GRÁFICO 7: INVOLUCRAMIENTO LABORAL

For the years that followed the 1968-1975 Framingham cohort recruitment, at least until the 1980s, effective treatment of risk factors using drug therapies was relatively uncommon, although antihypertensive drugs were used increasingly in subsequent years. Lipid lowering therapy did not become commonplace until the 1990s. A reduction in smoking occurred in men (although not in most female populations, where it tended to rise), and blood pressure and cholesterol values tended to decline, contributing to the global improvement in coronary heart disease mortality since the 1980s (32).

The Framingham study therefore took place in an environment relatively free of the effects of risk factor modification on outcomes. This raises a further issue, as the estimation of cardiovascular risk using the Framingham algorithms is required for modern populations whose future risk may be affected by drug therapy, and whose risk estimation should theoretically be carried out using ‘pre-treatment’ values of blood pressure and cholesterol. In modern practice, such values are often unavailable if the patient is already on treatment, particularly when the drug therapy preceded the introduction of electronic medical records. In the UK, this began in the late 1980s or early 1990s. By the end of the latter decade the majority of UK practices were computerised to varying degrees. Nevertheless, as discussed above a significant proportion of modern patients have treated risk factors whose pre-treatment values are either not recorded or recorded in a form not accessible to electronic retrieval.

Lack of availability of ‘pre-treatment’ values for blood pressure and cholesterol creates a practical difficulty, and an obstacle to the estimation of risk in treated individuals. Such individuals include the majority of those on the hypertension register, which was recommended in the Coronary Heart Disease National Service Framework (13) as the most likely place to begin case finding for those at high coronary heart disease risk. A systematic attempt to identify the practice’s ‘at risk’ population will

therefore miss these patients if it is confined to those who are not currently taking anti- hypertensive or lipid lowering drug therapy, although this approach has been advocated (33). A similar approach was recommended by JBS2, in which patients off treatment were to be risk assessed opportunistically.

Other solutions have included:

 Recognising the problem but still using the modified values as inputs,

accepting that cardiovascular risk will be under-estimated. This is the approach used in the ‘e-Nudge’ case-finding tool to be described in detail later in this dissertation. It is also suggested in the 2008 NICE guidance on Lipid Modification (20).

 Using ‘treatment for blood pressure’ status as an input to the algorithm. This

is used in the Pocock algorithm (34) and in the later QRISK and QRISK2 algorithms (1, 10).

 Introducing an interaction term between systolic blood pressure and anti-

hypertensive treatment (35).

 Entering an ‘assumed value’ for the pre-treatment levels of blood pressure or

cholesterol. JBS2 (9) suggests a systolic blood pressure of 160 mmol/L and a total to HDL cholesterol ratio of 6.0 as the assumed values.

 In a new Framingham based risk algorithm designed for use in primary care,

D’Agostino et al provide alternative regression co-efficients for systolic blood pressure depending on whether it is a treated value or not (36).

The Framingham study has become the most frequently used data source for estimating cardiovascular risk, partly because of the relative freedom from the effects of treatment on outcomes. Because of the global improvement in cardiovascular mortality since the original study was completed, the CHD algorithm has been found to over-predict risk in the general population of the UK (28), in Germany (37), and in

Belfast and France (38). In the UK, over-estimation is particularly evident in the low risk populations as discussed above (21).

The Framingham algorithms themselves have experienced numerous revisions over the years. An early risk scoring system was published in 1967 (39) and drew on the data collected from the original recruitment cohorts that commenced in 1948. A widely cited paper from 1976 (6) describes a new logistic regression algorithm to combine the risk factors but at this point high density lipoprotein (HDL) cholesterol was not included. The algorithm in current common use is that published in 1991 (8) and includes HDL cholesterol. This was further modified in 2000 to enable it to predict cardiovascular events in patients with established cardiovascular disease such as a history of myocardial infarction, i.e. in the secondary prevention scenario (35) although this algorithm has not entered routine practice in the UK.

The contrast between ‘pre-treatment’ and ‘modified’ risk is particularly relevant if one is attempting to create practice based ‘At risk of CVD’ registers. This was first proposed by the CHD NSF of 2000 (although this document was concerned more specifically with CHD rather than CVD risk). Such registers would include people whose risk had been identified on the basis of pre-treatment risk factor measurements (e.g blood pressure and serum cholesterol) but who had subsequently undergone treatment of these factors to the point where the estimated risk based on treated values would be lower than that required to justify inclusion on the register. This raises the important issue for identifying potentially at risk individuals based on current electronic data: are we interested in identifying those that are still at risk when assessed using treated factors values, or are we interested in controlling risk in those whose ‘original’ (unmodified) risk was high? The CHD NSF of 2000 clearly preferred the latter, whilst more recent guidelines (such as NICE CG67) that recognise the difficulties (increasingly evident since 2000) in identifying pre-treatment levels in general practice tend to favour the former, with appropriate adjustment in risk

2.7 Cardiovascular risk algorithms using alternative data to