New approaches to the implementation of cardiovascular disease prevention

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UvA-DARE (Digital Academic Repository)

Jørstad, H.T.

Publication date

2016

Document Version

Final published version

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Jørstad, H. T. (2016). New approaches to the implementation of cardiovascular disease

prevention. Boxpress.

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NEW APPRO A CHE S T O THE IMPLEMENT A TION OF C ARDIO V A SCULAR DISEA SE PREVENTION HAR ALD THUNE JØRS TAD

NEW APPROACHES TO

THE IMPLEMENTATION OF

CARDIOVASCULAR DISEASE

PREVENTION

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004

NEW APPROACHES TO THE IMPLEMENTATION OF CARDIOVASCULAR DISEASE PREVENTION

Thesis, Academic Medical Center - University of Amsterdam, Amsterdam, The Netherlands.

ISBN/EAN: 9789462953949

Author: Harald Thune Jørstad

Cover: Guido Lagerweij

Lay-out: Jeroen de Rooij | NextGear

Printed by: Uitgeverij BOXPress || Proefschriftmaken.nl

Published by: Uitgeverij BOXPress, ‘s-Hertogenbosch

Financial support by the Dutch Heart Foundation, University of Amsterdam, and Stichting Amstol for the publication of this thesis is gratefully acknowledged.

The work was financially supported by an unrestricted educational grant from AstraZeneca. Additional financial support for publication of this thesis was provided by: Amgen, Astellas, Bayer, Boehringer, Genzyme, Pfizer, Sanofi, and Servier.

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NEW APPROACHES TO THE IMPLEMENTATION OF CARDIOVASCULAR DISEASE PREVENTION

ACADEMISCH PROEFSCHRIFT

ter verkrijging van de graad van doctor aan de Universiteit van Amsterdam op gezag van de Rector Magnificus

prof. dr. D.C. van den Boom

ten overstaan van een door het College voor Promoties ingestelde commissie, in het openbaar te verdedigen in de Agnietenkapel

op donderdag 30 juni 2016, te 10:00 uur door Harald Thune Jørstad geboren te Trondheim, Noorwegen

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PROMOTIECOMMISSIE

Promotores: Prof. dr. R.J.G. Peters Universiteit van Amsterdam

Prof. dr. J.G.P. Tijssen Universiteit van Amsterdam

Copromotores: Dr. S.M. Boekholdt Universiteit van Amsterdam

Prof. dr. W.J.M. Scholte op Reimer Universiteit van Amsterdam

Overige leden: Prof. dr. R.J. de Winter Universiteit van Amsterdam

Prof. dr. L.J. Gunning-Schepers Universiteit van Amsterdam

Prof. dr. J.B.L. Hoekstra Universiteit van Amsterdam

Prof. dr. H.C.P.M. van Weert Universiteit van Amsterdam

Dr. C.S. Jennings Imperial College London

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008 NEW APPROACHES TO THE IMPLEMENTATION OF

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CARDIOVASCULAR DISEASE PREVENTION TABLE OF CONTENTS

PART

01 PART

02

Introduction and general outline

The Systematic COronary Risk Evaluation (SCORE) in a large UK population: 10-year follow-up in the EPIC-Norfolk prospective population study

Jørstad HT, Colkesen EB, Minneboo M, Peters RJ, Boekholdt SM, Tijssen JG, Wareham NJ, Khaw KT

European Journal of Preventive Cardiology, 2013

RESPONSE study: Randomised Evaluation of Secondary Prevention by Outpatient Nurse SpEcialists

Study design, objectives and expected results

Jørstad HT, Alings AM, Liem AH, von Birgelen C, Tijssen JG, de Vries CJ, Lok DJ, Kragten JA, Peters RJ.

Netherlands Heart Journal, 2009

Effect of a nurse coordinated prevention program on cardiovascular risk after an acute coronary syndrome: Main results of the RESPONSE trial

Jørstad HT, von Birgelen C, Alings AM, Liem A, van Dantzig JM, Jaarsma W, Lok DJ, Kragten HJ, de Vries K, de Milliano PA, Withagen AJ, Scholte Op Reimer WJ, Tijssen JG, Peters RJ.

Heart, 2013

Estimated 10-year cardiovascular mortality seriously underestimates overall cardiovascular risk: Observations from the EPIC-Norfolk prospective population study

Jørstad HT, Colkesen EB, Boekholdt SM, Tijssen JG, Wareham NJ, Khaw KT, Peters RJ

Heart, 2016

The Dutch SCORE-based risk charts seriously underestimate the risk of cardiovascular disease

Jørstad, HT, Boekholdt, SM, Wareham, NNJ, Khaw, KT, Peters, RJ

Accepted Netherlands Heart Journal, 2016

RISK ASSESSMENT IN

PRIMARY PREVENTION

NURSE COORDINATED SECONDARY

PREVENTION AFTER AN ACUTE

CORONARY SYNDROME

011 021 069 085 035 051 019 067

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CHAPTER 7

CHAPTER 8

CHAPTER 9

CHAPTER 10

Participation in a Nurse Coordinated Prevention Programme Improves Health Related Quality of Life and Reduces Depressive Symptoms in Patients with an Acute Coronary Syndrome: Results of the RESPONSE trial

Jørstad HT*, Minneboo M*, Helmes, H, Fagel ND, Scholte op Reimer, WJ, Tijssen JG, Peters RJ * contributed equally

Under review BMC cardiovascular disorders

Partial disclosure of study information to participating patients: Experiences from a randomized clinical trial

Jørstad HT, Tijssen JG, Peters RJ

Submitted

Nurses’ perspectives on nurse-coordinated prevention programmes in secondary prevention of cardiovascular disease: A pilot survey

Jørstad HT, Chan YK, Scholte Op Reimer WJ, Doornenbal J, Tijssen JG, Peters RJ

Contemporary Nurse 2015

Summary, discussion and future perspectives Nederlandse Samenvatting

Dankwoord List of Publications PhD Portfolio About the Author

107 123 137 151 157 167 175 179 181

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INTRODUCTION AND

GENERAL OUTLINE

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CARDIOVASCULAR DISEASE PREVENTION CHAPTER 1 NEW APPROACHES TO THE IMPLEMENTATION OF

CARDIOVASCULAR DISEASE PREVENTION

INTRODUCTION

Cardiovascular disease (CVD) is one of the biggest contemporary health problems worldwide. Mortality from CVD alone contributed to 17.3 million deaths in 2008, which represents 30% of all global deaths. Currently, 10% of the total global disease burden is attributed to CVD, and the World Health Organization predicts that both mortality and the total burden of CVD will increase

dramatically in the near future.1

The major risk factors for CVD are well known, and evidence-based primary (in apparently healthy individuals) and secondary (in individuals with clinical manifestations of the disease) prevention has been shown to decrease cardiovascular mortality and morbidity. Clear targets have been defined for healthy lifestyles, risk factors, and medication use by the international societies in Europe and

in the Unites States.2,3_{However, the implementation of primary and secondary prevention is}

cur-rently far from optimal. Therefore, new approaches to the implementation of cardiovascular disease prevention are needed.

