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Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2

BMJ 2008; 336 doi: (Published 26 June 2008) Cite this as: BMJ 2008;336:1475

Cardiovascular risk prediction and ethnicity: QRISK2 strengthens creaky foundations

Editor, Hippisley Cox et al(1), and Christiaens (2), have published important empirical data and conceptual reasoning, respectively, in relation to cardiovascular risk prediction. Hippisley Cox have strengthened the creaky foundations of risk prediction in relation to ethnic minority groups, but their vision is that the challenges are mathematical. By contrast, Christiaens questions the emphasis on absolute risk reduction, which favours older people, and suggests using relative risks within each age group-an approach which is, arguably, ageist. Very recently, the BMJ published Cooper and O’Flynn’s summary of NICE Guidance on risk assessment of cardiovascular disease, which places considerable emphasis on combining mathematically predicted risk and clinical judgement(3).

Before turning to the specifics of the new published papers, I would like to emphasise some basic principles, building on my recent rapid response to Cooper and O'Flynn ( Risk prediction tools are developed in populations and the outcome is a probability between 0% and 100%. As I have discussed recently, epidemiological data apply extremely well to populations, although some refinement is required when extrapolating from one population to another.(4) Disease occurs in individuals and is either 0% or 100%. Population data are not directly applicable to individuals, so it is not surprising that risk prediction is poor at the individual level. Conceptually, there is a mismatch between the clinical need of individual patients, and the available data. The sciences of individuals-genetics, toxicology, pathology, etc-need to realise the promise of personalised medicine so we can predict on individuals based upon their own data. Clinical medicine has become over- reliant upon epidemiology.

This fundamental limitation is a strong argument for clinical judgement, and individual clinical testing, following the screening test that risk prediction actually provides. Unfortunately, the screening test has become the definitive test-the tail is wagging the dog. Doctors have to decide whether the population derived risk is pertinent to the individual. The South Asian Health Foundation ( convened a conference in 2006 to review risk assessment modelling. The conference concluded that modelling is a screening tool requiring considerable clinical judgement before applying results to individuals, and this particularly applied to South Asians. . Even more clinical judgement is required in regard to ethnic minority and other populations where cardiovascular cohort studies are sparse(5).

The NICE guidance suggests the estimated risk for South Asian men is multiplied by 1.4. Hippisley Cox et al provide new data and new methods that improves on this pragmatic approach, but still need improvement. Contrary, to the Hippisley Cox et al's claim, theirs is not what we generally understand to be a prospective cohort study, and possibly it would be better described as a retrospective cohort. One of the problems these designs face is that the researchers are not in control of the measurement of exposure variables at baseline. One of the key variables is ethnicity.

Terminology for ethnic groups has been changing rapidly in the UK since 1993, when entry into this cohort started. The importance attached to collecting ethnicity data in the NHS has also shifted from apathy in 1993 to moderate interest now. The proportion of people with valid ethnic group codes in Hippisley Cox et al's study is, unsurprisingly, small. Population selection biases are almost inevitable. Firstly, that the Read codes used for ethnicity were only introduced around about 2002. Prior to that, a variety of Read codes were (and still are) used, comprising of a mixture of racial and ethnicity terms, that were not in accord with UK census categories ( Presumably, ethnic status was described using the Read codes given in the paper. If so, ethnic minority groups would be recent entrants to the cohort (around about 2002 and onwards), while others were entered throughout the period of the study. In diseases where the incidence and case fatality is changing rapidly, as for cardiovascular diseases, this will create period (cohort) effects. Secondly, repeat attenders at the practice are more likely to have an ethnic code, particularly if they are also being entered into a chronic disease register, either because they have the disease, a relevant comorbidity (such as diabetes or impaired glucose tolerance), or relevant risk factors. So, higher risk people are more likely to have ethnic codes. Since people without a specific ethnic group codes were placed into the White category, the potential bias is self-evident.

The authors acknowledge ethnic group missclassification, but conclude that this would reduce the size of apparent ethnic group differentials. We cannot be complacent about missclassification. It is very difficult to demonstrate that missclassification is nondifferential, and this is unlikely as illustrated above. Even if it is, missclassification in confounding variables can produce substantial spurious associations reflected in surprisingly high relative risks. The diabetes prevalence in the White reference group is very low indeed, and we would expect 3 or 4%, and higher if some of these people are non-White minorities. This is one potential indicator of the impact of period effects and missclassification.

Several researchers have been applying for funding for multi-ethnic cardiovascular cohort studies for many years, including myself. Fortunately, a very large cardiovascular cohort study has now been assembled, in Southall-it is called Lollipop, and is publishing cross- sectional data already-Professor Jaspal Kooner is the principal investigator.(6) We can foresee prospective analyses in a few years time.

