American College of Cardiology Foundation

11/21/2019 | Press release | Distributed by Public on 11/21/2019 14:41

Risk Assessment for Cardiovascular Disease Prevention: Comparing the American and European Approaches

Introduction

A cornerstone of primary prevention of atherosclerotic cardiovascular disease (ASCVD) is estimation of the absolute risk of developing a hard, cardiovascular event (myocardial infarction/cerebrovascular accident) over the next decade. In clinical practice, such estimation helps clinicians identify patients at high enough risk to obtain net benefit from preventive interventions ranging from aggressive lifestyle modification to pharmacological therapies (i.e. statin, aspirin, and/or antihypertensive medications).

The use of clinical risk estimation scores in primary prevention has been extensively assessed. Indeed, over the past two decades, several risk-estimation tools have been developed and implemented in key guidelines worldwide. A recent systematic review that included 41 randomized, controlled trials showed that providing quantitative cardiovascular risk assessment to clinicians and patients provided statistically significant (although modest) beneficial effects on cardiovascular risk factors and lowered subsequent estimated cardiovascular risk at follow-up without harm.1 While there are no outcomes trials available, a cluster-randomized trial is underway to assess the impact of using cardiovascular risk scores on cardiovascular disease (CVD) event rates in very-high-risk Medicare beneficiaries.2

Risk Scores in the United States

1) Pooled Cohort Equations

The sex- and race-specific pooled cohort equations (PCE) estimate the 10-year risk for hard ASCVD events (coronary death, nonfatal myocardial infarction, or fatal/nonfatal stroke). The PCE estimates are fairly well calibrated near decision thresholds (i.e. 7.5 to 10%) for individuals from the broad US population, in both men and women.3-5 Furthermore, the performance of the PCE has been extensively examined in external validation samples.6-8

However, some major limitations and weaknesses have been described:

  • Suboptimal calibration in contemporary US cohorts such as the Multi-Ethnic Study of Atherosclerosis (MESA).8
  • Variable performance in racially-diverse non-US populations.6-8
  • Overestimation of risk in groups with predicted 10-year risk >10%, and in subpopulations more likely to receive preventive pharmacologic therapy (i.e. higher socioeconomic status).9
  • Underestimation of risk in patients with chronic inflammatory diseases (lupus, rheumatoid arthritis, advanced psoriasis, HIV).

2) Reynolds Risk Score

The Reynolds Risk Score, rarely used clinically, was obtained from a cohort of white individuals and includes other variables (i.e. family history, high‐sensitivity C‐reactive protein (hsCRP). It performed better than PCE in several cohorts of individuals with higher socioeconomic status.10

3) Lifetime Risk Estimation

In addition to short-term risk prediction (PCE), the AHA/ACC 2019 guidelines also recommend 30-year risk assessment to better estimate the long-term implications of the aggregate burden of risk factors. This tool is particularly beneficial in younger individuals (<50 years of age) who may have low short-term risk but elevated lifetime risk, who could benefit from early more aggressive lifestyle changes and possibly earlier consideration of certain pharmacologic therapies.11

AHA/ACC Multisociety Guideline for Initial and Further Refinement of Risk Assessment

The 2018 Cholesterol Clinical Practice Guidelines and 2017 Hypertension Clinical Practice Guidelines recommend using the PCE for most adults in the primary prevention setting (except those with familial hypercholesterolemia or LDL-C ≥190 mg/dL) to guide decision-making with regards to pharmacologic treatment initiation based on patient's preferences.12,13

It is important to note that multivariable cardiovascular risk prediction scores carry some inherent imprecision, which is particularly relevant in groups at high risk of under- or over-treatment. Therefore, further refinement of cardiovascular risk can be performed in the presence of risk enhancers, and/or identification of subclinical atherosclerosis using coronary artery calcium (CAC) scoring.14,15

Over the past decade, a considerable amount of evidence has demonstrated that selective use of CAC measurement in addition to the PCE improves discrimination, calibration, and net reclassification. This is especially the case among patients with estimated intermediate-risk who have CAC score ≥100 Agatston units or ≥75th percentile for age, sex and race/ethnicity, who are then expected to have greater benefit from statin therapy due to 'up-risking'. Conversely, intermediate-risk patients who have a CAC score of 0 have low observed 10-year event rates that fall below the range where statins may provide net benefit (allowing for 'de-risking' or 'down-risking').

