Heart Disease HealthRisk Assessment

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Medically Reviewed By: Expert-24 Medical Review Board on November 28, 2012 | References | Terms of Use & Privacy

HEALTHTOOLS™ (HEALTHRISK™ AND HEALTHAGE™) DOES NOT PROVIDE MEDICAL ADVICE. It is intended for informational purposes only. It is not a substitute for professional medical advice, diagnosis or treatment. Never ignore professional medical advice in seeking treatment because of something you have read on the site. If you think you may have a medical emergency, immediately call your doctor or dial 911.

Expert Review Panel – Expert-24 Ltd

Terms of reference

The aim of the Expert Review Panel is to ensure that all Expert-24 clinical and epidemiological content is robust, independent and up to date.

Qualifications

Medical Director and Editor

Dr. Timothy Dudley

Chairman of the Expert Review Panel

Dr. Robin Christie

Current authors and reviewers for the Health Risk Assessment

Dr. Martin Dawes

Dr. Jonathan Mant

Emeritus authors and reviewers for the Health Risk Assessment

The following individuals were deeply involved in the creation of the health risk assessment at its inception, but are no longer active reviewers on the panel:

Dr. John Fletcher

Dr. Emma Boulton

Professor Larry Ramsay

Professor Klim McPherson



Patient-centered health risk using an Evidence Based Medicine approach

Who created it and how often is it reviewed and updated?

This health risk assessment is brought to you by Expert-24 Limited. Expert-24 Ltd has full editorial control over content and strives to ensure that the content is: 

  • Robust - All information used is derived from reputable, referenced sources and subject to rigorous expert review. The content is written by the medical staff of Expert-24 and reviewed by an independent Expert Review Panel. All content is subject to regular review and updated to incorporate the latest evidence. Oxford Health Consulting was commissioned to conduct independent research to determine the model for disease and mortality-specific risks, the contents and its assumptions. The research and statistical modeling behind the risk assessment has been led by Dr. John Fletcher. Dr. Fletcher is deputy editor of the Canadian Medical Association Journal. He holds a Masters degree in Public Health Quantitative Methods and is a member of the Royal College of General Practitioners. 
  • Independent - The content on the site is provided by Expert-24 Limited, an independent UK company providing knowledge automation and decision support tools to improve health and wellbeing. No member of the Expert Review Panel has any financial stake in Expert-24 Ltd. Content creation and ongoing Quality Assurance is provided by Expert-24 Ltd and its Expert Review Panel. 
  • Up to date - All clinical material is subject to review by Expert-24 and its Expert Review Panel at least annually.

Why is this health risk assessment different than others?

Most health risk assessments say if a person is at high, medium or low risk of either dying from or developing a given medical condition. Most also indicate what lifestyle factors contribute to this risk. What they do not say is the magnitude of each risk for an individual and how much that person’s risk will decrease if they change their lifestyle. For example, if one is at moderate risk of two diseases, say bowel cancer and heart disease, most people would be unaware that their risk of heart disease is still five times higher than their risk of bowel cancer. 

In order to construct an electronic risk assessment tool for health and disease states, it is necessary to provide supporting research evidence and a method of encapsulating the best estimate of relative risk. For each medical condition, it is necessary to present credible estimates of risk, based on evidence from relevant, peer reviewed medical research. Important features of the risk assessment tool are: 
  • The tool gives numerical estimates of risk, rather than an imprecise statement such as "increased risk" or "reduced risk". 
  • The tool has the capability for interaction, allowing users to explore the impact on their personal risk of changing individual risk factors. 
  • The tool utilizes best available medical evidence 

The aim of this project is to provide healthy people with a quantitative assessment of their personal risk of developing some important diseases and some of the factors that influence their risk. This is an ambitious task and we would not claim to have produced the definitive approach. Although we believe this is the most informative collection of disease prediction equations available at the present time they do have limitations. The ones we are aware of are outlined below.

What exactly does a given percentage risk mean?

Someone looking at their risk of lung cancer until the age of 50 should read this model as saying, "Assuming survival to age 50 the chance of developing lung cancer during that time would be (some predicted value)". This approach has the appeal that changing risk factors will have the expected impact on cumulative risk and the mathematics remains transparent. We chose the risk of developing a certain condition rather than the risk of dying from it because for many people the fear of living and dealing with a disabling disease is as frightening as dying from it. 