The authors have proposed that their database be linked to the 2001 census to extract ethnic codes (and possibly other information). This approach has been implemented in Scotland.(7) In Fischbacher et al's study of myocardial infarction in South Asians compared to non-South Asians there was a marked variation in incidence-about 60% excess in men, and about 80% in women. These excesses are commensurate with those reported by Hippisley Cox et al.

The idea that South Asians have an excess of 40% CVD is, of course, simplistic. First, this figure only applies to CHD, not stroke, where the excess of mortality is about 100%. Second, mortality data, most recently examined by Wild et al(8) show excesses almost exactly in line with those of Hippisley Cox et al. In Bhopal et al's predictions the Framingham equations were fairly good for CHD but very poor for stroke.(9) In developing and testing prediction tools it might be better to work with each of these outcomes.

How do we apply these kind of findings in clinical practice? We have recently reported on a clinic and community-based cardiovascular disease risk factor control programme in Scotland.– Khush Dil.(10) To our knowledge, no other cardiovascular prevention project has reported outcomes in South Asians. The project doubled the calculated Framington risk prediction. Why so? This was guided by 1991 England and Wales mortality data, showing that the SMR for CHD in men was 142 in Indians, 148 in Pakistani and 151 in Bangladeshi and even higher for stroke e.g. 281 for Bangladeshi men.(11) Risk prediction tools, however, did not show such excesses. This decision was vindicated by the even higher inequalities in 2001 mortality data (8) and data from Fischbacher et al's linkage study.(7) The multifaceted intervention of Khush Dil was association with beneficial change in behaviour, biochemistry and intermediate clinical outcomes. In future work of this kind I would advocate QRISK2, and I would modify the predicted risk for each ethnic group. I would, however, recommend prediction to be a component of a detailed clinical assessment that tailored the intervention to the individual.

These recent observations call for rigorous trials. First, trials need to demonstrate the cost-effectiveness of cardiovascular screening compared with clinical care without this screening instrument and/or with other instruments. Second, trials need to show the effectiveness of risk factor change programmes, especially in S. Asians, including the practical value of risk prediction tools. Third, the costs and benefits of the absolute approach compared to the relative risk approach advocated by Christiaens et al(2) need to be compared.

Reference List

(1) Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, Minhas R, Sheikh A et al. Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2. BMJ 2008; 336(7659):1475- 1482.

(2) Christiaens T. Cardiovascular risk tables. BMJ 2008; 336(7659):1445-1446.

(3) Cooper A, O'Flynn N. Risk assessment and lipid modification for primary and secondary prevention of cardiovascular disease: summary of NICE guidance. BMJ 2008; 336(7655):1246-1248.

(4) Bhopal R. Causes in epidemiology: the jewels in the public health crown. J Public Health 2008;, fdn052.

(5) Ranganathan M, Bhopal R. Exclusion and Inclusion of Nonwhite Ethnic Minority Groups in 72 North American and European Cardiovascular Cohort Studies. PLoS Med 2006; Vol 3(3):0001-0008,

(6) Kooner JS, Chambers JC, guilar-Salinas CA, Hinds DA, Hyde CL, Warnes GR et al. Genome-wide scan identifies variation in MLXIPL associated with plasma triglycerides. Nat Genet 2008; 40(2):149-151.

(7) Fischbacher CM, Bhopal R, Povey C, Steiner M, Chalmers J, Mueller G et al. Record linked retrospective cohort study of 4.6 million people exploring ethnic variations in disease: myocardial infarction in South Asians. BMC Public Health 2007; 7(1):142.

(8) Wild SH, Fischbacher C, Brock A, Griffiths C, Bhopal R. Mortality from all causes and circulatory disease by country of birth in England and Wales 2001-2003. J Public Health (Oxf) 2007; 29(2):191-198.

(9) Bhopal R, Fischbacher C, Vartiainen E, Unwin N, White M, Alberti G. Predicted and observed cardiovascular disease in South Asians: application of FINRISK, Framingham and SCORE models to Newcastle Heart Project data. J Public Health 2005; 27:93-100.

(10) Mathews G, Alexander J, Rahemtulla T, Bhopal R. Impact of a cardiovascular risk control project for South Asians (Khush Dil) on motivation, behaviour, obesity, blood pressure and lipids. J Public Health 2007; 29(4):388-397.

(11) Gill PS, Kai J, Bhopal R.S, Wild S. Health Care Needs Assessment: Black and Minority Ethnic Groups. The epidemiologically based needs assessment reviews. In: Stevens A et al, editor. Abingdon: Radcliffe Medical Press Ltd, 2007.

Competing interests: None declared

Competing interests: No competing interests

18 July 2008
Raj S Bhopal
Professor of public health
Public Health Sciences, University of Edinburgh, EH89AG