Part 1: Risk assessment in primary prevention

The current approach to primary prevention of CVD is “case finding”, also known as the “high risk approach”. This approach focuses its efforts on identifying healthy individuals with the highest levels of CVD risk factors, and utilizes the established framework of medical services to reduce

this risk.4_{To aid this approach, several risk assessment tools have been developed. Using}

differ-ent algorithms based on differdiffer-ent fatal and non-fatal outcomes, risk can be calculated for the very short term, 10-years, or lifetime. Decisions to initiate preventive measures are based on this risk. However, the so-called prevention paradox coined by Geoffrey Rose (1926-1993) points to the fact the majority of CVD comes from the population at low or moderate risk, and only a minority

from the high-risk population.4_{This is because the number of individuals at high risk is small as}

compared with the number of individuals at low to moderate risk. As an alternative to the high-risk approach, Rose suggested the “population strategy”. This approach is a public health-oriented approach, which aims to shift the population distribution of one or more risk factors to reduce the total burden of CVD, as opposed to reducing a single individuals’ risk. One such strategy is the

polypill approach.5_{The polypill includes a combination of low-dose preventive medication (i.e.}

statin, aspirin, blood-pressure lowering agents), which theoretically can lead to a drastic reduction in CVD if implemented on a population level. Disadvantages to such population strategies are that each individual only reaps a small benefit, and that major changes on a societal level are needed for effective implementation

The European Society of Cardiology (ESC) guidelines on CVD prevention in clinical practice recommend treatment decisions to be made using the high risk approach, based on the predicted

10-year risk of CVD mortality.2,6_{This risk can be calculated using the Systematic COronary Risk}

Evaluation (SCORE) algorithm, which is based on the pooling of several large, European

popula-tion-based cohorts.7_{The SCORE algorithm includes age, sex, smoking status, systolic blood}

pres-sure, and serum total cholesterol or total/HDL-cholesterol ratio, and can be rapidly calculated using SCORE risk charts and online calculators. Risk charts have been published for high-risk countries and low-risk countries, in addition to country-specific calibrated versions. Based on data from the

World Health Organization,1_{the most recent ESC guidelines have reclassified the United Kingdom}

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of SCORE has not been studied in a large, population-based UK cohort. We therefore compared the predicted 10-year CVD mortality as calculated using the SCORE high-risk and low-risk algorithms with the observed 10-year CVD mortality in the European Prospective Investigation of Cancer-

Norfolk (EPIC-Norfolk) prospective population study.8_{(Chapter 2)}

The most recent ESC guidelines on CVD prevention suggest that there is a fixed relationship between CVD mortality and the total burden of CVD events, defined as the composite of fatal plus

non-fatal CVD.2,6_{It is suggested that in high-risk individuals with a 10-year CVD mortality risk of}

≥5%, as estimated using SCORE, total CVD (mortality plus morbidity) is threefold higher, and

pos-sibly more in young men, and less in women and in older individuals.2,6,9_{This has led to the}

sugges-tion of using a fixed multiplier (3×) for calculating total CVD based on CVD mortality only. From a

patient’s perspective, total CVD risk is the most relevant parameter for initiating CVD prevention,10

and using CVD mortality only can result in underestimation of the total CVD burden.10_Although

mortality is a more robust clinical outcome, CVD morbidity is equally relevant to providers of healthcare, policy makers and insurance companies. Currently, the relationship between total CVD and CVD mortality in the general population is unclear, and the proposed multiplier for conversion from CVD mortality to total CVD has not been validated. We therefore investigated the relationship between total CVD (fatal and non-fatal events) and CVD mortality in the EPIC-Norfolk prospective population study. (Chapter 3)

In the Netherlands, the current multidisciplinary guidelines on CVD risk management (CVRM) rec-ommend using a modified version of the Systematic COronary Risk Evaluation (SCORE) to

esti-mate 10-year risk of fatal and non-fatal CVD.11_{The original SCORE chart and algorithm on which}

the modified, current version is based is the low-risk SCORE,7_{which estimates 10-year risk of fatal}

CVD only. Using data from 2 different national cohorts,7,11,12_{multipliers have been calculated to}

convert the risk of 10-year fatal CVD to the risk of 10-year fatal- and non-fatal CVD, including first non-fatal hospitalizations for myocardial infarction, cerebrovascular disease and congestive heart

failure.11,12_{These multipliers are 5x the SCORE predicted fatal CVD for individuals aged 35-45}

years, 4x for individuals aged 45-65 years, and 3x for individuals aged >65 years. Overall risk is

presented in the charts, and coded by colour.11_{These multipliers have not been validated in other,}

large population-based studies, and include only 3 clinical manifestations of non-fatal CVD. Based on our findings in the EPIC-Norfolk study (Chapter 3), we applied the ratios of CVD mortality/ morbidity to the original SCORE low-risk charts to design a new, updated risk chart, and compared the updated risk chart with the current risk chart. (Chapter 4)

Part 2: Nurse coordinated secondary prevention after an acute

coronary syndrome

Patients with established coronary artery disease (CAD) are at particularly high risk of subsequent coronary events and death. Effective secondary prevention can reduce this risk. Modification of cardiovascular risk factors can reduce the risk of recurrent myocardial infarction, decrease the need

for interventional procedures, improve quality of life, and effectively extend survival.13

Comprehensive guidelines for the long-term management of patients with CAD have been issued

by the American Heart Association/American College of Cardiology (AHA/ACC)14_{and the}

European Society of Cardiology (ESC).2,15_{Effective secondary prevention includes interventions}

to change behavior and modify lifestyle (smoking cessation, regular exercise, weight control, and healthy food choices) and pharmaceutical interventions (antiplatelet agents, statins, β-blockers,

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CARDIOVASCULAR DISEASE PREVENTION CHAPTER 1

angiotensin converting enzyme inhibitors, and angiotensin receptor blockers).2,3,15-17_{In a}

system-atic review of lifestyle interventions in patients with CAD, a marked reduction in mortality risk was associated with smoking cessation (35-50%), physical activity (20-30%), moderate alcohol

consumption (15-20%) and healthy dietary choices (15-45%).18_{Risk reductions were seen in both}

CAD patients and in general population cohorts.18_{Furthermore, pharmacological interventions}

reduce the mortality risk in CAD patients: low-dose aspirin (18%),19_{statins (21%),}20_β-blockers

(23%),21_{and ACE inhibitors (26%).}22_{Combined, these interventions could potentially reduce the}

risk of recurrent events by more than two thirds.23

Unhealthy lifestyles (i.e. smoking, an unhealthy diet, overweight and insufficient physical activity) are among the most important of the modifiable risk factors for CVD. Ideally, an intervention in secondary prevention of CVD should be able to successfully improve these risk factors, as several other risk factors (i.e. hypertension, diabetes mellitus, dyslipidemia) improve along with healthier lifestyles. However, the results of the EUROASPIRE surveys (European Action on Secondary Pre-vention by InterPre-vention to Reduce Events) show that the implementation of secondary prePre-vention,

including successful lifestyle modification, is disappointing.24-26

There are several reasons why successful lifestyle modification in patients with CVD is difficult. First, individuals with clinically manifest CVD are generally middle-aged or older, and have spent decades developing their unhealthy lifestyles as individuals, part of their families, and within social networks. While an acute CVD event might motivate patients to improve their lifestyles, existing unhealthy lifestyle may be challenging to successfully modify in the short and long term. Second, physicians lack the time, motivation, and incentives to invest in strategies to improve patients’ lifestyles. In short, at present a considerable gap exists between guidelines on secondary prevention and the actual implementation of these measures.

One approach to improve secondary prevention may be to involve other allied professionals, with new initiatives such as nurse coordinated prevention programs. Potentially, nurses participating in such programs are motivated to follow guidelines, have more time for advising and counsel-ing patients, and can monitor and assist attempts to improve unhealthy lifestyles. We therefore designed the Randomised Evaluation of Secondary Prevention by Outpatient Nurse SpEcialists (RESPONSE) trial to quantify the impact of a practical, hospital-based nurse coordinated preven-tion programme integrated into the routine clinical care of patients who have sustained an acute coronary syndrome. (Chapters 5-9)

Aims of this thesis:

1. To investigate the performance of the Systematic COronary Risk Evaluation (SCORE)

in a contemporary, UK population based cohort, after the reclassification of the UK as a low-risk country; to investigate the relationship between CVD mortality and CVD morbidity in this cohort; to evaluate the consequences of this for the risk stratification in the Netherlands

2. To evaluate the effect of a nurse coordinated prevention programme on cardiovascular

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Outline of this thesis

Part I (chapters 2-4) Risk assessment in primary prevention

The first part of the thesis concerns risk assessment in primary prevention, in particular regarding the use of SCORE, and the consequences of including non-fatal outcomes in risk-assessment. In

chapter 2, we investigate whether the SCORE low-risk algorithm provides a more accurate risk

prediction of 10-year CVD mortality in a UK population than the high-risk algorithm, as the UK has recently been reclassified as a low-risk country. In chapter 3, we investigate the relationship between 10-year CVD mortality and 10-year CVD morbidity, and whether using mortality risk to calculate morbidity risk leads to an underestimation of the overall cardiovascular risk. In chapter 4, we evaluate the current Dutch SCORE-charts recommended by the national guidelines. Using the findings as presented in chapter 3, we evaluate whether the Dutch SCORE-charts underestimate an individuals’ risk of clinically relevant fatal- and non-fatal CVD.

