European Journal of Preventive Cardiology

An individual’s overall cardiovascular risk should guide appropriate therapy and patient management. Several risk assessment scores are available; however, further development of risk algorithms is necessary to account for changes in available treatments and patient lifestyles, to make use of emerging risk factors and more accurate methods for measuring outcomes, and to provide more targeted measurement of risk for different patient subpopulations. When developing a risk model it is important to clearly define the outcome that the risk will predict, the period of follow up, the patient population, and the predictors to be used and how they will be combined. An appropriate statistical model is specified with the aim of finding the weighted combination of the candidate risk factors that best predicts the disease outcome. Stepwise regression is used to systematically search through candidate risk factors to produce a final model with an acceptable number of highly relevant variables. Possible non-linear effects of continuous variables and interactions between variables must be considered. However, the selection of variables requires not just statistical criteria but also clinical, biological and epidemiological judgement. In general, relatively simple, clinically reasonable and easy-to-use models that can be generalized to other settings are preferred to complex mathematical models that fit the sample data perfectly. There is a permanent need for updating cardiovascular risk scores to reflect advances in our clinical knowledge over time and changes in population risk. Development of a risk model requires both statistical expertise and a sound knowledge of the clinical and epidemiological aspects of cardiovascular disease.

While cardiovascular disease and certain other conditions are considered to confer a high or very high risk of cardiovascular events, the asymptomatic population can be subdivided in different categories of total CV risk using risk models; this allows the clinician to adapt the intensity of preventive strategies accordingly. Risk models, such as that based on the US Framingham Study and the SCORE model, based on European cohorts, estimate risk according to the presence of risk factors, including age, gender, smoking habits, systolic blood pressure, and cholesterol levels. However, estimation of an individual’s cardiovascular risk remains approximate, and whether new biomarkers of risk will improve risk assessment is a key question. Several novel cardiovascular risk markers have been suggested, including lipid, inflammatory, thrombotic, and genetic biomarkers. Demonstrating that a novel biomarker is predictive of cardiovascular disease is, by itself, insufficient proof that it adds incremental value to existing risk estimation models. The Net Reclassification Improvement index provides an indication of the ability of a novel marker to improve risk estimation by classifying individuals to a more correct category. In addition, new risk models may be calibrated by measuring how closely predicted outcomes agree with actual outcomes. Traditional cardiovascular risk factors explain most of an individual’s risk. Consequently, the addition of new risk factors to existing models has provided disappointingly small effects overall. However, there addition to conventional risk estimation may be useful in correctly reclassifying individuals at intermediate risk as above or below a chosen intervention threshold.

Carotid intima-media thickness assessed by ultrasonography of carotid arteries is a safe, non-expensive, feasible and accurate method for detecting early signs of atherosclerosis and carotid intima-media thickness and change in carotid intima-media thickness over time reflect cardiovascular disease risk. Technical aspects impact on the measurement, variability and interpretation of carotid intima-media thickness. These include device aspects, inter- and intra-sonographer variability and the ultrasound protocol used. The mean common carotid intima-media thickness and the mean maximum common carotid intima-media thickness are the most widely used carotid intima-media thickness measurements. Common carotid intima-media thickness values of around 0.5 mm are considered ‘normal’ in young adults. Values are higher in men than in women, in African–Americans than Caucasians and increase with age. Carotid intima-media thickness values at or above the 75th percentile of a reference population indicate increased cardiovascular risk. Guidelines differ in their recommendations for the use of carotid intima-media thickness measurements for risk assessment in primary prevention because evidence suggesting that it improves upon conventional risk scores is inconsistent. Carotid intima-media thickness is frequently used in clinical trials as a surrogate endpoint for cardiovascular events on the assumption that regression or slowed progression of carotid intima-media thickness, induced by cardiovascular risk interventions, reflects a reduction in cardiovascular events. However, further data are required to confirm this linear relationship. No international guidelines exist on the use of carotid intima-media thickness as a research tool. Quality control in acquisition, measurement and interpretation of carotid intima-media thickness are important considerations and the carotid intima-media thickness protocol used should be determined by the research question under investigation.

