/12): 580-585
Competing interests:
None declared
Competing interests: No competing interests
Although the use of ROC analysis has been criticised as a means for
assessing the utility of models that predict future risk, or stratify
individuals into risk categories(1), it is clear that applying QRISK (2)
does not greatly improve the discrimination of individuals who will or
will not develop cardiovascular disease. The marginal improvement in the
ROC statistic still leaves more than 20% of patients in the wrong
category. There appears to be an urgent need for more direct measures of
vascular health, perhaps coronary calcium score (3), before initiating
life-long treatment with statins, or other agents unnecessarily.
1. Cook NR. Use and misuse of the receiver operating characteristic curve
in risk prediction. Circulation 2007;115:928-35.
2. Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, May M, Brindle P.
Derivation and vlidation of QRISK, a new cardiovascular diseases risk
score for the United Kingdom: a prospective open cohort study. BMJ
2007;335: 136-141.
3. Greenland P, LaBree L, Azen SP, Doherty TM, Detrano RC. Coronary artery
calcium score combined with Framingham score for risk prediction in
asymptomatic individuals. Journal of the American Medical Association
2004;291:210-5.
Competing interests:
None declared
Competing interests: No competing interests
The effect of socio-economic deprivation on cardiovascular risk has
not been explained by differences in the prevalence of risk factors
traditionally used to estimate a person’s risk of developing
cardiovascular disease1. Two new risk scores, ASSIGN and QRISK, have
recently been developed to include measures of deprivation 2 3 in an
attempt to improve the prediction of risk using such scoring systems. One
potential factor contributing to the value of risk scores including
deprivation is that current smoking status (used in conventional scoring
systems) may not adequately reflect differences in lifelong smoking habit
in people of different socio-economic status.
We have therefore explored whether levels of lifelong smoking differ
by socio-economic status among current smokers. Analyses were based on
data for 3350 people aged 50-75 years participating in a primary
prevention trial of aspirin for asymptomatic atherosclerosis who had no
history of clinical cardiovascular disease but who were at moderately
increased risk of cardiovascular disease with an ankle brachial index
(ratio of systolic blood pressure in the ankle to that in the arm) of 0.95
or less4. Socio-economic status was assigned on the basis of postcode and
was defined by quintiles of the Scottish Index of Multiple Deprivation
(SIMD. SIMD is derived from Census data on income, employment, housing,
health, education and skills/training/access to services and
telecommunications 5. We used linear regression modelling to investigate
whether lifelong exposure to smoking as measured by pack-years of smoking
(number of cigarettes smoked per year multiplied by years of smoking/20)
varied by deprivation quintile after adjusting for age. Data for pack
years of smoking were square-root transformed in the analysis.
The mean age of the population studied was 62 years; 22% were in the
least deprived national quintile for deprivation and 27% were in the most
deprived quintile. Prevalence of current smoking increased from 13% for
the least deprived quintile to 36% to the most deprived quintile. Among
current smokers age-adjusted pack years of smoking increased from 30 to 34
(p=0.029 for trend) for the least to the most deprived quintile.
These findings suggest that current smoking status does not
adequately reflect differences in lifelong exposure to cigarette smoking
associated with deprivation. Risk scores such as the Framingham risk score
6 that only include current risk factor status may therefore not identify
the cardiovascular risk associated with deprivation appropriately 3;7;8.
As a consequence making treatment decisions based on scores that do not
include deprivation may result in a widening of health inequalities. Our
results support the importance of validating and refining scores that
include deprivation to address inequalities in risk of cardiovascular
disease.
Reference List
1. van Rossum CT, Shipley MJ, van de MH, Grobbee DE, Marmot MG.
Employment grade differences in cause specific mortality. A 25 year follow
up of civil servants from the first Whitehall study. J.Epidemiol.Community
Health 2000;54:178-84.
2. Woodward M, Brindle P, Tunstall-Pedoe H. Adding social
deprivation and family history to cardiovascular risk assessment: the
ASSIGN score from the Scottish Heart Health Extended Cohort (SHHEC). Heart
2007;93:172-6.
3. 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.
4. Heald CL, Fowkes FG, Murray GD, Price JF. Risk of mortality and
cardiovascular disease associated with the ankle-brachial index:
Systematic review. Atherosclerosis 2006;189:61-9.
5. Office of the Chief Statistician. Scottish Index of Multiple
Deprivation 2004: Summary Technical Report. 2004. Edinburgh, Scottish
Executive.
6. Anderson KM, Odell PM, Wilson PW, Kannel WB. Cardiovascular
disease risk profiles. Am.Heart J. 1991;121:293-8.
7. Brindle PM, McConnachie A, Upton MN, Hart CL, Davey SG, Watt GC.
The accuracy of the Framingham risk-score in different socioeconomic
groups: a prospective study. Br.J.Gen.Pract. 2005;55:838-45.
8. Tunstall-Pedoe H,.Woodward M. By neglecting deprivation,
cardiovascular risk scoring will exacerbate social gradients in disease.
Heart 2006;92:307-10.
Competing interests:
None declared
Competing interests: No competing interests
Hippisley-Cox et al recently described a new risk estimation system,
QRISK, which was derived from and tested on a UK population [1]. They
demonstrated superior performance in terms of the estimation of absolute
risk when the system was tested in the validation cohort, compared to
Framingham [2] and ASSIGN [3]. Given, as the authors acknowledge, that the
validation cohort came from the same population as the derivation cohort,
this is perhaps not surprising.
