Cardiovascular risk assessment in daily clinical practice: when and how to use a risk score

Abstract

Cardiovascular diseases (CVDs) remain a major cause of morbidity and mortality worldwide. The European Society of Cardiology Guidelines encourage the use of risk prediction models to enhance an adequate management of cardiovascular risk factors and the implementation of healthy behaviours. In primary prevention, estimating CVD risk is used to identify patients at high risk in order to enhance preventive strategies and decrease the incidence of unfavourable events and pre-mature cardiovascular deaths. Risk models integrate information on several conventional risk factors and estimate individual risk over a 10-year period. In addition to conventional risk factors, emerging non-traditional markers should be considered and mentioned in risk stratification. In secondary prevention, optimal management of patients include evaluation of residual CVD risk. The 10-year risk of recurrent events is not the same for all patients. The identification of high-risk patients is mandatory to prevent recurrent events and to allow to engage intensive treatments and follow-up strategies, representing an opportunity for major public health gain. This review provides a guide to evaluate which CVD risk score is appropriate for use in different settings in clinical practice.

Introduction

Cardiovascular diseases (CVDs) are the leading cause of death and sickness worldwide, responsible for an estimated 18.6 million deaths in 2019. An adequate management of risk factors and the implementation of healthy behaviours significantly decrease the risk of unfavourable events and pre-mature cardiovascular deaths. The World Health Organization (WHO) estimates that 75% of cardiovascular mortality can be reduced with appropriate changes in lifestyle. However, despite numerous public health and clinical attempts for primary CVD prevention, a significant portion of the population at ‘high ‘and ‘very high’ risk remains unnoticed and untreated.¹ In order to allow early intervention, a more accurate identification of these patients with high risk is required. In addition to conventional risk factors, emerging non-traditional markers {(apolipoprotein A, apolipoprotein B, high-sensitivity C-reactive protein, brain natriuretic peptides, troponin I, homocysteine, interleukins 1 and 6, lipoprotein(a) [Lp(a], cholesterol remnants, size and number of LDL particles, tissue/tumour necrosis factor-α, and uric acid} should be considered and mentioned in a tailored approach in risk stratification.²

Optimal management of patients, to prevent atherosclerotic cardiovascular disease (ASCVD), should involve programmes of screening based on individual features in the context of a structured network, including synergistically cardiologists, nurses, and general practitioners.^3,4

Assessing the risk of CVD in individuals who appear healthy as well as elderly people, in patients with established ASCVD, and in patients with diabetes mellitus (DM) can yield data for customized individual interventions. A step-by-step strategy can be used to customize treatment objectives.⁵

Risk estimation in apparently healthy people

The European Society of Cardiology (ESC) provides the use of risk prediction models to estimate individual risk over a 10-year period and to identify people at higher risk of CVD who should benefit most from preventive action.

The current ESC Prevention Guidelines⁵ developed the Systemic Coronary Risk Estimation 2 (SCORE2), an updated algorithm tailored to European populations to estimate an individual’s 10-year risk of fatal and non-fatal CVD events (myocardial infarction and stroke) in apparently healthy people aged 40–69 years with risk factors that are untreated or have been stable for several years. SCORE2 is sex-specific and includes age, smoking status, systolic blood pressure (SBP), and total and HDL cholesterol. Compared with the original SCORE, SCORE2 provides significant updates, such as the adaption to contemporary CVD rates, adjustment for competing non-CVD mortality, the combined outcome of fatal and non-fatal CVD events, and the use of age-related risk categories in order to evaluate an accurate estimation of total CVD burden (Figure 1). The algorithm was calibrated to four clusters of countries (low, moderate, high, and very high CVD risk) that are grouped based on national CVD mortality rates published by the WHO and it was validated in a large population cohort.^6,7