Part II (chapters 5-9) Nurse coordinated secondary prevention

after an acute coronary syndrome

The second part of this thesis concerns the findings of the Randomised Evaluation of Secondary Prevention by Outpatient Nurse SpEcialists (RESPONSE) trial, a trial designed to quantify the impact of a practical, hospital-based nurse coordinated prevention programme integrated into the routine clinical care of patients who have sustained an acute coronary syndrome. In chapter 5, we present the study design, objectives and expected results of our randomized trial. In chapter 6, we present the main findings of our trial. In chapter 7, we present the effects of this programme on quality of life and depression. In chapter 8, we address the fact that patients participating in this tri-al received incomplete tritri-al information to minimize contamination and a so-ctri-alled “Hawthorne-ef-fect”, and present patients’ perspectives in participating in a trial with such a design. Finally, in

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REFERENCES

1. World Health Organization. Global Health Observatory Data Repository. http://apps.who.int/ghodata/ (2011, accessed 5 June 2012). WHO.

2. Perk J, De Backer G, Gohlke H, et al. European Guidelines on cardiovascular disease prevention in clinical practice (version 2012). The Fifth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of nine societies and by invited experts). European Heart Journal. 2012;33(13):1635-1701. doi:10.1093/ eurheartj/ehs092.

3. Smith SC. AHA/ACC Guidelines for Secondary Prevention for Patients With Coronary and Other Ath-erosclerotic Vascular Disease: 2006 Update: Endorsed by the National Heart, Lung, and Blood Institute. Circulation. 2006;113(19):2363-2372. doi:10.1161/CIRCULATIONAHA.106.174516.

4. Rose G. Strategy of prevention: lessons from cardiovascular disease. British medical journal (Clinical research ed). 1981;282(6279):1847-1851.

5. Wald NJ, Law MR. A strategy to reduce cardiovascular disease by more than 80%. BMJ. 2003;326(7404):1419–0. doi:10.1136/bmj.326.7404.1419.

6. Graham I, Atar D, Borch-Johnsen K, et al. European guidelines on cardiovascular disease prevention in clinical practice: executive summary: Fourth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (Constituted by representatives of nine societies and by invited experts). European Heart Journal. 2007;28(19):2375-2414. doi:10.1093/ eurheartj/ehm316.

7. Conroy RM, Pyörälä K, Fitzgerald AP, et al. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. European Heart Journal. 2003;24(11):987-1003.

8. Day N, Oakes S, Luben R, et al. EPIC-Norfolk: study design and characteristics of the cohort. European Prospective Investigation of Cancer. Br J Cancer. 1999;80 Suppl 1:95-103.

9. Vartiainen E, Jousilahti P, Alfthan G, Sundvall J, Pietinen P, Puska P. Cardiovascular risk factor changes in Finland, 1972-1997. International Journal of Epidemiology. 2000;29(1):49-56.

10. Cooney MT, Dudina A, D’Agostino R, Graham IM. Cardiovascular risk-estimation systems in pri-mary prevention: do they differ? Do they make a difference? Can we see the future? Circulation. 2010;122(3):300-310. doi:10.1161/CIRCULATIONAHA.109.852756.

11. CBO N, Genootschap NH. Multidisciplinaire Richtlijn Cardiovasculair Risicomanagement. 2011. 12. van Dis I, Kromhout D, Geleijnse JM, Boer JMA, Verschuren WMM. Evaluation of cardiovascular

risk predicted by different SCORE equations: the Netherlands as an example. European journal of cardiovascular prevention and rehabilitation : official journal of the European Society of Cardiology, Working Groups on Epidemiology & Prevention and Cardiac Rehabilitation and Exercise Physiology. 2010;17(2):244-249. doi:10.1097/HJR.0b013e328337cca2.

13. Allen JK, Blumenthal RS, Margolis S, Young DR, Miller ER, Kelly K. Nurse case management of hyper-cholesterolemia in patients with coronary heart disease: results of a randomized clinical trial. American Heart Journal. 2002;144(4):678-686.

14. Smith SC, Allen J, Blair SN, et al. AHA/ACC guidelines for secondary prevention for patients with coro-nary and other atherosclerotic vascular disease: 2006 update: endorsed by the National Heart, Lung, and Blood Institute. Circulation. 2006;113(19):2363-2372. doi:10.1161/CIRCULATIONAHA.106.174516.

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15. Graham I, Atar D, Borch-Johnsen K, et al. European guidelines on cardiovascular disease prevention in clinical practice: full text. Fourth Joint Task Force of the European Society of Cardiology and other societies on cardiovascular disease prevention in clinical practice (constituted by representatives of nine societies and by invited experts). European journal of cardiovascular prevention and rehabilitation : official journal of the European Society of Cardiology, Working Groups on Epidemiology & Preven-tion and Cardiac RehabilitaPreven-tion and Exercise Physiology. 2007;14 Suppl 2:S1-S113. doi:10.1097/01. hjr.0000277983.23934.c9.

16. Smulders YM, Burgers JS, Scheltens T, et al. Clinical practice guideline for cardiovascular risk manage-ment in the Netherlands. The Netherlands journal of medicine. 2008;66(4):169-174.

17. Burgers JS, Simoons ML, Hoes AW, Stehouwer CDA, Stalman WAB. [Guideline ‘Cardiovascular Risk Management’]. Nederlands tijdschrift voor geneeskunde. 2007;151(19):1068-1074.

18. Iestra JA. Effect Size Estimates of Lifestyle and Dietary Changes on All-Cause Mortality in Coronary Artery Disease Patients: A Systematic Review. Circulation. 2005;112(6):924-934. doi:10.1161/CIRCU-LATIONAHA.104.503995.

19. Weisman SM, Graham DY. Evaluation of the benefits and risks of low-dose aspirin in the secondary prevention of cardiovascular and cerebrovascular events. Arch Intern Med. 2002;162(19):2197-2202. 20. LaRosa JC, He J, Vupputuri S. Effect of statins on risk of coronary disease: a meta-analysis of

random-ized controlled trials. JAMA. 1999;282(24):2340-2346.

21. Freemantle N, Cleland J, Young P, Mason J, Harrison J. beta Blockade after myocardial infarction: sys-tematic review and meta regression analysis. BMJ. 1999;318(7200):1730-1737.

22. Rodrigues EJ, Eisenberg MJ, Pilote L. Effects of early and late administration of angiotensin-converting enzyme inhibitors on mortality after myocardial infarction. Am J Med. 2003;115(6):473-479.

23. Yusuf S. Two decades of progress in preventing vascular disease. Lancet. 2002;360(9326):2-3. doi:10.1016/S0140-6736(02)09358-3.

24. Group EIS. Lifestyle and risk factor management and use of drug therapies in coronary patients from 15 countries; principal results from EUROASPIRE II Euro Heart Survey Programme. European Heart Journal. 2001;22(7):554-572. doi:10.1053/euhj.2001.2610.

25. Kotseva K, Wood D, De Backer G, et al. EUROASPIRE III: a survey on the lifestyle, risk factors and use of cardioprotective drug therapies in coronary patients from 22 European countries. European journal of cardiovascular prevention and rehabilitation : official journal of the European Society of Cardiology, Working Groups on Epidemiology & Prevention and Cardiac Rehabilitation and Exercise Physiology. 2009;16(2):121-137. doi:10.1097/HJR.0b013e3283294b1d.

26. Kotseva K, Wood D, De Bacquer D, et al. EUROASPIRE IV: A European Society of Cardiology survey on the lifestyle, risk factor and therapeutic management of coronary patients from 24 Eu-ropean countries. EuEu-ropean Journal of Preventive Cardiology. February 2015:2047487315569401. doi:10.1177/2047487315569401.

