GRÁFICO 8: SUPERVISIÓN

Other important studies contributing to what is known about cardiovascular risk include MONICA (40), PROCAM (41), SCORE (42) ASSIGN (21) and QRISK (1, 10).

2.7.1 MONICA

MONICA (Monitoring trends and determinants in cardiovascular disease) was a large prospective observational survey of cardiovascular risk factor patterns and event rates organised by the World Health Organisation, involving 41 collaborating centres in 21 countries, and a total study population of approximately 15 million people aged 25-64 years. It was designed to investigate the relationships between trends in CVD risk factors and CVD mortality rates (43). The original Framingham study included 5573 individuals, a small enough number to allow an intensive follow up strategy. MONICA involved much larger numbers and required alternative approaches. Designed prospectively and conducted using protocols standardised across collaborating centres, MONICA is an early example of epidemiological surveillance of cardiovascular disease patterns using routinely collected health data on an international scale. This source created quality issues in event monitoring (11). Differences in ascertainment occurred between collaborating centres. Some used the ‘hot pursuit’ method, in which patients admitted to hospital following an event would be interviewed whilst still an inpatient. Others used the ‘cold pursuit’ approach, in which event monitoring relied on searches on hospital records following discharge (44). Hense et al suggested that blood pressure measurement quality in MONICA should be assessed not simply by visits and inspections of the collaborating sites, but by examining the actual blood pressure measurements themselves (45). Two techniques, the ‘last digit preference’ and the

‘proportion of identical duplicate measurements’ were shown to improve the comparability of quality standards between centres. In the two Belgian collaborating centres, misclassification of CHD cases was found to be partly due to coding problems (46). This issue is likely to affect any research relying on diagnostic coding, and will be discussed further later in this dissertation.

MONICA was designed primarily as a longitudinal survey of diverse multinational populations rather than a cohort study with individual follow up (although this did also occur). It did not therefore result in a risk algorithm, other than through its contribution to the SCORE project, which included MONICA cohort data from Scotland and Germany (42).

2.7.2 PROCAM

PROCAM (Prospective Cardiovascular Munster study) was a cohort study based at Munster in Germany, commencing in 1986 (41). The study confirmed the relevance of the classical risk factors, and suggested that serum triglycerides, apolipoprotein b, and coagulation factors were also relevant to CHD risk and might be used to improve risk estimations. The main outcomes in this study were myocardial infarction and sudden cardiac death. Cerebrovasular disease outcomes were recorded, but the upper limit of the age range was 65 years, above which stroke incidence rises steeply (7). Interestingly, this study raised the question of a ‘J-shaped curve’ relating total and LDL cholesterol levels to all cause mortality, due to an apparent increase in cancer deaths in smokers with low levels of these factors (47).

2.7.3 SCORE

The SCORE (Systematic Coronary Risk Evaluation) algorithm is based on examination of 12 different cohort studies from 11 European countries (42). The outcomes only include fatal cardiovascular events. Whilst these data are from European rather than

Framingham for the UK population in the JBS2 report or in the 2006 NICE guidelines on statin prescribing (48). One of the reasons for this was the need to include non-fatal as well as fatal CVD outcomes.

SCORE does not include diabetes status as an input risk variable, recognising that patients with diabetes should generally be considered at high CVD risk. This became the recommended approach supported by the British Hypertension Society (49), the Diabetes National Service Framework (50, 51) and JBS2 (9). However, risk algorithms have been derived for patients with diabetes from the United Kingdom Prospective Diabetes Study for both CHD (15) and stroke (52). The CHD algorithm has been compared with the Framingham CHD function in a study of patients with newly diagnosed type 2 diabetes but free of CHD (53). Both algorithms were found to be poorly calibrated to the study population’s outcomes, although discrimination was moderately effective. However the most recent NICE guideline on type 2 diabetes recognises that not all patients are at sufficient cardiovascular risk to justify lipid lowering therapy and that in such cases a risk assessment should be undertaken on an annual basis using the UKPDS risk engine (15).

2.7.4 QRISK and QRISK2

More recently, a new risk algorithm based on UK data was derived using the QRESEARCH database at the University of Nottingham (54). This algorithm was named QRISK (10) and was later improved to produce QRISK2 (1). Based exclusively on data held in EMIS practices, the algorithm was later validated using the THIN database (which involve VISION (In Practice Systems) data) and found to out-perform Framingham as a predictive tool for CVD events in the UK population (55). However, as discussed above (and very clearly stated by Liew and Glasziou (56)), it may be more appropriate to address the concept of underlying, untreated risk rather than the risk based upon outcomes of populations whose CVD risk factors are being actively managed.

2.7.5 Comparisons between Framingham and alternatives

The Framingham risk function has been compared with European algorithms including Dundee, British Regional Heart Study (BRHS), and PROCAM (57). The algorithms were applied to a sample of 206 consecutive male patients attending a hypertension clinic. Apart from the BRHS data (in which systematically lower risk estimates were produced), Framingham made comparable predictions to the other algorithms and was considered adequate for use in Northern European male populations.

Framingham algorithms have also been applied to different ethnic groups to test external validity, as the Framingham study population was predominantly composed of white Americans. The multiple ethnic groups investigation (58) examined data from six prospective cohort studies in ethnically diverse populations. The algorithm performed well among white and black men and women, but required recalibration for Japanese American and Hispanic men, and Native American women.

The validity of the Framingham algorithm in the modern UK population remains a concern, particularly in Asian men, whose observed risk tends to be higher than the predicted risk using Framingham. To estimate the diverse risk levels of different minority groups, the ETHRISK algorithm was developed, based on survey data from UK populations (59). However, this has not yet been validated through a cohort study within these populations.