Excess risk attributable to traditional cardiovascular risk factors in clinical practice settings across Europe - The EURIKA Study

Hypertension, dyslipidemia, smoking and diabetes mellitus are established modifiable causes of cardiovascular (CVD) disease [1, 2]. However, in spite of effective interventions and widespread knowledge, the prevalence of CVD risk factors in Western populations is high and the proportion of patients with controlled risk factors is low [3, 4]. It is clear that we need more effective translation strategies at the individual and the population levels to control the CVD disease epidemic.

Physicians involved in primary prevention are key players in CVD risk control strategies. Risk scoring instruments have been developed to help practitioners assess the overall CVD risk of patients and guide clinical interventions. The European Society of Cardiology has promoted the use of the Systematic Coronary Risk Evaluation (SCORE) equation to estimate the 10-year risk of CVD death, with separate equations for high and low risk regions in Europe [1, 5]. It may be important for physicians to consider the reduction in CVD risk that would be obtained if all patients had their risk factors controlled according to current guidelines. As a consequence, we used data from the European Study on CVD Risk Prevention and Management in Daily Practice (EURIKA), a cross-sectional study of primary care and specialized outpatient clinics involved in primary prevention in Europe, to calculate the estimated excess risk attributable to the presence and to the lack of control of traditional CVD risk factors in usual clinical care in Europe.

Hypertension, dyslipidemia, smoking, and diabetes were responsible for 32.7 (32.0-33.4), 15.1 (14.8-15.4), 10.4 (9.9-11.0), and 16.4% (15.6-17.2) of CVD risk, respectively (Table 2). These risk factors accounted for 57.7% (57.0-58.4) of CVD risk, with relatively little between country variability (between-country range 52.6 to 61.6%). The 10-year absolute excess risks of CVD death attributable to hypertension, hyperlipidemia, smoking, and diabetes were 3.49 (95% confidence interval 3.34-3.63), 1.14 (1.08-1.19), 0.92 (0.85-0.99), and 2.25% (2.12-2.39), respectively (Figure 1 and Additional File 1: Table S1). The absolute excess risk attributable to the four risk factors combined was 5.66% (5.47-5.85) (Figure 2), with a between-country range of 3.06 to 4.65% among low-risk countries and 5.39 to 8.12% among high-risk countries.

			Low risk	countries					High risk	countries
	Overall	Belgium	France	Greece	Spain	Switzerland	Austria	Germany	Norway	Russia	Sweden	Turkey	UK
Hypertension	32.7	29.9	30.1	26.1	30.9	33.1	32.4	33.5	34.3	37.1	38.8	32.7	33.5
	(32.0-33.4)	(27.5-32.3)	(27.5-32.7)	(23.6-28.6)	(28.4-33.3)	(30.8-35.5)	(29.9-34.9)	(31.1-35.9)	(31.7-36.8)	(34.8-39.3)	(36.3-41.3)	(30.3-35.1)	(31.1-35.9)
Dyslipidemia	15.1	11.2	15.3	14.4	14.7	14.2	17.6	17.3	16.8	19.2	16.0	13.3	11.9
	(14.8-15.4)	(10.0-12.3)	(14.1-16.5)	(13.2-15.9)	(13.5-15.9)	(13.0-15.3)	(16.5-18.8)	(16.1-18.4)	(15.6-18.1)	(18.1-20.3)	(14.8-17.2)	(12.2-14.5)	(10.8-13.0)
Smoking	10.4	8.5	8.4	16.4	8.3	11.6	11.0	9.4	14.1	10.3	9.1	9.7	8.8
	(9.9-11.0)	(6.7-10.4)	(6.4-10.4)	(14.5-18.3)	(6.5-10.2)	(9.7-13.4)	(9.1-12.8)	(7.6-11.2)	(12.2-16.0)	(8.5-12.1)	(7.2-11.0)	(7.9-11.5)	(7.0-10.6)
Diabetes	16.4	15.7	13.9	17.1	16.7	17.5	14.7	21.5	13.6	14.5	15.1	21.8	13.7
	(15.6-17.2)	(12.9-18.5)	(10.8-16.9)	(14.2-20.1)	(13.8-19.5)	(14.7-20.3)	(11.8-17.7)	(18.7-24.3)	(10.6-16.6)	(11.9-17.2)	(12.1-18.1)	(19.0-24.6)	(10.8-16.5)
All risk factors	57.7	52.6	53.9	56.5	55.2	58.7	57.9	61.0	60.6	61.6	61.1	59.4	54.1
	(57.0-58.4)	(50.3-54.9)	(51.4-56.4)	(54.1-58.9)	(52.9-57.6)	(56.4-61.0)	(55.5-60.3)	(58.6-63.3)	(58.1-63.0)	(59.4-63.8)	(58.7-63.6)	(57.0-61.7)	(51.8-56.4)

Values in Table are % (95% confidence interval).