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RISK

ASSESSMENT

IN PRIMARY

PREVENTION

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THE SYSTEMATIC

CORONARY RISK EVALUATION

(SCORE) IN A LARGE

UK POPULATION

10-YEAR FOLLOW-UP IN THE EPIC-NORFOLK

PROSPECTIVE POPULATION STUDY

Jørstad HT, Colkesen EB, Minneboo M, Peters RJ, Boekholdt SM, Tijssen JG, Wareham NJ, Khaw KT

European Journal of Preventive Cardiology, 2013

CHAPTER 2

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ABSTRACT

BACKGROUND: The European Society of Cardiology endorses cardiovascular disease (CVD) risk stratification using the Systematic COronary Risk Evaluation (SCORE) algorithm, with separate algorithms for high-risk and low-risk countries. In the 2012 European Guidelines on CVD Prevention in Clinical Practice, the UK has been reclassified as a low-risk country. However, the performance of the SCORE algorithm has not been validated in the UK.

DESIGN: We compared CVD mortality as predicted by SCORE with the observed CVD mortality in the European Prospective Investigation of Cancer-Norfolk (EPIC-Norfolk) prospective population study, a cohort representative of the general population.

METHODS: Individuals without known CVD or diabetes mellitus, aged 39–65 years at baseline, were included in our analysis. CVD mortality was defined as death due to ischaemic heart disease, cardiac failure, cerebrovascular disease, pe-ripheral artery disease and aortic aneurysm. Predicted CVD mortality was calculat-ed at baseline using the SCORE high-risk and low-risk algorithms.

RESULTS: A total of 15,171 individuals (57.1% female) with a mean age of 53.9 (SD 6.2) years were included. Predicted CVD mortality was 2.85% (95% confi-dence interval (CI) 2.80–2.90) with the SCORE high-risk algorithm and 1.55% (95% CI 1.52–1.58) with the low-risk algorithm. The observed 10-year CVD mortality was 1.25% (95% CI 1.08–1.44). Similar results were observed across sex and age subgroups.

CONCLUSION: In the large EPIC-Norfolk cohort representative of the UK popu-lation, the SCORE low-risk algorithm performed better than the high-risk algorithm in predicting 10-year CVD mortality. Our findings indicate that the UK has been correctly reclassified as a low-risk country.

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INTRODUCTION

The European Society of Cardiology (ESC) guidelines on cardiovascular disease (CVD) preven-tion in clinical practice recommend that treatment decisions be based on the predicted 10-year risk

of CVD mortality.1_{This risk can be calculated using the Systematic COronary Risk Evaluation}

(SCORE) algorithm, which is based on the pooling of several large European population- based

cohorts.2_{The SCORE algorithm includes age, sex, smoking status, systolic blood pressure, and}

serum total cholesterol or total/HDL-cholesterol ratio, and can be rapidly calculated using SCORE risk charts. Risk charts have been published for high-risk countries and low-risk countries, in

addi-tion to country-specific calibrated versions.2,3_{Based on data from the World Health Organization,}4

the most recent ESC guidelines have reclassified the United Kingdom (UK) as a low-risk country,

with no country-specific calibrated version.1_{However, the performance of the SCORE has not been}

studied in a large, population-based UK cohort.

We compared the predicted 10-year CVD mortality as calculated using the SCORE high-risk and low-risk algorithms with the observed 10-year CVD mortality in the European Prospective

Investi-gation of Cancer- Norfolk (EPIC-Norfolk) prospective population study.5

METHODS

Study population

We used data from the EPIC-Norfolk prospective population study, a cohort of men and women aged 39–79 years residing in the county of Norfolk in the UK. Details of the study have been

described elsewhere.5_{In brief, between 1993 and 1997, 77,630 adults were invited from general}

practices to participate in the study. Of these, 25,639 (33%) provided signed informed consent for study participation and attended a baseline health assessment. Participants completed question-naires about their personal and family history of disease, drug use and lifestyle, including smoking status. Participants were also asked whether a doctor had ever told them that they had any of the following conditions: diabetes mellitus, myocardial infarction, stroke. Anthropometric and blood pressure measurements and non-fasting blood samples were collected at the health assessment. Two measures were taken of diastolic and systolic blood pressure using an Accutorr Sphygomanometer (Datascope, UK) after the participant had sat for 3 min. Measurements were obtained on an arm held horizontally at the level of the mid- sternum. A medium or large cuff size was used accord-ing to arm circumference. Calibration was undertaken regularly to check the accuracy of both the equipment and the operators. Total cholesterol, high-density lipoprotein (HDL) cholesterol and triglyceride were measured on an RA 1000 (Bayer Diagnostics, Basingstoke, UK). Low-density

lipoprotein (LDL) cholesterol was calculated using the Friedewald formula6_{except when}

triglycer-ide was >4 mmol/l.

In comparison with the general population of the UK, anthropometric variables, blood pressure and serum lipids in the EPIC-Norfolk cohort are representative of the population studies recorded

in the Health Survey of England.5,7_{There were, however, fewer current smokers.}5_Participants

were followed up for the development of cause-specific mortality. Vital status for all EPIC-Norfolk participants was obtained through death certification at the Office for National Statistics. Death

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certificates were coded by trained nosologists using the International Classification of Diseases revision 10 (ICD-10). Previous validation studies in this cohort indicated high specificity of such

case ascertainment.8

Study design

In keeping with the selection criteria of the SCORE algorithm, we excluded individuals aged over 65 years and those with a history of CVD (myocardial infarction or stroke) or diabetes mellitus at baseline. Additionally, subjects with missing data on SCORE variables were excluded. CVD mor-tality was defined as death where CVD was coded as the underlying or contributing cause. CVD was defined as ischaemic heart disease (ICD-10 codes I20–I25), cardiac failure (ICD-10 codes I11, I13 and I50), cerebrovascular disease (ICD- 10 codes I60–I69), peripheral artery disease (ICD-10 codes I70–I79) and aortic aneurysm (ICD-10 code I71). The SCORE algorithm for high-risk and low-risk countries was applied to the EPIC-Norfolk data.

Statistical methods

Baseline characteristics were summarized separately for men and women, using numbers and percentages for categorical variables, mean and standard deviation (SD) for continuous variables with a normal distribution, and median and interquartile range for continuous variables with a non-normal distribution. Predicted 10-year CVD mortality rates were calculated using the SCORE

algorithm for high-risk countries and low-risk countries.2,3_{As SCORE was designed to predict}

10-year CVD mortality, the observed mortality rates in our cohort were limited to the first 10 years of follow-up using Kaplan–Meier (KM) estimates. Ratios of predicted to observed CVD mortality, as well as absolute differences between predicted and observed CVD mortality, were calculated for the total population and stratified by sex and age subgroups in accordance with the SCORE

charts.2_{We separately evaluated the coronary heart disease (CHD) mortality risk function and the}

non-coronary heart disease (NCHD) risk function. Receiver operator characteristic (ROC) curves with corresponding areas under the curve (AUCs) were calculated using the high-risk and low- risk algorithms and compared using C-statistics. In order to assess the calibration of both SCORE algo-rithms, we used the Hosmer–Lemeshow goodness-of-fit test, which aligns the number of predicted and observed CVD deaths by deciles of predicted risk. To correct for possible confounding due to the non-fasting state of the cholesterol measurements, we performed a sensitivity analysis using adjusted levels of total cholesterol (correction to baseline total cholesterol +0.2 mmol/l if fasting time was 0–2 h, +0.1 mmol/l if fasting time was 2–5 h and no change if fasting time was 5 h), as

described by Langsted et al.9_{ROC curves were calculated to compare both algorithms using}

ob-served total cholesterol values against corrected total cholesterol values.

RESULTS

The study population consisted of 25,639 participants. A total of 10,468 were excluded because they were older than 65 years (n=8053), had a history of CVD or diabetes mellitus (n=697) or miss-ing data (n=1718) (Figure 1). After exclusion, 15,171 study participants <65 years without a history of CVD or diabetes and with a complete dataset were available for analysis. Table 1 presents the characteristics of the study participants. Mean age was 53.9 years (SD 6.2), 57.1% were female and

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13.1% were current smokers. Mean body mass index was 26.0 kg/m2 (SD 3.9), mean total choles-terol 6.0 mmol/l (SD 1.1) and mean LDL cholescholes-terol 3.9mmol/l (SD 1.0), which was slightly above levels recommended by the ESC guidelines.