Risk estimation in older persons

Treatment benefit differs in older persons whose life expectancy is restricted. The relation between risk factors and CVD risk declines with increasing age and the competing risk of non-CVD death needs to be taken into account. Predictive models have the potential to support patient-centred healthcare decision-making by considering, biological age, frailty, patient preferences, and other pertinent patient features. Ignoring the impact of non-CVD death leads to an overestimation of risk and of a possible benefit of treating people. A risk-adjusted model for individuals aged over 70 years without pre-existing CVD to estimate 5- and 10-year risk of incident CVD—the new SCORE2-Older Persons (SCORE2-OP) was developed. The model has been externally validated in cohorts and trials from multiple countries and calibrated to four different geographical risk regions, using WHO data. These scores may help older people make shared decisions about their CVD risk management by explaining the likelihood of CVD events and the possible advantages of treating risk factors (Figure 1).^7,8

How to estimate a person’s 10-year risk of total cardiovascular disease events, using SCORE2 and SCORE-OP?⁵

• Identify the correct cluster of countries.

• Accompanying risk table for their sex, smoking status, and (nearest) age.

• Within that table finds the cell nearest to the person’s BP and non-HDL cholesterol.

Risk estimates then need to be adjusted upwards as the person approaches the next age category.

Risk estimation in woman

Cardiovascular diseases are the leading cause of death in women. Unfortunately, however, the misperception that women are ‘protected’ against ischaemic heart disease leads to an under-diagnosis and under-treatment. If compared with men, women are more likely to have different clinical manifestations of ischaemic heart disease and heterogeneous clinical presentation, characterized by sex-specific symptoms. After menopause, the lower risk of cardiac events during the fertile age leaves women with untreated risk factors and vulnerable to develop ASCVD. The SCORE2 algorithm,⁸ commonly used in women, do not take into consideration gender-specific risk factors. A tailored risk assessment should incorporate gender risk factors in order to adopt preventive measures and an optimal medical therapy in women (Table 1).⁹

Risk estimation in patients with diabetes

The 2023 ESC Guidelines for the management of CVD in patients with diabetes¹⁰ recommend in patients aged ≥40 years with Type 2 diabetes without ASCVD or severe target organ damage (TOD) to estimate 10-year CVD risk using the SCORE2-Diabetes algorithm in order to guide treatment choices. SCORE2-Diabetes algorithm integrates information on conventional CVD risk factors (age, smoking status, SBP, and total and HDL cholesterol) with diabetes-specific information (age at diabetes diagnosis, HbA1c, and estimated glomerular filtration rate). This model is calibrated to four clusters of countries (low, moderate, high, and very high CVD risk) using the similar methodology of the SCORE2 and SCORE2-OP algorithms and validated in 217 036 individuals.¹⁰ A tailored approach provides an appropriate risk estimation to enhance the accuracy, practicability, and sustainability of CVD prevention strategies and help guide preventive treatment (Figure 1).

Risk estimation in patients with chronic kidney disease

Chronic kidney diseases (CKD) are a worldwide public health problem with an increasing incidence and prevalence and high cost. Patients with moderate and severe CKD have a high and very high CVD risk status regardless of other risk factors.⁵ Estimated glomerular filtration rate and albuminuria are not included in SCORE2 and SCORE2-OP algorithm. Recently, CKD measures were added on into these algorithms to improve CVD risk prediction in patients with CKD (Figure 2).¹¹

Recently, the American Heart Association introduced the American Heart Association Predicting risk of cardiovascular disease EVENTs (PREVENT) equations¹² to estimate the 10- and 30-year risk of total CVD for people aged 30 years and older. The calculator was developed using health information from more than 6 million adults, including people from different racial and ethnic, socio-economic, and geographic backgrounds. It takes into account also kidney and metabolic diseases (body mass index) and provides the possibility of adding biomarkers, including glycated haemoglobin and urine albumin–creatinine ratio. The equations are sex-specific and race was removed as a risk variable, acknowledging that race is a social rather than biological construct. The addition of the social deprivation index allows for the inclusion of social determinant of health factors related to cardiovascular health and risk.¹²