This is different than lifetime risk calculations, which generally calculate the risk of dying from a given condition. Lifetime risk must take account of the fact that we all die of something in the end and calculating the relative contribution of common competing causes of death at various ages is difficult. Not only that, but the interpretation by users is complex. For example, a user of an interactive model predicting lifetime risk of lung cancer would see their individual risk of lung cancer fall with increasing cigarette consumption, because they would be dying of heart disease and chronic lung disease before they could get lung cancer.


How accurate are these percentages?

These models are good for illustrating the change in risk due to the presence or absence of single risk factors for prediction times of up to 5 years. They are likely to be reasonably good for 15 or 20 years and for combinations of several risk factors. For longer prediction times and varying more than, say, four risk factors the results should be regarded as illustrative rather than precise. The absolute level of risk for an individual may also be wide of the mark because the majority of overall risk remains unexplained in most research studies. This is why "confidence intervals" have not been included. That said these prediction equations do calculate the best estimate of risk that can be provided on the data given. 

Is this useful in the end? We believe it is. We believe that putting some quantification on risk allows users to explore the possible impact on their health of altering what they do. We find this approach more informative than a bland statement of "high risk" that is often value laden or that a certain action will "cut down" a risk without any indication of by how much.

Is risk really reversible?

This is a difficult question to answer, but in many cases the answer seems to be, "yes". This is good news for people with high risks who are older. Intuition might tell you that you are constantly doing damage to your body that accumulates over time, and in many cases that may be true. An example of this is in skin cancer, where the earlier and more often you are badly burned in life, the higher your risk of skin cancer. Staying out of the sun when you are old cannot reverse this risk. 
However, there is good evidence that for heart disease, for example, your risks can be significantly reduced no matter what your age. Cholesterol reduction by medications called "statins" reduces the risk of heart attack, angina or sudden death from heart problems by up to 30%, and this is entirely independent of age. Similarly, blood pressure reduction by drugs reduces the risk of stroke and heart disease by 25% - again entirely independent of age. Because in general it is older people who have the highest risks, they actually stand to benefit the most from treatment. 

The risk for developing heart disease in tobacco users has been shown to decline to a level comparable with a person who has never smoked within 2-3 years of giving up. Furthermore, the risk of having a stroke is reversed after 5-10 years of stopping. Studies have also shown that life expectancy improves even in people who stop smoking later in life (i.e. at 65 years or older). 

The reduction of risk that can be obtained from changing lifestyle habits such as diet, alcohol consumption and exercise is largely unknown. Therefore, the amount of risk reduction that can be expected from optimizing these habits needs to be viewed with caution. Certainly they should not take the place of blood pressure control, cholesterol control, and smoking cessation as goals.


How good is the evidence?

Our aim in searching for evidence was to identify up to ten high quality, relevant research studies for each topic. We used Medline to search using free text, MeSH terms and thesaurus search terms specific to each medical condition. To narrow the documents we used filters using "risk" and study design type; cohorts, case control, longitudinal, follow up. Searches were limited to studies published in English language and human studies. Although a comprehensive systematic review of the literature on each disease was not possible due to the scope of this project, we feel that the evidence used represents a reasonable cross-section of high-quality literature on the subjects in question. 
What we have done is to seek out plausible values of relative risk to use in the prediction equations. We have used an approach that searches for high quality research studies and have then applied our judgment tempered by Austin Bradford Hill's criteria for causation when selecting which risks to use. Hill's criteria are: strength, consistency, specificity, temporality, biological gradient, plausibility, coherence, experimental evidence and analogy. 

If this sometimes appears somewhat subjective then that is because at times it is a matter of judgment. The judgments have seldom altered the relative risk by more than a small amount. For each risk factor we had to choose a value to use in the model and have been faced at times with a range from which to choose. While a meta-analysis may provide the best point estimate, one is not always available and would be spurious to conduct on the sample of studies we have used for each condition. Given the level of uncertainty surrounding an individual's absolute personal risk we are comfortable with a comparatively lesser degree of uncertainty regarding a risk factor's relative risk.

What is the mathematical model that is used?

The actual mathematical and statistical models and risk coefficients that are used to determine risk are proprietary at this time, but have been validated by the authors and reviewers to be appropriate for use in this setting. 