Table 1. Population characteristics

Total Male Female Missing

(n = 15,171) (n = 6509) (n = 8662) n (%)

Age, years 53.9 ± 6.2 54.1 ± 6.2 53.8 ± 6.2 0 (0)

Weight, kg 73.3 ±13.2 80.6 ± 11.3 67.9 ± 11.8 17 (0.1)

Body mass index, kg/m2 _{26.0 ± 3.9} _{26.2 ± 3.2} _{25.9 ± 4.3} _{28 (0.2)}

Waist/hip ratio 0.84 ± 0.09 0.91 ± 0.06 0.78 ± 0.06 30 (0.2)

Current smokers 13.1 (1988) 13.4 (871) 12.9 (1117) 106 (0.6)

Systolic blood pressure, mmHg 131 ± 17 134 ± 16 129 ± 17.1 29 (0.2)

Diastolic blood pressure, mmHg 81 ± 11 84 ± 11 79 ± 10.6 29 (0.2)

Total cholesterol, mmol/L 6.0 ± 1.1 6.0 ± 1.1 6.1 ± 1.1 1125 (6.7)

LDL cholesterol, mmol/L 3.9 ± 1.0 3.9 ± 1.0 3.8 ± 1.0 1605 (9.5)

HDL cholesterol, mmol/L 1.4 ± 0.4 1.2 ± 0.3 1.6 ± 0.4 1605 (9.5)

Triglycerides, mmol/L 1.4 (1.0-2.1) 1.7 (1.2-2.4) 1.4 (0.9-1.8) 1126 (6.7)

Data are presented as mean ± standard deviation, percentage (number) or median (interquartile range). Percentages of missing data are presented as percentage before exclusion of individuals with missing values (n=1718) over the whole population (n=16,889). LDL: low-density lipoprotein; HDL: high-density lipoprotein

Table 2 presents the predicted CVD mortality according to the high-risk and low-risk SCORE algo-rithms and the observed 10-year CVD mortality. Predicted CVD mortality according to the SCORE high-risk algorithm was 2.85% (95% confidence interval (CI) 2.80–2.90), whereas according to the SCORE low-risk algorithm it was 1.55% (95% CI 1.52–1.58). The observed 10-year CVD mortality (KM estimate) was 1.25% (95% CI 1.08–1.44). Goodness-of-fit for the SCORE high-risk

algorithm was x2_{=152.95 (p<0.001), while for the SCORE low-risk algorithm it was x}2_=21.60

(p=0.02).

Table 2. Predicted and observed 10-year cardiovascular mortality

SCORE High risk SCORE Low risk Observed

% (95%-CI) % (95%-CI) n % (95%-CI)

Total cardiovascular mortality (n=15,171) 2.85 (2.80-2.90) 1.55 (1.52-1.58) 187 1.25 (1.08-1.44)

Male (n=6509) 4.53 (4.43-4.63) 2.35 (2.29-2.40) 126 1.97 (1.66-2.34)

Female (n=8662) 1.58 (1.55-1.62) 0.95 (0.92-0.97) 61 0.71 (0.55-0.92)

Coronary heart disease mortality 2.03 (1.99-2.07) 1.0 (0.98-1.02) 93 0.62 (0.51-0.76) Non-coronary heart disease mortality 0.82 (0.80-0.83) 0.55 (0.52-0.77) 94 0.63 (0.52-0.77)

SCORE: Systematic COronary Risk Evaluation; CI: confidence interval

The observed 10-year CHD mortality was 0.62% (95% CI 0.51–0.76). Predicted CHD mortality ac-cording to the SCORE high-risk algorithm was 2.03% (95% CI 1.99–2.07), with an AUC of 0.84 and

goodness-of-fit of x2_{=59.50 (p < 0.0001). According to the SCORE low-risk algorithm CHD mortality}

was 1.0% (95% CI 0.98–1.02), with an AUC of 0.84 and a goodness-of-fit of x2_{=30.82 (p=0.0006).}

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Figure 1.Study Population

The observed 10-year NCHD mortality was 0.63% (95% CI 0.52–0.77). Predicted NCHD mortality according to the SCORE high-risk algorithm was 0.82% (95% CI 0.80–0.83), with an AUC of 0.73

and a goodness-of-fit of x2_{=13.28 (p=0.21). According to the SCORE low-risk algorithm NCHD}

mortality was 0.55% (95% CI 0.54–0.56), with an AUC of 0.73 and a goodness-of-fit of x2_=10.33

(p=0.41). There was no significant difference between the AUCs of both algorithms (x2_<0.01)

(p=0.95).

Overall, CHD mortality was markedly more over-estimated than NCHD mortality. SCORE high-risk overestimated CHD by 330% compared with 160% by SCORE low-high-risk. NCHD mortality was overestimated by SCORE high-risk by 30%. Conversely, SCORE low-risk showed an underestima-tion of 13%.

Among men, the predicted CVD mortality according to the SCORE high-risk algorithm and the SCORE low-risk algorithm was 4.53% (95% CI 4.43–4.63) and 2.35% (95% CI 2.29–2.40), respec-tively. The observed 10-year CVD mortality (KM estimate) in men was 1.97% (95% CI 1.66–2.34). Among women, the predicted CVD mortality according to SCORE high-risk algorithm was 1.58% (95% CI 1.55–1.62) and 0.95% (95% CI 0.92– 0.97) according to the SCORE low-risk algorithm. The observed 10-year CVD mortality in women was 0.71% (95% CI 0.55–0.92).

Across all age–sex groups as defined by SCORE cut-offs, the high-risk algorithm consistently overestimated CVD mortality to a larger extent than the low-risk algorithm (Figure 2 and Appendix 1 in the supplementary material). When using the SCORE high-risk algorithm, this overestimation varied between 107% and 169% in men, and 99% and 218% in women. Using the SCORE low-risk algorithm, over-estimation was considerably lower, varying between 10% and 39% in men and

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21% and 82% in women (Figure 3). When applying the ESC treatment threshold of SCORE 10-year predicted CVD mortality 5%, the SCORE high-risk algorithm classified an additional 12.4% of the population (1879 individuals) as 5% compared with the SCORE low-risk algorithm (2562 (16.9%) individuals by SCORE high-risk algorithm vs. 683 (4.5%) individuals by SCORE low-risk algorithm). Discriminative performance of the high-risk and the low-risk SCORE algorithm in predicting

Figure 2. Observed Cardiovascular Mortality as compared to SCORE High-risk and Low-risk Predicted Cardiovascular Mortality

according to Age and Sex . Error bars represent the standard deviation.

Figure 3. Ratios of SCORE High-risk and SCORE Low-risk / Observed Cardiovascular Mortality by Age and Sex. Optimal ratio=1 of

predicted/observed cardiovascular mortality.

21% and 82% in women (Figure 3). When applying the ESC treatment threshold of SCORE 10-year predicted CVD mortality ≥5%, the SCORE high-risk algorithm classified an additional 12.4% of the population (1879 individuals) as ≥5% compared with the SCORE low-risk algorithm (2562 (16.9%) individuals by SCORE high-risk algorithm vs. 683 (4.5%) individuals by SCORE low-risk algorithm). Discriminative performance of the high-risk and the low-risk SCORE algorithm in predicting 10-year CVD mortality was virtually identical, with an AUC of 0.78 (95% CI 0.75–0.81) using the high-risk algorithm and 0.78 (95% CI 0.75–0.81) using the low-risk algorithm (Appendix 2). Comparing the ROCs for SCORE high-risk and low-risk yielded an x2=0.16 (p=0.68).

In the sensitivity analysis, the AUC for SCORE in predicting 10-year CVD mortality using cor-rected versus uncorcor-rected total cholesterol levels did not change the AUC using either the high-risk algorithm (AUC 0.78 vs. 0.78) or the low-risk algorithm (AUC 0.78 vs. 0.78) (Appendix 3).

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In individuals excluded from the main analysis due to missing data, mean age was slightly higher (55.7 years (SD 6.2)) and 53% were female. Risk factor control was slightly worse as compared

with the study population, with 15.4% current smokers, a mean BMI of 27.4kg/m2_{(SD 4.6), a mean}

systolic blood pressure of 135mmHg (SD 18) and a mean diastolic blood pressure of 84 mmHg (SD 11). LDL-cholesterol was identical (3.9 mmol/l (SD 1.0)) as compared with the study population. The observed 10-year CVD mortality was 1.90% (32 events) (95% CI 1.35–2.67) – slightly higher than in the study population.

