Risk estimation is critical to identify patients who may benefit from more aggressive lifestyle interventions and pharmacological therapy for the prevention of cardiovascular disease (CVD). To serve this purpose, risk score models, such as the Framingham Risk Score (FRS),¹ Systematic COronary Risk Evaluation (SCORE),² QRISK,³ and the Pooled Cohort Equation (PCE),⁴ were created to prognosticate risk and provide guidance for informed decision-making regarding initiation or intensification of preventive strategies.⁵ However, these risk scores were derived from older cohorts, which may result in overestimation of risk due to the change in prevalence of risk factors and suboptimal validation in contemporary cohorts.⁵

In addition, age is the primary driver of risk in current score models. Therefore, younger individuals generally have lower calculated short-term (10-year) risk, which may delay initiating preventive strategies in what could be the golden window for effective prevention in younger individuals with traditional CVD risk factors. To address this concern, prior lifetime risk score models, such as QRISK-lifetime,³ extended the horizon for CVD risk prediction. However, these models are not adjusted for competing risk of death and are still prone to risk overestimation as they were derived from older cohorts. In addition, previous risk scores do not provide estimations of CVD-free life expectancy.

To address the deficiencies in prior risk score models, in a recent paper published in the European Heart Journal, Jaspers et al. introduced LIFE-CVD.⁶ This new risk score calculator is derived from Multi-Ethnic Study of Atherosclerosis (MESA), which is a modern North American cohort. LIFE-CVD prognosticates both the 10-year and lifetime CVD risk and estimates the potential increase in CVD-free life expectancy with preventive interventions, including initiation or intensifying statin therapy, lowering systolic blood pressure, initiating aspirin or equivalent antithrombotic therapy and smoking cessation. The LIFE-CVD risk score is validated for individuals between 45-80 years old. Acceptable results for external validation of this risk score model in American and European cohorts supports its international generalizability for individuals with different ages and racial/ethnic background. This tool is available online for patients and clinicians (www.U-Prevent.com).

As per prior risk score models, LIFE-CVD demonstrates that increasing age is a negative prognosticator of CVD risk. LIFE-CVD reveals that with increasing age, although a higher 10-year absolute CVD risk reduction is observed after implementation of lifestyle or pharmacological interventions, a lower benefit is achieved with the gain in CVD-free life expectancy. Figure 1 illustrates the gain in CVD-free life expectancy for a 45-year-old patient compared to a 75-year-old patient with similar risk factor burden. As the atherosclerotic processes start in young adulthood,⁷ this illustration supports that risk score models should ideally accurately assess younger patients, who are more likely to benefit from preventive interventions and optimal management of risk factors. Physicians should continue to emphasize earlier lifestyle and pharmacological interventions even for patients younger than 45 years of age who have CVD risk factors.

Figure 1

Risk score models, such as LIFE-CVD, assume a constant risk factor profile with the exception of age. When communicating risk to patients, in particular younger patients at lower risk, it is important to explain that one's risk factor profile may change over time. For example, a young patient with high BMI but no other CVD risk factors is at increased risk of diabetes and hypertension, but their long-term CVD risk is often not discussed adequately in periodic medical visits. Since competing risk factors would change the estimated CVD-free life expectancy and result in change in preventive treatment strategies, an earlier comprehensive discussion regarding lifestyle modification is needed for risk factor optimization. Also, physicians should employ a more dynamic approach with more frequent CVD risk estimation to be able to capture increased lifetime CVD risk sooner. Early identification and management of risk factors would result in lower exposure time to risk factors and a lower probability of developing evident clinical CVD.

While the LIFE-CVD risk score is a valuable improvement over prior tools, more work is required to create a comprehensive risk tool. First, like other risk factor models, LIFE-CVD does not include the duration and severity of exposure to risk factors such as smoking, hypertension, diabetes and hyperlipidemia. Second, it also overlooks certain lifestyle preventive interventions, such as weight loss, dietary modifications and exercise duration (such as minutes per week), which hinder a clinician's ability to accurately calculate the protective benefits of these changes. Third, it does not include risk enhancing factors, such as inflammatory diseases and chronic kidney disease, that are known to increase CVD risk.⁸

To address these limitations, it would be fruitful to include lifetime exposures to known risk factors. Coronary artery calcium (CAC) score has been shown to have strong performance for CVD risk stratification, and is considered a valuable indirect measure of cumulative exposure to risk factors.⁹ Since CAC is not readily available for all patients, its optional inclusion in risk calculation for better CVD risk prediction may be considered. This feature is available in more recent risk score models such as MESA 10-year coronary heart disease risk calculator.¹⁰

Additionally, as technology generates vast longitudinal data on weight, diet (such as shopping lists) and exercise (such as steps per day), utilizing these data would avail more accurate assessment of these important preventive measures and their clinical benefit. Finally, inclusion of risk enhancing clinical conditions that accelerate CVD will be an important step to creating a comprehensive calculator. Table 1 summarizes new advantages of LIFE-CVD risk score model as well as expectations from a future lifetime ideal risk score model.