Primary prevention is the most effective strategy for reducing the burden of cardiovascular disease; however, predicting cardiovascular risk in the asymptomatic population lacks precision. Traditional methods of estimating risk are based on the presence of certain risk factors, some of which (e.g. hypertension, dyslipidaemia) are modifiable. Cardiovascular risk is also determined by a plethora of genetic risk factors, and this is partially reflected by a positive family history of cardiovascular disease; however, family history may not always be an accurate indication of genetic cardiovascular risk. Genome-wide association studies have identified numerous genetic variants associated with increased cardiovascular risk and cardiovascular risk factors. The addition of genetic information to conventional risk scores has the potential to increase the discriminative power of the score. Genetic markers may be particularly helpful for predicting life-time risk of cardiovascular disease in younger subjects, which is often underestimated by traditional risk scores. Advances in our understanding of the genetics of cardiovascular risk provide opportunities for improving both the prevention and treatment of cardiovascular disease.

Cardiovascular risk management in the primary prevention population is impeded by the fact that risk scores do not identify properly a low–moderate risk population that could benefit from the use of preventive strategies. Current risk scores are further limited by their complexity and the lack of applicability to special patient populations and the real-life clinical setting, which deters their use. Clinical trials with risk factor interventions have been based on either a treat-to-target or fire-and-forget strategy and have used cardiovascular mortality or major events as their primary endpoint. These endpoints may not be appropriate for assessing cardiovascular benefit in low–moderate risk patients. Cardiovascular risk assessment should guide the strategy for risk factor intervention; however, this needs to be more clearly defined in low–intermediate risk patients.

Early intervention to control cardiovascular disease risk factors can result in significant reductions in cardiovascular disease mortality. However, there is a large gap between risk factor management outlined in clinical guidelines and actual patient care, with preventative medication being underused, particularly in low-income countries. Consequently, cardiovascular disease remains the leading cause of mortality. There are several barriers to guideline implementation. First, clinical trial populations are not representative of the at-risk population as a whole. Patients treated in clinical practice are often older with more co-morbidities, making them more difficult to treat. In addition, adverse effects of medication and complex dosing regimens reduce adherence to therapy. The wide choice of guidelines, lack of awareness of the guidelines and a perception that their recommendations are unrealistic, as well as time constraints on the physician and prescription costs, have also been identified as reasons why guideline recommendations are not followed. Strategies to improve adherence to guideline recommendations include research to better define optimal treatment in different patient populations, increased funding for research and guideline dissemination and implementation programmes, policies promoting healthy lifestyles and preventative medicine and physician and patient education. Systemic monitoring of quality of care and outcomes are also important to identify gaps in guideline implementation that could be addressed. A multidisciplinary team approach to risk factor management and telephone/Internet support systems have been shown to improve compliance with lifestyle changes and pharmacotherapy. Overcoming the barriers to implementation of guideline recommendations is key to improving cardiovascular disease prevention.

Background: Long-term trials on the effectiveness of pharmacological treatment for primary cardiovascular disease prevention are scant. For that reason risk prediction models are used as a tool to project changes in cardiovascular disease incidence due to changes in risk factor levels observed in short-term randomized clinical trials. In this article, we summarize the literature on the application of these risk models in pharmacoeconomic studies for primary cardiovascular disease prevention interventions in high-income countries.

Methods and results: We systematically reviewed the available literature on the application of cardiovascular disease risk models in pharmacoeconomic studies and assessed the quality of incorporation of risk models in these studies. Quality assessment indicated the distance between the characteristics of populations of the risk model and the studies reviewed, the frequent disagreement between risk model and study time horizons and the lack of proper consideration of the uncertainty surrounding risk predictions.

Conclusion: Given that utilizing a risk model to project the effect of a pharmacological intervention to cardiovascular events provides an estimate of the intervention's clinical and economical impact, consideration should be paid to the agreement between the study and risk model populations as well as the level of uncertainty that these predictions add to the outcome of a decision-analytic model. In the absence of hard endpoint trials, the value of risk models to model pharmacological efficacy in primary cardiovascular disease prevention remains high, although their limitations should be acknowledged.