Risk estimation in patients with familial hypercholesterolaemia

According to ESC Guidelines, patients affected by familial hypercholesterolaemia (FH) are considered with a high risk of pre-mature ASCVD.⁵ However, this risk is highly heterogeneous and unfortunately, existing risk calculators were not developed in FH cohorts and underestimate the ASCVD risk in these patients. Recently, the FH-Risk-Score was realized to predict the incidence of ASCVD events over a period of 10 years in subjects with no prior cardiovascular event and is comprised of seven simple clinical variables, sex, age, HDL cholesterol, LDL cholesterol, hypertension, smoking, and Lp(a) (Figure 2).¹³ Another ASCVD risk calculator designed specifically for the FH population is the Montreal-FH-SCORE.¹⁴ It uses five simple clinical variables namely age, HDL cholesterol, sex, arterial hypertension, and smoking to stratify the risk of ASCVD and it has only been developed and validated in retrospective cohorts. Furthermore, The SAFEHEART-RE (Spanish Familial Hypercholesterolemia Cohort)¹⁵ was realized in a prospective cohort of Spanish FH subjects, but included both subjects in primary and secondary cardiovascular prevention. The FH-risk-score is the first score to predict cardiovascular death and could offer personalized cardiovascular risk assessment and treatment for patients with FH.¹³

Patients with previous cardiovascular events: how to prevent further events? Residual cardiovascular disease risk

The identification of high-risk patients using this risk stratification tool is mandatory and allows to engage intensive treatments and follow-up strategies, representing an opportunity for major public health gain.¹⁶

According to American and European guidelines,^5,17 patients with established ASCVD have 20% or more absolute 10-year risk of developing recurrent vascular events, such as cardiovascular death, ischaemic stroke, or myocardial infarction.

The 10-year risk of cardiovascular events is not the same for all patients and ranges from low ranges (<10%) to extremely high (30%).

Residual CVD risk has been defined as the risk of ASCVD events that persists despite treatment for or achievement of targets for risk factors such as LDL cholesterol, blood pressure, and glycaemia.

Risk stratification tools for secondary prevention to predict long-term risk of developing recurrent vascular events include the secondary manifestations of arterial disease (SMART) 2 risk score¹⁸ and the European Action on Secondary and Primary Prevention by Intervention to Reduce Events (EUROASPIRE) risk model (Figure 3).¹⁷

The 10-year risk of recurrent ASCVD events [including coronary artery disease (CAD), cerebrovascular disease, peripheral artery disease, abdominal aortic aneurysm and poly-vascular disease] in patients with established ASCVD can be estimated with the SMART risk score.¹⁹ It was developed in a population of vascular patients in the Netherlands and was external validated in three different cohorts of 18 436 patients with established CVD. The variables included in the model are age, sex, current smoking, diabetes, blood pressure, cholesterol, CAD, cerebrovascular disease, peripheral artery disease, creatinine, and high-sensitivity C-reactive protein. An updated existing tool (SMART 2 risk score) was proposed to broaden generalizability across regions: it was recalibrated with regional incidence rates and validated in external populations.¹⁸

The EUROASPIRE Risk Calculator¹⁷ estimates 2-year risk of recurrent CVD in patients with stable CAD to optimize their management in order to prevent further diseases or death. Data were available for 12 484 patients after a median follow-up time of 1.7 years. This model is mainly driven by comorbidities including diabetes, renal insufficiency, and dyslipidaemia, but also symptoms of depression and anxiety.²⁰

The REduction of Atherothrombosis for Continued Health (REACH) Registry²¹ demonstrated that, in addition to the traditional risk factor, the burden of disease, lack of treatment, and geographical location are all related to an increased risk of cardiovascular morbidity and mortality and validated a risk score to estimate the risk of major adverse cardiovascular events. It offers an opportunity to evaluate the prevalence and clinical consequences of atherothrombosis in a wide range of patients from different regions around the world.

Lifetime risk calculators

Lifetime risk is defined as a measure of the risk that a certain event will happen during a person’s lifetime.