Absolute excess risks were particularly high among patients with current estimated risk ≥ 10% (excess risks attributable to the presence and to the lack of control of risk factors of 14.50 and 9.41%, respectively), among patients with diabetes (10.52 and 5.73%, respectively), among patients ≥ 65 years of age (9.76 and 5.12%, respectively), and among current smokers (9.09 and 6.69%, respectively) (Additional File 1: Table S3).

Hypertension, dyslipidemia, smoking and diabetes explained 57.7% of estimated risk of CVD death of patients 50 years of age and older with at least one CVD risk factor who attended primary and specialty clinics involved in primary prevention across Europe in the EURIKA study. Eliminating these risk factors would translate in an absolute reduction of 5.66% in the 10-year risk of CVD death. Even though EURIKA patients were under medical care, lack of control of traditional risk factors was common, and explained almost 30% of estimated risk. Control of hypertension, dyslipidemia, smoking and diabetes would reduce the estimated 10-year risk of CVD death by 3.12%. Absolute risk reductions through elimination or control of hypertension, dyslipidemia, smoking and diabetes would be particularly large in patients with high overall risk, in patients with diabetes, in elderly patients, and in current smokers.

The prevalence of CVD risk factors in the EURIKA population was high, partly as a consequence of including patients with at least one traditional CVD risk factor (hypertension, dyslipidemia, smoking, diabetes or obesity) [6]. Among subjects 50 years of age or older in Western societies, however, the prevalence of these risk factors is very high [10–13], making the EURIKA findings applicable to a wide segment of the population attending general medical care. In addition, a large fraction of EURIKA patients did not reach target levels of control [1]. These findings are consistent with earlier surveys showing highly inadequate risk factor control in patients with and without established CVD disease [3, 14–18]. For instance, the EUROASPIRE III Survey, conducted in 2006 - 2007 among patients without a history of atherosclerotic disease who were treated with antihypertensive, lipid-lowering, or antidiabetic drugs in general practice in 12 European countries, found that 73.7% of patients using antihypertensive medications had blood pressure above 140/90 mmHg (above 130/80 mmHg among patients with diabetes), 69.4% of patients using lipid-lowering medications had total cholesterol above 5.0 mmol/L (above 4.5 mmol/L among patients with diabetes), and 60.1% of self-reported patients with diabetes had HbA1 c above 6.1% [3]. Our analysis of the EURIKA Study extends these findings to show that lack of risk factor control is responsible for a substantial excess risk among patients already under clinical care, highlighting the need for more effective translation of evidence-based guidelines into routine care.

Hypertension has been identified as the leading risk factor for mortality and the third cause of disability-adjusted life-years worldwide [19, 20]. In the EURIKA population, elevated blood pressure was responsible for 32.7% of CVD risk. Indeed, our analyses likely underestimate the contribution of elevated blood pressure to CVD risk as we did not consider the excess risk contributed by patients with prehypertension [20]. Even though the EURIKA population was under medical surveillance and 68.5% of study patients were receiving antihypertensive medications, over 50% of EURIKA patients had measured levels of blood pressure above 140/90 mmHg. Control of blood pressure levels to target levels would reduce the estimated risk by 8.8%, with particularly high gains in Sweden, Russia, and Turkey, although there may be a degree of resistant hypertension that even after aggressive management by the clinician might not result in blood pressure being brought back to target. Beyond the well-known challenges for control of hypertension in clinical settings [21], lowering blood pressure to normotensive levels in the population will require intensive public health action to control the obesity epidemic and to promote healthy eating and exercise habits in the general population [19, 20]. Our analysis of the EURIKA data emphasizes the need to combine clinic-based and population strategies to curb high blood pressure-related risk.

Dyslipidemia was responsible for 15.1% of CVD death risk in the EURIKA population, and achieving control levels of 5 mmol/L in all EURIKA participants would reduce the estimated CVD death risk by 10.6%. The impact of dyslipidemia was particularly high in Russia, a country with very high average cholesterol levels and with low rates of control. As for hypertension, control rates for dyslipidemia are low even with the availability of highly effective and safe interventions to reduce cholesterol levels [22]. Furthermore, since the benefits of cholesterol lowering therapies are independent of pre-treatment cholesterol levels or other patient characteristics [22, 23], cholesterol lowering therapies could be used to achieve specific reductions in cholesterol levels (instead of targeting pre-specified levels of control) as a core component of high-risk patient management. The high proportion of patients with uncontrolled dyslipidemia in the EURIKA population and the associated excess risk further call for added clinical and public health efforts to control cholesterol levels.