The use of CAC score in particular populations is described below:

  • Low (0-<5%) and borderline-risk (5-<7.5%) patients: the yield of CAC measurements resulting in scores ≥100 is overall very low; therefore, the utility of CAC measurement in these groups is limited. However, the Society of Cardiovascular Computed Tomography indicates that CAC scanning may be useful in those with a family history of premature ASCVD and an ASCVD estimate of <5%.16
  • Intermediate-risk patients (7.5%-<20%): CAC measurement can be effective in reclassifying risk in a large proportion of individuals, because those who have CAC ≥100 (or >75th percentile for age/sex/race/ethnicity) have event rates in the range where the benefit of statin therapy would clearly exceed any potential side effects. On the other hand, those with CAC=0 appear to have considerably lower 10-year event rates, suggesting that pharmacologic therapy would be of limited near-term value. For those with CAC 1-99, reclassification is modest although they likely remain in a statin benefit group (especially when the score is above the 50th percentile for one's age and sex), so clinical judgement and patient preferences should guide decision-making. Repeat CAC measurement in 5 years may be considered in untreated patients with a CAC of 0 in order to reassess CAC progression.
  • High risk patients (≥20%): the yield of CAC=0 is very small, and reclassification by CAC of 0 generally does not result in subgroups with 10-year event rates below 7.5% Thus, it is not recommended to order a CAC measurement in those already classified as high risk.

The European Recommendations for Risk Assessment

Similar to the AHA/ACC guidelines, the 2016 European counterpart recommends risk prediction tools to provide objective risk estimates that can assist health professionals.17 The current 2019 European guidelines recommend the Systematic COronary Risk Evaluation (SCORE) to predict 10-year risk of cardiovascular death, or QRISK® (in the United Kingdom) to predict a composite outcome of coronary heart disease, ischemic stroke, or transient ischemic attack.18 The ESC Primary Prevention Guideline recommends using HeartScore®, the interactive version of the SCORE risk charts that offers risk calculation and management advice in 17 languages.17

Similarly, since endpoint definitions differ across guidelines, the cut-off values also differ. The European guidelines recommend classification based on cardiovascular mortality risk: low (<1%), moderate (≥1 to <5%), high (≥5% to <10%) or very high (≥10%). Potential reclassification factors are socioeconomic status, family history of premature CVD, body mass index, CAC score, presence of atherosclerotic plaque in the carotid arteries, and a low ankle-brachial index (ABI).17

The European guidelines also recommend systematic cardiovascular risk assessment in men older than 40 years old, women older than 50 years old, and post-menopausal women with no known cardiovascular risk factors. The European guidelines recommend informing younger adults about their cardiovascular risk by using a relative risk chart that compares the risk of a patient with several cardiovascular risk factors to the risk of others of the same age with ideal levels of risk factors.

As with the PCE, SCORE and QRISK® have several weaknesses and limitations:

  • SCORE and QRISK® are not necessarily generalizable to the global population, since they were derived from European populations.
  • The original SCORE risk algorithm cannot be used in individuals older than 65 years old.18,19

Unlike American guidelines, European preventive cardiology societies have attempted to develop risk estimators for special populations:

  • Elderly populations: the JBS3 risk calculator and the elderly risk score account for competing non-vascular mortality.18,20
  • Patients with diabetes mellitus: the ADVANCE-risk score takes into account diabetes-specific variables, such as hemoglobin A1C, albuminuria, presence of retinopathy, atrial fibrillation, and duration of diabetes in addition to classic risk factors in order to provide more accurate estimations.21
  • Patients with vascular disease: The SMART risk score includes variables such as the number of vascular disease locations, kidney function, hsCRP, and number of years since first diagnosis of vascular disease.22
  • Patients with heart failure: the MAGGIC risk calculator estimates the 1- and 3-year all-cause mortality for patients with heart failure.