DISCUSSION

In the EPIC-Norfolk prospective population study, a cohort representative of the general UK popula-tion, we observed that the SCORE low-risk algorithm more accurately estimates CVD mortality than the SCORE high-risk algorithm. This concurs with the recent ESC guidelines reclassifying the UK as a low-risk country instead of a high-risk country.

The SCORE cardiovascular risk algorithm was developed using 12 European cohort studies, in

which the inclusion dates ranged from 1967 to 1991.2_{In comparison, the EPIC-Norfolk cohort}

en-rolled participants between 1993 and 1997. Mortality rates for CHD have risen during the 20th cen-tury, reaching a peak in the 1970s and 1980s in the UK and Western Europe, but showing a decline

since then.10–13_{Factors affecting this reduction in cardiovascular mortality include both improved}

acute and chronic treatments of cardiovascular diseases, as well as improvements in primary and secondary prevention. For example, statin prescription was negligible before 1995, but has increased

significantly since the publication of landmark statin trials.11_{In the EPIC-Norfolk cohort, use of}

lipid-lowering therapy was negligible at baseline, but has likely increased substantially since then, although no exact data are currently available. These changes could contribute to the discrepancies between predicted risk based on the older SCORE cohorts and findings from our more recent cohort. Overall, the discriminatory ability was high for both SCORE algorithms. Calibration was suboptimal for both, albeit superior using the low-risk algorithm. Both algorithms performed better in predicting non-coronary heart disease mortality as compared with coronary heart disease mortality. The over-estimation of cardiovascular mortality was largely due to an overover-estimation of CHD mortality using both algorithms, potentially reflecting the changes in CHD mortality due to recent improvements in acute treatment as well as primary and secondary prevention during follow-up. Both algorithms performed better in predicting NCHD mortality, with only a slight overestimation using the high-risk algorithm and a slight underestimation using the low-risk algorithm.

Our findings are similar to those of van Dis et al., who showed that the SCORE high-risk algorithm over-estimates the risk of CVD mortality in a large Dutch population-based cohort. Parallel to the

UK, the 2012 ESC guidelines have reclassified the Netherlands as a low-risk country.4,14_Conversely,

de Bacquer and de Backer showed that the predicted risk of fatal CVD using a calibrated SCORE

for Belgium does correspond with observed CVD mortality.15_{However, the UK and the Netherlands}

were initially classified as high-risk countries, whereas Belgium was classified as a low-risk country. Our findings are consistent with those of Capewell and O’Flaherty, who have shown that several countries classified as high-risk in the 1980s and 1990s, including the UK and the Netherlands, now

have similar CVD mortality rates compared with countries previously classified as low risk.13

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than the original SCORE publication.2_{This may be related to the fact that the UK National Institute}

for Health and Clinical Excellence (NICE) guidelines recommend the Framingham risk equation in addition to the QRISK and ASSIGN algorithms. QRISK and ASSIGN include classic as well as non-traditional risk factors for cardiovascular disease, such as social deprivation and family history of premature coronary heart disease, and have been validated in a large UK primary care

popu-lation.8,16–18_{If SCORE were to become an accepted risk assessment tool in the UK, the low-risk}

algorithm is a suitable alternative.

Both SCORE algorithms are based on the same Weibull regression, but use different regression coef-ficients in high-risk and low-risk countries to calculate the 10-year risk of coronary and non-coronary cardiovascular disease for an individual’s age and age in 10 years’ time – reflected in the different

values for a and p in the algorithms.2_{The coefficients for the other relevant risk factors (smoking,}

total cholesterol, systolic blood pressure) are identical in both algorithms, explaining why the ROC curves for both models are nearly identical when applied to the same individuals. However, our analysis shows that the absolute predicted risk is markedly higher when using the high-risk algorithm as compared with the low-risk algorithm.

The reclassification of the UK as a low-risk country could potentially influence the frequency of initiation of cardiovascular prevention. Using the SCORE low-risk algorithm to estimate cardiovas-cular mortality risk instead of the high-risk algorithm (as recommended by the 2012 ESC guidelines) resulted in 12.4% fewer study participants reaching this threshold. On a national level, considering that the UK population aged 30–59 years consisted of 24.8 million people in 2010, using the SCORE low-risk instead of the high-risk algorithm may lead to the initiation of cardiovascular prevention in three million fewer individuals. This might decrease short-term health-care spending in primary prevention. However, this could potentially adversely influence long-term risk in individuals with low baseline cardiovascular risk. Periodic re-evaluation is therefore recommended, as per current guidelines.

When interpreting the results of our study, several aspects need to be taken into account. First, while the EPIC-Norfolk cohort recorded detailed baseline information about demographic, anthropomet-ric and lifestyle parameters as well as pharmacological therapy, there is only limited information available about changes in pharmacological therapy over time. The increase in the prescription of lipid-lowering drugs from 1995 onward could potentially be a factor contributing to the lower rate of

observed CVD mortality in the EPIC-Norfolk cohort.19_{Furthermore, cardiovascular prevention}

pro-grammes focusing on risk factors not included in SCORE, particularly on lifestyle, could have had a similar impact. Second, the EPIC-Norfolk cohort is similar to a nationally representative sample for

anthropometric variables, blood pressure and serum lipids.5,7_{However, the population in the Norfolk}

area is healthier than the general UK population, with a standardized mortality ratio of 0.94 (source: Office for National Statistics). Potentially, this contributes to the overestimation of mortality by both algorithms in our study population. Third, we observed a slightly higher CVD mortality in individ-uals excluded from the main analysis due to missing SCORE variables as compared with our study population. This difference could potentially be explained by the slightly higher age and the slightly inferior risk factor profiles in the excluded individuals. However, we cannot exclude that the missing variables could have influenced this modest difference in CVD mortality. Fourth, while the ICD-10 codes of the outcome events in our study were largely identical to the ICD-9 codes included in the original SCORE project, there are a few differences. Most importantly, conduction disorders (426), cardiac dysrhythmias (427) and ill- defined descriptions and complications of heart disease (429) were not specifically coded in our study population. Potentially, this could have contributed to a

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low-030 NEW APPROACHES TO THE IMPLEMENTATION OF

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er number of outcome events in our study. Fifth, cholesterol levels were measured in a non-uniform fasting state. However, the original cohorts on which SCORE was based likewise included measure-ments collected during varying fasting states, and the 2012 ESC guidelines on cardiovascular pre-vention do not specify that cholesterol levels should be obtained in a fasting state. Furthermore, after correcting for non-fasting cholesterol levels in our sensitivity analysis, the differences in calculated risks were negligible.

CONCLUSION

The SCORE high-risk algorithm considerably overestimated the risk of 10-year CVD mortality in the EPIC- Norfolk population. The SCORE low-risk algorithm provided more accurate risk prediction of 10-year CVD mortality. Our findings support the recent reclassification of the UK as a low-risk country.