We acknowledge the obvious merits of the project, particularly the
very substantial amount of recent data used, the diversity of those
included in terms of ethnicity and social background and the demonstration
of the important risk gradient associated with decreasing social class.
However, we have noted several methodological limitations which may have
caused inconsistencies and may lead to difficulty when applying the risk
function to other populations.
Firstly, QRISK is not based on a cohort study with randomly selected
participants; rather the participants in QRISK were patients of selected
GPs, so its representativeness of the total UK population is uncertain.
Although mean risk factor levels correlate well with those of the British
population in 1995, the correlation with mortality and event rates for the
general British population at this time was not described. The risk
factors were measured at varying times relative to the date of study entry
and not specifically for the purposes of the study. No information is
given on standardisation of measurement of the risk factors. This is in
contrast to SCORE [4], and many other risk estimation systems.
Importantly, a very substantial number of the participants do not have
information on risk factor levels, for example, total cholesterol
(TC)/high density lipoprotein (HDL) cholesterol ratio was not available in
approximately 70% of the sample.
Secondly, the hazard ratio for total cholesterol/high density
lipoprotein cholesterol is completely inconsistent with numerous previous
studies. It is reported as 1.001 for each increase of 1 unit in men and
women, with confidence intervals including 1 in each. According to these
figures, the hazard ratio associated with a substantial increase in TC/HDL
from 2 to 10 would be 1.005, which is trivial regardless of the baseline
risk. It is possible that part of the reason for this trivial hazard ratio
is the method used to deal with the problem of missing data. For
approximately 70% of participants the TC/HDL levels were calculated by a
computerised regression technique based on values of other predictor
variables, which was repeated 5 times. This may explain why certain other
variables for example, body mass index and level of social deprivation
have assumed a greater importance relative to cholesterol measures.
Lastly, the authors indicated a significant difference between the
survival rate in those with measured TC/HDL ratio and the survival rate in
those with missing data. (10 year observed risks of CV event were 4.0% and
4.9% in women and men respectively who had a recorded TC/HDL ratio
compared to 7.9% and 10.9% for women and men who did not have a recorded
TC/HDL ratio.) No information was given on the usage of statins in the
population. Presumably GPs are more likely to measure total
cholesterol/HDL ratio in patients being considered for or already
receiving statin therapy. Therefore, given that the ~70% of participants
without a measured TC/HDL ratio were assigned the age-band and gender
specific mean value from the derivation dataset , the values of TC/HDL
assigned to those without a recorded value in the validation dataset were
definitely those of a much lower risk group and were possibly on-treatment
values. It may be reasonable that in a population in which ~70% of
participants have been given such an artificial value for TC/HDL a risk
estimation system in which changes in TC/HDL ratio cause a negligible
effect on risk would function well. However, how well such a system would
estimate risk in people with measured values of TC/HDL ratio is unknown.
The results of the second validation may provide some useful information.
1. Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, May M,
Brindle P. Derivation and valiation of QRISK, a new cardiovascular disease
risk score for the United Kingdom: prospective open cohort study. BMJ
2007: 10.1136/bmj.39261.471806.55.
2. Anderson K, Wilson PW, Odell PM, Kannel WB. An updated coronary
risk profile. A statement for health professionals. Circulation
1991;83:356-362.
3. Woodward M, Brindle P, Tunstall-Pedoe H, for the SIGN group on
risk estimation. Adding social deprivation and family history to
cardiovascular risk assessment: the ASSIGN score from the Scottish Heart
Health Extended Cohort (SHHEC). Heart 2007;93:172–176.
4. Conroy R, Pyörälä K, Fitzgerald AP, Sans S, Menotti A, De Backer
G, De Bacquer D, Ducimetière P, Jousilahti P, Keil U, Njølstad I, Oganov
RG, Thomsen T, Tunstall-Pedoe H, Tverdal A, Wedel H, Whincup P, Wilhelmsen
L, Graham IM; SCORE project group. Estimation of ten-year risk of fatal
cardiovascular disease in Europe: the SCORE project. Eur heart J
2003;24:987-1003.
Competing interests:
None declared
Competing interests: No competing interests
Are risk calculators answering the wrong question?
Qrisk in common with the other risk calculators calculates an
individuals 10 year risk of cardiovascular disease. In all these
algorithms the biggest determinate of the risk is the age of the patient.
The 10 year risk of a 35 year old patient is much lower than the 10 year
risk of a 75 year old patient with otherwise identical risk factors.
NICE says that patient with a 10 year risk of above 20 % should get a
statin. This means that large numbers of elderly patients get a statin but
very few patients in their 30s or 40s get a statin. Is this logical?
We generally give statin therapy for life. While, we do not know what
happens if you take a statin for decades, it seems likely that the longer
you take the statin the more you will benefit. The recently published long
term follow up of the WOSCOPS study supports the long term benefits of
taking a a statin.
Conversely, if a patient is never going to develop CHD then they are
not going to benefit from taking a statin.
We need to know what an individual’s lifetime risk of CHD is, not
their 10 year risk. We then need to know what happens, if those patients
at high risk, take statins for several decades.
Competing interests:
I have received lecture fees and educational sponsorship from several statin manufacturers
Competing interests: No competing interests