References: Coronary Heart Disease

Most recently reviewed:

  1. Mitrou P et al. Mediterranean Dietary Pattern and Prediction of All Cause Mortality in a US Population: Results from the NIH AARP Diet and Health Study. Arch Int Med. 2007; 167(22): 2461-8. 
  2. Fung T et al. Adherence to a DASH-Style Diet and Risk of CHD and Stroke in Women. Arch. Int. Med. 2008; 168(7):713-720
  3. Sinha, R et al. Meat Intake and Mortality: A Prospective Study of Over Half a Million People. Arch. Int. Med. 2009; 169(6): 562-571.
  4. Pan A, et al. Red Meat Consumption and Mortality. Results from 2 prospective cohort studies. Arch. Int. Med. Published online March 12, 2012.
  5. Magnus P et al. Controlling for High-Density Lipoprotein Cholesterol Does Not Affect the Magnitude of the Relationship Between Alcohol and Coronary Heart Disease. Circulation 2011; 124: 2296-2302.
  6. Taylor RS, Ashton KE,Moxham T, Hooper L, Ebrahim S. Reduced dietary salt for the prevention of cardiovascular disease.Cochrane Database of Systematic Reviews 2011, Issue 7. Art. No.: CD009217.
  7. Kay-Tee Khaw, et al. Combined Impact of Health Behaviours and Mortality in Men and Women: The EPIC Norfolk Prospective Population Study. PloSMedicine Jan 2008; 5(1): 0039-0047
  8. Yi TY et al. Obesity as Compared with Physical Activity in Predicting Risk of Coronary Heart Disease in Women. Circulation Jan. 31, 2006; 113(4): 499-506
  9. Holterman A et al. Risk factors for ischaemic heart disease mortality among men with different occupational physical demands. A 30 year prospective cohort study. BMJ Open 2012; 2:e000279
  10. Wijndaele K et al. Television Viewing and Incident Cardiovascular Disease: Prospective Associations and Mediation Analysis in the Epic Norfolk Study. PLOS ONE May 2011; 6(5): e20558

Guidelines reviewed annually:

  1. Prevention of cardiovascular disease at population level. National Institute for Health and Clinical Excellence. June 2010 http://www.nice.org.uk/nicemedia/live/13024/49273/49273.pdf
  2. Aspirin for the prevention of cardiovascular disease: U.S. Preventive Services Task Force, revised 2009. http://www.uspreventiveservicestaskforce.org/uspstf/uspsasmi.htm
  3. Risk estimation and the prevention of cardiovascular disease. A national clinical guideline, revised 2007. Scottish Intercollegiate Guidelines Network
  4. http://www.sign.ac.uk/guidelines/fulltext/93-97/index.html

Articles from previous updates:

  1. Halbert SC et al. Tolerability of red yeast rice (2,400 mg twice daily) versus pravastatin (20 mg twice daily) in patients with previous statin intolerance. Am J Cardiol. 2010 Jan 15;105(2):198-204
  2. Lloyd-Jones DM et al. Defining and Setting National Goals for Cardiovascular Health Promotion and Disease Reduction. The American Heart Association’s Strategic Impact Goal Through 2020 and Beyond. Circulation Feb 2, 2010: 586-613
  3. Brugts JJ, Yetgin T, Hoeks SE, et al. The benefits of statins in people without established cardiovascular disease but with cardiovascular risk factors: meta-analysis of randomised controlled trials. BMJ 2009;338:b2376.
  4. Antithrombotic Trialists' (ATT) Collaboration, Baigent C, Blackwell L, Collins R, et al. Aspirin in the primary and secondary prevention of vascular disease: collaborative meta-analysis of individual participant data from randomised trials. Lancet 2009;373(9678):1849-1860.
  5. Almgren T, et al. "Stroke and coronary heart disease in treated hypertension - a prospective cohort study over three decades", J Int Med June 2005, 257(6): 496-502
  6. Viikari JS, et al. "Risk factors for coronary heart disease in children and young adults", Acta Paediatr Suppl Dec 2004, 93(446): 34-42
  7. Knekt P, et al. "Antioxidant vitamins and coronary heart disease risk: a pooled analysis of 9 cohorts", Am J Clin Nutr Dec 2004, 80(6): 1508-20
  8. Kaur S, et al. "The impact of environmental tobacco smoke on women's risk of dying from heart disease: a meta-analysis", J Women's Health Oct 2004, 13(8):888-97
  9. Ciardullo AV, et al. "Non-HDL cholesterol predicts coronary heart disease in primary prevention: findings from an Italian 40-69 year old cohort in general practice", Monaldi Arch Chest Dis June 2004, 62(2):69-72
  10. Pereira MA, et al. "Dietary fiber and risk of coronary heart disease: a pooled analysis of cohort studies", Arch Int Med Feb 2004 164(4):370-6
  11. Britton A, Mamot M "Different measures of alcohol consumption and risk of coronary heart diseas and all-cause mortality: 11 year follow-up of the Whitehall II Cohort Study", Addiction Jan 2004 99(1):109-16
  12. Evidence-based guidelines for cardiovascular disease prevention in women: 2007 update. American Heart Association - Professional Association. 2004 Feb (revised 2007 Mar 20).
  13. Summary of recommendations for clinical preventive services. American Academy of Family Physicians - Medical Specialty Society. 1996 Nov (revised 2007 Aug).
  14. Joint British Societies (2005) JBS 2: Joint British Societies' guidelines on prevention of cardiovascular disease in clinical practice. Heart 91(Suppl 5), v1-v52.
  15. Bobrie, G. et al. Cardiovascular prognosis of "masked hypertension" detected by blood pressure self-measurement in elderly treated hypertensive patients. JAMA. 2004 Mar 17;291(11):1342-9.
  16. Cooper A, O'Flynn N. Risk assessment and lipid modification for primary and secondary prevention of cardiovascular disease: summary of NICE guidance. BMJ 2008;336:1246-1248.
  17. The general public 2008 NICE guideline on cholesterol is available at: http://www.nice.org.uk/nicemedia/pdf/CG67publicinfo.pdf
  18. Becker DJ, Gordon RY, Halbert SC, et al. Red yeast rice for dyslipidemia in statin-intolerant patients. Annals of Internal Medicine. 2009;150(12):830-39.
  19. Rissanen, T.H., "Low intake of fruits, berries and vegetables is associated with excess mortality in men: the Kuopio Ischaemic Heart Disease Risk Factor (KIHD) Study", Journal of Nutrition, 01 Jan 2003, 133(1), 199-204
  20. Malyutina, S.et al, "Relation between heavy and binge drinking and all-cause and cardiovascular mortality in Novosibirsk, Russia: a prospective cohort study" Lancet 2002; 360: 1448-54
  21. Manson JE et al. Walking compared with vigorous exercise for the prevention of cardiovascular events in women. N Engl J Med 2002 Sep 5;347(10):716-25
  22. Yu S. et al, Caerphilly study.What level of physical activity protects against premature cardiovascular death? The Caerphilly study. Heart 2003 May;89(5):502-6
  23. Lakka, H.M., "Abdominal obesity is associated with increased risk of acute coronary events in men", European Heart Journal, 01 May 2002; 23(9): 706-13
  24. Abbasi, F., "Relationship between obesity, insulin resistance, and coronary heart disease risk.", J Am Coll Cardiol. 04 Sep. 2002; 40(5): 937-43
  25. Kannel, W.B., "Risk stratification of obesity as a coronary risk factor.", American Journal of Cardiology, 1 Oct. 2002; 90(7): 697-701
  26. Ashton, W.D., "Body mass index and metabolic risk factors for coronary heart disease in women." European Heart Journal, 01 Jan. 2001; 22(1): 46-55
  27. Tanasescu, M., "Exercise type and intensity in relation to coronary heart disease in men." JAMA 23 Oct 2002; 288(16): 1994-2000
  28. Schnohr, P., "Coronary heart disease risk factors ranked by importance for the individual and community. A 21 year follow-up of 12 000 men and women from The Copenhagen City Heart Study.", European Heart Journal, 01 Apr 2002; 23(8): 620-6
  29. Orford, J.L., "A comparison of the Framingham and European Society of Cardiology coronary heart disease risk prediction models in the normative aging study." American Heart Journal, 01 Jul. 2002; 144(1): 95-100
  30. Clarke, R., "Underestimation of the importance of blood pressure and cholesterol for coronary heart disease mortality in old age." European Heart Journal, 01 Feb. 2002; 23(4): 286-93

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