To facilitate the use of these tools in clinical practice, the European guidelines recommend using the U-Prevent tool. U-Prevent is an interactive website that incorporates risk calculators for multiple categories of patients, including LIFE-CVD for lifetime risk estimation, SMART-REACH for vascular patients aged 45-80 years, and the DIAL model for patients with diabetes aged 30-85 years old. All U-Prevent risk algorithms have been extensively validated in contemporary European and North American populations, and geographical updates are applied when appropriate. The U-prevent lifetime tools can be used to estimate the effect of specific treatment options, such as changing a statin dose or adding aspirin.

Highlights

  • Practical Clinical Tool. Both European and AHA/ACC CVD risk estimations offer essential tools for short-term risk and elevated lifetime risk that clinicians are recommended to use to inform decision-making around early, more aggressive lifestyle changes and possibly earlier consideration of certain pharmacologic therapies.1
  • Individualize CVD Risk. Both US and European guidelines emphasize a personalized approach to estimating CVD risk. The European guidelines account for elderly patients (JBS3 risk calculator)8,20 and comorbidities including diabetes mellitus (ADVANCE-risk score),21 vascular disease (SMART risk score),22 and heart failure (MAGGIC risk calculator). European 'risk modifiers' include socioeconomic status, family history, Body Mass Index (BMI), and diagnostic evidence of CVD (ex. CAC, ABI, carotid ultrasound). AHA/ACC guidelines recommend consideration of CAC for risk assessment in persons whose estimated ASCVD risk is uncertain to 'up-risk' or 'de-risk' a patient's risk status. Additionally, AHA/ACC defined 'risk enhancers' (i.e. family history of premature ASCVD, metabolic syndrome, primary hypercholesterolemia, chronic inflammatory conditions) to individualize risk prediction.
  • Risk Estimation Endpoints. A key difference between AHA/ACC (PCE) and European (SCORE) risk estimation tools is the endpoint. AHA/ACC ASVD risk estimation uses the endpoint of fatal and non-fatal myocardial infarction and stroke in comparison to European SCORE CVD risk estimation that uses the hard endpoint of cardiovascular death.
  • Future Research. Currently no outcome trials have investigated the role of cardiovascular risk estimation on CVD event rates. A cluster-randomized trial is in progress studying the impact of cardiovascular risk scores on CVD event rates in very-high-risk Medicare beneficiaries.2 Future studies are needed to evaluate CVD risk scores and correlation with CVD event rates, morbidity, and mortality.

