SY 04-4 HOW TO IMPROVE CVD RISK PREDICTION IN A LOW-RISK POPULATION

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Abstract

Many cardiovascular disease (CVD) risk prediction tools have been developed in an attempt to identify those at highest risk in order for them to benefit from interventional treatment. The first CVD risk tool that was developed was the coronary heart disease risk tool by the Framingham Heart Study in 1998 (1). However the Framingham Risk Score could overestimate (or underestimate) risk in populations other than the US population. Hence several other risk engines have also been developed, primarily for a better fit in the communities in which the tools are to be used (2, 3). Having said that the Framingham Heart Study risk tool has been validated in several populations (4, 5) and found to work reasonably well after some recalibration.

Most risk prediction tools predict short term risk ie over a period of 10 years but since more recently risk tools now attempt to predict life-time risk or at least risk over the next 30 years. (6-8). The practical use of these risk prediction tools is that it is able to separate those at high risk (ie > 20% risk of a CVD event fatal or non-fatal event in the next 10 years) from those with the lowest risk (< 10% risk over 10 years). It then helps practitioners to triage them to either receive preventive therapy (high risk group) or none at all (low risk group) respectively. However in those with medium risk ie between 10-20%, the decision to offer treatment or not is less clear. In such a situation, other CVD risk factors for example family history of premature coronary heart disease, other biomarkers like elevated hs-CRP, presence of chronic kidney disease or albuminuria can be employed to further stratify risk.

It is known that risk prediction tools are very much age dependent and in a younger individual with mildly raised CVD risk factors, his global CVD risk may be grossly under-estimated. Here additional CVD risk factors beyond those traditionally used in risk engines should be sought in order to recalibrate that individual’s seemingly low risk and earlier intervention introduced if indeed he is of higher risk than what has been predicted by the conventional risk tools. Here too the use of life-time risk is probably of more importance than the traditional 10 year risk tool, again in order to identify those seemingly at “low” risk 10 year risk to receive treatment if the life-time risk is greater compared to an individual of the same age with optimal parameters. Furthermore while it is known that those with highest risk benefit the most from intervention, it is the population at large with the low or lower risk which contributes most to total CV morbidity and mortality in a country or community.

Hence while short term risk prediction to identify those at highest risk is useful particularly in the presence of limited resources, attention should also be paid to those with short term low risk if the aim is to reduce CVD morbidity and mortality in any substantial way

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