The LIFE-CVD risk score is one of the most novel and worthwhile efforts to quantify ASCVD risk. Its use of widely available clinical data, combined with informative graphic layouts, facilitates clinical discussion and implementation of preventive measures in common practice. While this tool improves risk assessment, continued effort is needed to further improve future tools to include cumulative risk exposure and additional preventive strategies (such as weight less, diet and exercise) and known risk enhancing factors.

Table 1: Distinguishing Features in LIFE-CVD Risk Score Model and Expectations for Features Lacking in Current Risk Score Models

Advantages of LIFE-CVD	Future Expectations
Estimating short-term 10-year CVD risk	Ability to estimate risk in younger individuals to guide earlier implementation of preventive strategies to those who will benefit the most
Estimating lifetime CVD-free life expectancy	Ability to consider duration and severity of individual risk factor exposures
Estimating absolute risk reduction with preventive strategies
Estimating CVD-free years gained following preventive treatment strategies	Inclusion of other strong CVD predictors such as coronary artery calcium (CAC)
Ability to assess the effect of individual treatment strategies on CVD free years gained	Inclusion of other residual risk factors that contribute to lifetime risk such as inflammatory disorders, chronic kidney disease
Ability to assess the effect of different doses of various cholesterol lowering therapies, which allows discussion for withholding, de-escalating or intensifying treatment	Optional inclusion of biomarkers associated with CVD such as hsCRP, Apo B, and Lp(a)
Derivation from a modern multi-ethnic cohort using readily available demographic and clinical characteristics	Cost-effectiveness analysis to communicate the costs of initiating or delaying preventive strategies
Generalizability: Good internal and external validation in international cohorts	Independent external validation
Adjustment for competing risk of death
Improved quality and presentation of information to aide in discussion justifying initiation or intensification of treatment and reinforce adherence to preventive strategies

References

1. Sullivan LM, Massaro JM, D'Agostino RB Sr. Presentation of multivariate data for clinical use: the Framingham Study risk score functions. Stat Med 2004;23:1631-60.
2. Conroy RM, Pyorala K, Fitzgerald AP, et al. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J 2003;24:987-1003.
3. Hippisley-Cox J, Coupland C, Robson J, Brindle P. Derivation, validation, and evaluation of a new QRISK model to estimate lifetime risk of cardiovascular disease: cohort study using QResearch database. BMJ 2010;341:c6624.
4. Lloyd-Jones DM, Huffman MD, Karmali KN, et al. Estimating longitudinal risks and benefits from cardiovascular preventive therapies among Medicare patients: the Million Hearts Longitudinal ASCVD Risk Assessment Tool: a special report from the American Heart Association and American College of Cardiology. J Am Coll Cardiol 2017;69:1617-36.
5. Matheny M, McPheeters ML, Glasser A, et al. Systematic review of cardiovascular disease risk assessment tools. U.S. Preventive Services Task Force Evidence Syntheses 2011.
6. Jaspers NEM, Blaha MJ, Matsushita K, et al. Prediction of individualized lifetime benefit from cholesterol lowering, blood pressure lowering, antithrombotic therapy, and smoking cessation in apparently healthy people. Eur Heart J 2019. [Epub ahead of print]
7. Berenson GS, Srinivasan SR, Hunter SM, et al. Risk factors in early life as predictors of adult heart disease: the Bogalusa Heart Study. Am J Med Sci 1989;298:141-51.
8. Grundy SM, Stone NJ, Bailey AL, et al. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA guideline on the management of blood cholesterol: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol 2018. [Epub ahead of print]
9. Hoffmann U, Massaro JM, D'Agostino RB Sr., Kathiresan S, Fox CS, O'Donnell CJ. Cardiovascular event prediction and risk reclassification by coronary, aortic, and valvular calcification in the Framingham Heart Study. J Am Heart Assoc 2016;22:5.
10. McClelland RL, Jorgensen NW, Budoff M, et al. 10-year coronary heart disease risk prediction using coronary artery calcium and traditional risk factors: derivation in the MESA (Multi-Ethnic Study of Atherosclerosis) with validation in the HNR (Heinz Nixdorf Recall) study and the DHS (Dallas Heart Study). J Am Coll Cardiol 2015;66:1643-53.

Clinical Topics: Anticoagulation Management, Cardiovascular Care Team, Diabetes and Cardiometabolic Disease, Dyslipidemia, Prevention, Nonstatins, Novel Agents, Statins, Diet, Hypertension, Smoking

Keywords: Dyslipidemias, Risk Factors, Blood Pressure, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Weight Loss, Fibrinolytic Agents, Life Expectancy, Smoking Cessation, Aspirin, Body Mass Index, Factor IX, Coronary Vessels, Risk Assessment, Hypertension, Life Style, Diabetes Mellitus, Smoking, Hyperlipidemias, Atherosclerosis, Diet, Renal Insufficiency, Chronic, Decision Making, Coronary Disease, Risk Reduction Behavior, Cohort Studies

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