APPENDICES

Appendix 1: Predicted and observed CVD mortality according to SCORE algorithm by sex and age

KM: Kaplan Meier

Appendix 2. ROC of SCORE high-risk vs low-risk algorithm on predicting 10-year cardivascular mortality

10-year cardiovascular mortality

SCORE High-risk SCORE Low-risk Observed

age n Calculated % (95%-CI) Calculated % (95%-CI) n KM, % (95%-CI) Male 39-49 1989 1.68 (1.63-1.72) 0.82 (0.80-0.85) 14 0.71 (0.42-1.19) (n=6509) 50-54 1586 3.19 (3.10-3.28) 1.61 (0.56-1.65) 22 1.41 (0.93-2.13) 55-59 1471 5.62 (5.44-5.79) 2.91 (2.81-3.00) 30 2.09 (1.46-2.97) 60-65 1463 8.77 (8.52-9.02) 4.66 (4.52-4.79) 60 4.23 (3.30-5.42) Female 39-49 2811 0.35 (0.34-0.35) 0.20 (0.19-0.20) 3 0.11 (0.03-0.33) (n=8662) 50-54 2126 0.91 (0.88-0.93) 0.53 (0.52-0.55) 8 0.38 (0.19-0.76) 55-59 1901 1.94 (1.89-2.00) 1.16 (1.13-1.19) 15 0.8 (0.48-1.32) 60-65 1824 3.91 (3.80-4.01) 2.37 (2.30-2.42) 35 1.96 (1.41-2.72)

False positive rate

1,0 0,8 0,6 0,4 0,2 0,0 Sensitivity 1,0 0,8 0,6 0,4 0,2 0,0 Reference line SCORE Low-risk SCORE High-risk Page 1

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031

THE SYSTEMATIC CORONARY RISK EVALUATION (SCORE) IN A LARGE UK POPULATION

Appendix 3. ROC of SCORE high-risk with and without correction for non-fasting total cholesterol

1,0 0,8 0,6 0,4 0,2 0,0 Sensitivity 1,0 0,8 0,6 0,4 0,2 0,0 Reference Line SCORE High-risk with maximal correction for non-fasting total cholesterol SCORE High-risk without correction for non-fasting total cholesterol Page 1 Appendix 4. ROC of SCORE low-risk with and without correction for non-fasting total cholesterol

1,0 0,8 0,6 0,4 0,2 0,0 Sensitivity 1,0 0,8 0,6 0,4 0,2 0,0 Reference Line SCORE Low-risk without correction for non-fasting total cholesterol SCORE Low-risk without correction for non-fasting total cholesterol

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Europe: The SCORE project. Eur Heart J 2003; 24: 987–1003.

3. European Society of Cardiology (ESC). HeartScore. http://www.heartscore.org/ (2007, accessed 5 June 2012)

4. World Health Organization. Global Health Observatory Data Repository. http://apps.who.int/ghodata/ (2011, accessed 5 June 2012)

5. Day N, Oakes S, Luben R, et al. EPIC-Norfolk: Study design and characteristics of the cohort. European Prospective Investigation of Cancer. Br J Cancer 1999; 80(Suppl. 1): 95–103.

6. Friedewald WT, Levy RI and Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem 1972; 18: 499–502. 7. Bennett N, Dodd T, Flatley F, et al. Health survey for England 1993. London: HMSO, 1995.

8. Boekholdt SM, Peters RJG, Day NE, et al. Macrophage migration inhibitory factor and the risk of myo-cardial infarction or death due to coronary artery disease in adults without prior myomyo-cardial infarction or stroke: The EPIC-Norfolk prospective population study. Am J Med 2004; 117: 390–397.

9. Langsted A, Freiberg JJ and Nordestgaard BG. Fasting and nonfasting lipid levels: Influence of normal food intake on lipids, lipoproteins, apolipoproteins, and cardiovascular risk prediction. Circulation 2008; 118: 2047–2056.

10. Levi F, Lucchini F, Negri E, et al. Trends in mortality from cardiovascular and cerebrovascular diseases in Europe and other areas of the world. Heart 2002; 88: 119–124.

11. Hardoon SL, Whincup PH, Petersen I, et al. Trends in longer-term survival following an acute myocar-dial infarction and prescribing of evidenced-based medica- tions in primary care in the UK from 1991: A longitu- dinal population-based study. J Epidemiol Community Health 2011; 65: 770–774.

12. Unal B, Critchley JA and Capewell S. Explaining the decline in coronary heart disease mortality in En-gland and Wales between 1981 and 2000. Circulation 2004; 109: 1101–1107.

13. Capewell S and O’Flaherty M. Rapid mortality falls after risk-factor changes in populations. Lancet 2011; 378: 752–753.

14. Van Dis I, Kromhout D, Geleijnse JM, et al. Evaluation of cardiovascular risk predicted by different SCORE equations: The Netherlands as an example. Eur J Cardiovasc Prev Rehabil 2010; 17: 244–249. 15. De Bacquer D and De Backer G. Predictive ability of the SCORE Belgium risk chart for cardiovascular

mortality. Int J Cardiol 2010; 143: 385–390.

16. Tunstall-Pedoe H. Cardiovascular risk and risk scores: ASSIGN, Framingham, QRISK and others: How to choose. Heart 2011; 97: 442–444.

17. Hippisley-Cox J, Coupland C, Vinogradova Y, et al. Derivation and validation of QRISK, a new cardio-vascular disease risk score for the United Kingdom: Prospective open cohort study. BMJ 2007; 335: 136.

18. Hippisley-Cox J, Coupland C, Vinogradova Y, et al. Predicting cardiovascular risk in England and Wales: Prospective derivation and validation of QRISK2. BMJ 2008; 336: 1475–1482.

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19. Scarborough P, Bhatnagar P, Wickramasinghe K, et al. Coronary Heart Disease Statistics, 2010 edition. Department of Public Health, University of Oxford: British Heart Foundation Health Promotion Research Group, 2010.

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ESTIMATED 10-YEAR

CARDIOVASCULAR

MORTALITY SERIOUSLY

UNDERESTIMATES OVERALL

CARDIOVASCULAR RISK

OBSERVATIONS FROM THE EPIC-NORFOLK

PROSPECTIVE POPULATION STUDY

Jørstad HT, Colkesen EB, Boekholdt SM, Tijssen JG, Wareham NJ, Khaw KT, Peters RJ

Heart, 2016

CHAPTER 3

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ABSTRACT

OBJECTIVE: The European Society of Cardiology’s prevention guideline suggests that the risk of total (fatal plus non-fatal) cardiovascular disease (CVD) may be cal-culated from the risk of CVD mortality using a fixed multiplier (3×). However, the proposed multiplier has not been validated. We investigated the ratio of total CVD to CVD mortality in a large population-based cohort.

METHODS: CVD mortality and total CVD (fatal plus non- fatal CVD requiring hospitalisation) were analysed using Kaplan-Meier estimates among 24 014 men and women aged 39–79 years without baseline CVD or diabetes mellitus in the prospective population-based European Prospective Investigation of Cancer and Nutrition-Norfolk cohort. CVD outcomes included death and hospitalisations for ischaemic heart disease, heart failure, cerebrovascular disease, peripheral artery disease or aortic aneurysm. The main study outcome was the ratio of 10-year total CVD to 10-year CVD mortality stratified by age and sex.

RESULTS: Ten year CVD mortality was 3.9% (900 CVD deaths, 95% CI 3.6% to 4.1%); the rate of total CVD outcomes was 21.2% (4978 fatal or non-fatal CVD outcomes, 95% CI 20.7% to 21.8%). The overall ratio of total CVD to CVD mortality was 5.4. However, we found major differences in this ratio when stratified by gender and age. In young women (39–50 years), the ratio of total CVD to CVD mortality was 28.5, in young men (39–50 years) 11.7. In the oldest age group, these ratios were considerably lower (3.2 in women and 2.4 in men aged 75–79 years).

CONCLUSIONS: The relationship between 10-year total CVD and CVD mortality is dependent on age and sex, and cannot be estimated using a fixed multiplier. Using CVD mortality to estimate total CVD risk leads to serious underestimation of risk, particularly in younger age groups, and particularly in women.

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INTRODUCTION

The most recent ESC guidelines on cardiovascular disease (CVD) prevention suggest that there is a fixed relationship between CVD mortality and the total burden of CVD events, defined as the

composite of fatal and non-fatal CVD.1 2_{It is suggested that in high-risk individuals with a 10-year}

CVD mortality risk of ≥5%, as estimated using Systematic COronary Risk Evaluation (SCORE), total CVD is threefold higher, and possibly more in young men, and less in women and in older

in-dividuals.1 3 _{This has led to the suggestion of using a fixed multiplier (3×) for calculating total CVD}

based on CVD mortality only. From a patient’s perspective, total CVD risk is the most relevant

parameter for initiating CVD prevention,4_{and using CVD mortality only can result in}

underestima-tion of the total CVD burden.5_{Although mortality is a more robust clinical outcome, cardiovascular}

morbidity is equally relevant to providers of healthcare, policy makers and insurance companies. Currently, the relationship between total CVD and CVD mortality in the general population is unclear, and the proposed multiplier for conversion from CVD mortality to total CVD has not been validated.