References

  1. Karmali KN, Persell SD, Perel P, Lloyd-Jones DM, Berendsen MA, Huffman MD. Risk scoring for the primary prevention of cardiovascular disease. Cochrane Database Syst Rev 2017;3:CD006887.
  2. Sanghavi DM, Conway PH. Paying for prevention: a novel test of medicare value-based payment for cardiovascular risk reduction. JAMA 2015;314:123-24.
  3. Dalton JE, Perzynski AT, Zidar DA, et al. Accuracy of cardiovascular risk prediction varies by neighborhood socioeconomic position: a retrospective cohort Study. Ann Intern Med 2017;167:456-64.
  4. Muntner P, Colantonio LD, Cushman M, et al. Validation of the atherosclerotic cardiovascular disease pooled cohort risk equations. JAMA 2014;311:1406-15.
  5. Wolfson J, Vock DM, Bandyopadhyay S, et al. Use and customization of risk scores for predicting cardiovascular events using electronic health record data. J Am Heart Assoc 2017;6pi:e003670.6
  6. Andersson C, Enserro D, Larson MG, Xanthakis V, Vasan RS. Implications of the US cholesterol guidelines on eligibility for statin therapy in the community: comparison of observed and predicted risks in the Framingham Heart Study Offspring Cohort. J Am Heart Assoc 2015;4:e001888.
  7. Chia YC, Lim HM, Ching SM. Validation of the pooled cohort risk score in an Asian population - a retrospective cohort study. BMC Cardiovasc Disord 2014;14:163.
  8. DeFilippis AP, Young R, Carrubba CJ, et al. An analysis of calibration and discrimination among multiple cardiovascular risk scores in a modern multiethnic cohort. Ann Intern Med 2015;162:266-75.
  9. Colantonio LD, Richman JS, Carson AP, et al. Performance of the atherosclerotic cardiovascular disease pooled cohort risk equations by social deprivation status. J Am Heart Assoc 2017;6:e005676.
  10. Ridker PM, Paynter NP, Rifai N, Gaziano JM, Cook NR. C-reactive protein and parental history improve global cardiovascular risk prediction: the Reynolds Risk Score for men. Circulation 2008;118:2243-51.
  11. Marma AK, Berry JD, Ning H, Persell SD, Lloyd-Jones DM. Distribution of 10-year and lifetime predicted risks for cardiovascular disease in US adults: findings from the National Health and Nutrition Examination Survey 2003 to 2006. Circ Cardiovasc Qual Outcomes 2009;3:8-14.
  12. 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. Circulation 2019;139:e1082-143.
  13. Whelton PK, Carey RM, Aronow WS, et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: Executive Summary: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Hypertension 2018;71:1269-1324.
  14. Yeboah J, Polonsky TS, Young R, et al. Utility of nontraditional risk markers in individuals ineligible for statin therapy according to the 2013 American College of Cardiology/American Heart Association Cholesterol Guidelines. Circulation 2015;132:916-922.
  15. Yeboah J, Young R, McClelland RL, et al. Utility of nontraditional risk markers in atherosclerotic cardiovascular disease risk assessment. J Am Coll Cardiol 2016;67:139-147.
  16. Dudum R, Dzaye O, Mirbolouk M, et al. Coronary artery calcium scoring in low risk patients with family history of coronary heart disease: validation of the SCCT guideline approach in the coronary artery calcium consortium. J Cardiovasc Comput Tomogr 2019;13:21-25.
  17. Piepoli MF, Hoes AW, Agewall S, et al. 2016 European Guidelines on cardiovascular disease prevention in clinical practice: The Sixth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of 10 societies and by invited experts) Developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR). Eu Heart J 2016;37:2315-81.
  18. JBS3 Board. Joint British Societies' consensus recommendations for the prevention of cardiovascular disease (JBS3). Heart 2014;100:ii1-ii67.
  19. Cooney MT, Selmer R, Lindman A, et al. Cardiovascular risk estimation in older persons: SCORE O.P. Eur J Prev Cardiol 2016;23:1093-103.
  20. Stam-Slob MC, Visseren FL, Wouter Jukema J, et al. Personalized absolute benefit of statin treatment for primary or secondary prevention of vascular disease in individual elderly patients. Clin Res Cardiol 2017;106:58-68.
  21. Kengne AP, Patel A, Marre M, et al. Contemporary model for cardiovascular risk prediction in people with type 2 diabetes. Eur J Cardiovasc Prev Rehabil 2011;18:393-98.
  22. Dorresteijn JA, Visseren FL, Wassink AM, et al. Development and validation of a prediction rule for recurrent vascular events based on a cohort study of patients with arterial disease: the SMART risk score. Heart 2013;99:866-72.

Clinical Topics:Arrhythmias and Clinical EP, Diabetes and Cardiometabolic Disease, Dyslipidemia, Heart Failure and Cardiomyopathies, Noninvasive Imaging, Prevention, Atrial Fibrillation/Supraventricular Arrhythmias, Homozygous Familial Hypercholesterolemia, Lipid Metabolism, Nonstatins, Novel Agents, Primary Hyperlipidemia, Statins, Acute Heart Failure, Heart Failure and Cardiac Biomarkers, Hypertension

Keywords:Albuminuria, Ankle Brachial Index, Antihypertensive Agents, Arthritis, Rheumatoid, Atherosclerosis, Atrial Fibrillation, Aspirin, Algorithms, Body Mass Index, Calibration, Brain Ischemia, Carotid Arteries, Cardiovascular Diseases, Comorbidity, Cholesterol, Coronary Disease, C-Reactive Protein, Decision Making, Coronary Vessels, Factor X, Diabetes Mellitus, Follow-Up Studies, Hemoglobin A, Heart Failure, Hydroxymethylglutaryl-CoA Reductase Inhibitors, HIV-2, Hypercholesterolemia, Hyperlipoproteinemia Type II, Hypertension, Ischemic Attack, Transient, Medicare, Life Style, Metabolic Syndrome X, Myocardial Infarction, Patient Preference, Plaque, Atherosclerotic, Primary Prevention, Postmenopause, Risk Assessment, Risk Factors, Social Class, Psoriasis, Stroke, Tomography


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