Risk estimation is critical to identify patients who may benefit from the implementation of healthy behaviours and pharmacological therapy to decrease the risk of unfavourable clinical outcomes and future cardiovascular events.

Risk-factor burden, competing risks, and treatment duration establish the benefit obtained by preventive therapy. A LIFEtime-perspective CardioVascular Disease (LIFE-CVD) model²² was developed for the estimation of individual-level 10 years and lifetime treatment effects of cholesterol lowering, blood pressure lowering, antithrombotic therapy, and smoking cessation in apparently healthy people.

The 2021 ESC Guidelines on CVD prevention recommend the use of lifetime risk prediction models to aid decisions regarding intensified preventive treatment options in adults with Type 2 diabetes.²³ The recalibrated DIAbetes Lifetime perspective 2 (DIAL2) model, an update version of DIAL model, provides a useful tool for the prediction of CVD-free life expectancy and lifetime CVD risk for people with Type 2 diabetes without previous CVD in the European low- and moderate-risk regions aged 30–85 years.

The DIAL2 model has several advantages and added clinical relevance when compared with the previously published DIAL model. Ten-year predictions from SCORE2-Diabetes and lifetime predictions from DIAL2 can be consistently used in parallel, allowing a tailored management of patients.²⁴

On the basis of the previous studies, the innovative SMART-REACH model was developed for life expectancy for patients with clinically manifest coronary, cerebrovascular, and/or peripheral artery disease but without recurrent cardiovascular events. Predictors were sex, smoking, DM, SBP, total cholesterol, creatinine, number of CVD locations, atrial fibrillation, and heart failure (Figure 4).²⁵

The role of lipoprotein(a) in the risk prediction of cardiovascular disease

Epidemiologic and genetic studies support a causal and continuous association between Lp(a) concentration and cardiovascular outcomes in different ethnicities. As recommended by European Atherosclerosis Society consensus statement, Lp(a) should be measured at least once in adults and results interpreted in the context of a patient’s absolute global cardiovascular risk, with recommendations on intensified early risk factor management by lifestyle modification.²⁶

Recently, a new risk calculator was introduced and includes Lp(a) value together with traditional cardiovascular risk factors; however, further studies are needed to evaluate the role of the Lp(a) in the better risk prediction of CVD. The model is available online for patients and clinicians (https://www.lpaclinicalguidance.com/) and can be used to facilitate clinical discussion and implementation of preventive measures in common practice.

Digital technology for risk factor management

Internet and smart phone use has grown exponentially in the past decade. However, despite their popularity and potential, there is a lack of evidence about the effectiveness and usefulness of these technologies in the management of risk factors and the implementation of healthy behaviours.²⁷ The WHO defines digital as the field of knowledge and practice associated with the development and use of digital technologies to improve health (DH).²⁸ Digital health interventions (DHI), including telemedicine, web-based strategies, email, mobile phones, mobile applications, text messaging, and monitoring sensors, promote health awareness and adoption of a healthier lifestyle through data transfer between patients and clinicians.²⁷ Several studies have reviewed the use of DHI as a powerful tool and have demonstrated the effectiveness of DH on smoking, physical activity, weight loss, and blood pressure–lowering intervention in patients with CVD.^29,30 Further research is required in order to evaluate the potential benefit in specific cardiovascular risk populations.

Conclusion

A contemporary preventive cardiology programme appropriately tailored to each patient is necessary for prevention of CVD. Decisions about the start or intensification of preventive treatment should be made based on the expected clinical benefit obtained by prediction models. However, existing risk calculators, occasionally, fail to estimate the real risk. Emerging non-traditional markers should be considered and mentioned in a tailored approach in risk stratification in order to identify high- and very high-risk patients. An integrated, interdisciplinary method is crucial for preventing ASCVD, involving contributions from multiple disciplines and areas of expertise, in a patient- and family-centric approach.

All algorithms are available via the ESC CVD risk calculation app and as an interactive online calculator www.U-Prevent.com.

Funding

None declared.

Data availability

No new data were generated or analysed in support of this research.

References

Author notes