We hypothesised that the ratio of total CVD (fatal and non-fatal events) and CVD mortality is dependent on age and sex. We tested this hypothesis in the European Prospective Investigation of Cancer and Nutrition-Norfolk (EPIC-Norfolk), a large prospective population-based cohort, with detailed information on various chronic diseases, including CVD mortality and morbidity.

METHODS

Source population

We used data from the EPIC-Norfolk prospective population study, a cohort of 25 639 men and women aged 39–79 years residing in the county of Norfolk, UK. Details of the study have been

described elsewhere.6_{In brief, between 1993 and 1997, 77 630 adults were invited from general}

practices to participate in the study. Of these, 25 639 (33%) provided signed informed consent for study participation and attended a baseline health assessment. Participants completed ques-tionnaires about their personal and family history of disease, drug use and lifestyle, including smoking. Participants were also asked whether a doctor had ever told them that they had any of the following conditions: diabetes mellitus, myocardial infarction or stroke. Anthropometric and blood pressure measurements were performed and non-fasting blood samples were collected at the health assessment. The EPIC-Norfolk cohort was similar to a nationally representative sample for anthropometric indices, blood pressure measurements and serum lipid levels, but with a lower

proportion of smokers.6_{The participants’ National Health Service number was used to determine}

their hospital stay through the East Norfolk Health Authority database, which records all hospital contacts throughout England and Wales for Norfolk residents. Vital status for all EPIC-Norfolk participants was obtained through death certification at the Office for National Statistics. The underlying cause of death or hospital admission was coded by trained nosologists according to the International Classification of Diseases (ICD), Tenth Revision. The EPIC-Norfolk study complies

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Study design

For this analysis, the study population consisted of all EPIC-Norfolk participants who did not report a history of diabetes mellitus, myocardial infarction or stroke at the baseline health assess-ment. We excluded individuals with diabetes mellitus, as diabetes mellitus is not included as a variable in the SCORE algorithm. CVD mortality was defined as death where CVD was reported as the underlying cause of death on the death certificate. Total CVD was defined as CVD mortality plus hospitalisation with CVD as the underlying cause. Previous validation studies in this cohort

indicated high specificity of such case ascertainment.7_{We defined cardiovascular events or disease}

as the combination of ischaemic heart disease (ICD codes I20–I25), cardiac failure (ICD codes I11, I13, I50), cerebrovascular disease (ICD I60–I69), peripheral artery disease (ICD I70–I79) and aortic aneurysm (ICD I71). We defined 30-day CVD mortality as CVD mortality within 30 days of hospitalisation for a first non-fatal CVD event. CVDs or events not requiring hospitalisation, such as stable angina pectoris, heart failure without hospitalisation or intermittent claudication, were not included in our analysis. We report results for follow-up up to 31 March 2008, a mean follow-up of 11 years.

Statistical methods

Baseline characteristics were summarised separately for men and women, using numbers and per-centages for categorical data, means, 95% CI and SD for continuous data with a normal distribu-tion, and median and IQR for continuous variables with a non-normal distribution. Ten-year rates of CVD mortality and total CVD were estimated using the Kaplan-Meier (KM) method. Ratios and differences between cardiovascular mortality and morbidity rates were calculated for the total pop-ulation and in age groups (39–50 years, 50–55 years, 55–60 years, 60–65 years, 65–70 years, 70–75 years and 75–79 years), for men and women separately and according to SCORE (<5%, ≥5%). We evaluated the calculated total CVD/CVD mortality ratios, including 95% CIs, by performing individual resampling bootstrapping with 1000 iterations with the same sample size as the original sample. SCORE was calculated using the algorithm for low-risk countries in individuals younger than 65 years, using age at baseline, sex, smoking status, total cholesterol and systolic blood pres-sure. SCORE was only calculated in individuals with a complete data set of the abovementioned variables. Statistical analyses were performed in SPSS V.21 and STATA V.12.

RESULTS

A total of 25 639 individuals attended the baseline visit. Of these participants, 1625 had diabetes mellitus or a history of vascular disease. The study population consisted of 24 014 men and women without prevalent CVD or diabetes mellitus. Table 1 shows baseline characteristics of the study par-ticipants. In total, 56.2% of the study participants were women. Mean age was 58.8 (SD 9.3) years, and 11.8% were current smokers. Mean values for body mass index, total cholesterol and low-den-sity lipoprotein (LDL) cholesterol were slightly above levels recommended in primary prevention setting, respectively, at 26.3 kg/m2 (SD 3.9) and 6.2 mmol/L (SD 1.2) and 4.0 mmol/L (SD 1.1). There were no clinically relevant differences in CVD risk factors between men and women.

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Table 1. Population characteristics

Population characteristics (n=24,014) Total Male Female

(n=24,014) (n=10,509) (n=13,505)

Age, years 58.8 ± 9.3 59.0 ± 9.3 58.7 ± 9.3

Weight, kg 73.3 ± 13.1 80.3 ± 11.4 67.9 ± 11.8

Body mass index, kg/m2 _{26.3 ± 3.9} _{26.4 ± 3.3} _{26.2 ± 4.3}

Waist/hip ratio 0.85 ± 0.09 0.93 ± 0.06 0.79 ± 0.06

Current smokers 2836 (11.8) 1297 (12.3) 1539 (11.4)

Systolic blood pressure, mmHg 135.2 ± 18.3 137.1 ± 17.5 133.7 ± 18.8

Diastolic blood pressure, mmHg 82.4 ± 11.2 84.4 ± 11.1 80.9 ± 11.1

Total cholesterol, mmol/L 6.2 ± 1.2 6.0 ± 1.1 6.3 ± 1.1

LDL cholesterol, mmol/L 4.0 ± 1.0 3.9 ± 1.0 4.0 ± 1.1

HDL cholesterol, mmol/L 1.4 ± 0.4 1.2 ± 0.3 1.6 ± 0.4

Triglycerides, mmol/L 1.5 (1.1 - 2.2) 1.7 (1.2 -2.5) 1.4 (1.0 – 2.0)

SCORE, % (n= 15,171) 1.55 ± 1.8 2.35 ± 2.2 0.95 ± 1.1

Data are presented as number (percentage), mean ± standard deviation, or median (interquartile range).

LDL = Low-density lipoprotein HDL = High-density lipoprotein SCORE = Systematic Coronary Risk Evaluation, expressed as estimat-ed 10-year mortality risk.

Figure 1 shows the 10-year KM curves for cardiovascular mortality and morbidity. A total of 4978 study participants died of or were hospitalised for CVD, yielding a 10-year cumulative event rate for total CVD of 21.2% (95% CI 20.7% to 21.8%). A total of 900 study participants died of a CVD or event, yielding a 10-year CVD mortality rate of 3.9% (95% CI 3.6% to 4.1%). The overall ratio of total CVD/CVD mortality was 5.4. Of the 4978 study participants with a CVD or event, 360 individuals had a fatal event as first event (7.2% of total CVD); when 30-day CVD mortality was included this number was 643 (12.9% of total CVD). Of the 4618 non-fatal CVD events/hospital-isations, the majority was ischaemic heart disease (45.6%) followed by peripheral arterial disease (19.7%) and congestive heart failure (16.9%). Only 2.9% of the non-fatal events/hospitalisations were caused by an aortic aneurysm (table 2).

Table 2. Non-fatal 10-year CVD according to type

Type of event Total Male Female

n (%) n (%) n (%)

Ischemic heart disease 2105 (45.6) 1260 (46.9) 845 (43.8)

Congestive heart failure 781 (16.9) 444 (16.5) 337 (17.5)

Cerebrovascular disease 686 (14.9) 332 (12.4) 354 (18.4)

Hemorrhagic 118 (2.6) 55 (2.1) 63 (3.3)

Ischemic 568 (12.3) 277 (10.3) 291 (15.1)

Peripheral arterial disease 912 (19.7) 547 (20.4) 365 (18.9)

Aortic aneurysm 134 (2.9) 104 (3.9) 30 (1.6)

CVD = cardiovascular disease

Non-fatal 10-year CVD includes CVD diseases or events requiring hospitalization. Fatal CVD is not included in the table. Data are presented as number (percentage). Percentages may not add up to 100 because of rounding.