With a proportion of current smokers of 21.4% in the EURIKA population, smoking accounted for 10.4% of CVD risk, with particularly high attributable risks in Greece and Norway. Furthermore, since smoking habits were identified by questionnaire without confirmation by objective biomarkers, it is likely that the proportion of current smokers and the associated attributable risk have been underestimated [24]. The benefits of quitting smoking are well documented and all current smokers should be encouraged to quit [25, 26]. Primary practices and specialized clinics are important checkpoints in this process [26], although population strategies are needed to favor a smoke-free environment and an appropriate atmosphere for sustained quitting.

Diabetes was responsible for 16.4% of CVD risk in the EURIKA population, but control of diabetes would only reduce CVD risk by 3.1%. Excess risks associated with diabetes were particularly high in Turkey and Germany. While diabetes is a strong independent risk factor for CVD, the benefits of intensive glycemic control on macrovascular disease outcomes and mortality are controversial. Indeed, recent trials have shown either no significant reduction in CVD outcomes or increased mortality with intensive glycemic control [27]. In our analysis, we assumed based on observational data that the risk of CVD death for diabetic patients increases by 18% for each 1 percentage point increase in HbA1 c above target HbA1 c level [9], but this assumption will need to be modified as additional evidence accumulates on the impact of intense glycemic control on CVD outcomes and we develop more reliable estimates of the benefit of glucose control on macrovascular CVD outcomes. While attaining glycemic control targets is still a key objective of management of patients with diabetes, our data also show that substantial risk benefits can be realized in patients with diabetes by control of other cardiovascular risk factors. Ultimately, however, the main approach to diabetes risk control should be based on primary prevention of diabetes through control of the obesity epidemic and adoption of healthy lifestyles.

Our estimates of attributable risks in the EURIKA population rely on a series of assumptions. First, SCORE equations were developed to have better calibration for European populations, but there is concern that the SCORE equation may overestimate risk in many Western European countries with decreasing secular trends in CVD mortality as well as in elderly patients [28]. Calibration of the SCORE equation to country-specific CVD mortality rates has been advocated [29], but joint data on CVD risk factors and mortality rates in country-wide representative population samples were not available in most EURIKA countries. Conversely, the SCORE equation may underestimate risk in Russia and other Eastern European countries that are experiencing extremely high rates of CVD mortality [30, 31]. Furthermore, the SCORE equation may underestimate risk in patients with certain risk factors not included in the equation, such as those with a sedentary lifestyle, central obesity, a family history of premature CVD, or with the presence of subclinical atherosclerosis [1]. Country specific calibration or recalibration of equations to current CVD death rates will change the estimates, although we notice that the proportion of CVD risk attributable to each risk factor and overall is relatively constant across countries, suggesting that the main conclusions of our analyses are likely to hold under revised risk equations.

Second, the EURIKA population was already under clinical care and a high proportion of EURIKA patients were taking medications to lower blood pressure (68.5%), lipids (43.2%) or glucose levels (23.4%). Since we do not have pre-medication data collected under standardized conditions, we could not calculate the full impact of risk factors or the benefits of clinical care in terms of risk reduction. Third, while the cohorts used to derive the original SCORE equation included participants with diabetes, the equation did not incorporate diabetes as a predictor due to differences in diabetes definition and ascertainment across cohorts [5]. Since the prevalence of diabetes in the EURIKA population was much higher than the prevalence of diabetes in the SCORE populations, we considered that it was important to account for diabetes as an independent risk factor and thus increased the risk of patients with diabetes by a factor derived from a large pooled analysis of diabetes risk [8]. This approach may result in some overestimation of overall risk and of the contribution of diabetes in our study, but is likely a better approximation to the true underlying risk than the original SCORE equation in a population with high diabetes prevalence.

Finally, the SCORE equation is restricted to predict 10-year risk of fatal CVD, and thus underestimate the burden of CVD by excluding non-fatal events and events occurring after 10 years of follow-up. Even with these sources of uncertainty, our analyses indicate that 4 well-established, preventable and controllable risk factors were responsible for almost 60% of estimated CVD risk and that lack of control of these risk factors according to clinical guidelines was responsible for almost 30% of CVD risk, a failure of both clinical medicine and public health.

In the EURIKA study, lack of control of CVD risk factors was responsible for almost 30% of CVD mortality risk. Systematic monitoring of CVD risk factor levels and of SCORE risk estimates can thus help practitioners understand the implications of risk factor management and control in their patient populations. Patients with high estimated risk, patients with diabetes, elderly patients and current smokers would show substantial absolute reductions in estimated risk by attaining target control levels. These findings are in agreement with current guidelines to direct intensive therapy to patients at high estimated risk and validate the use of SCORE or other risk equations to manage risk reduction [1, 2]. The clinical approach to patients with low absolute risk, including young patients with high levels of risk factors, is more complex. Our data indicate that both a clinical and a public health approach are needed to maximize CVD prevention and call for added efforts to develop translational approached that effectively implement prevention strategies in individual patients and in population settings.