Skip to content
naulon
Get started

Corrected on Feb 1, 2023.

Source: the publisher, via Crossref · checked Oct 16, 2026 · Read the notice

World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions

Stephen Kaptoge, Lisa Pennells, Dirk De Bacquer and 97 more

The Lancet Global Health · 2019

doi:10.1016/s2214-109x(19)30318-3

Abstract

Background. To help adapt cardiovascular disease risk prediction approaches to low-income and middle-income countries, WHO has convened an effort to develop, evaluate, and illustrate revised risk models. Here, we report the derivation, validation, and illustration of the revised WHO cardiovascular disease risk prediction charts that have been adapted to the circumstances of 21 global regions.

Methods. In this model revision initiative, we derived 10-year risk prediction models for fatal and non-fatal cardiovascular disease (ie, myocardial infarction and stroke) using individual participant data from the Emerging Risk Factors Collaboration. Models included information on age, smoking status, systolic blood pressure, history of diabetes, and total cholesterol. For derivation, we included participants aged 40–80 years without a known baseline history of cardiovascular disease, who were followed up until the first myocardial infarction, fatal coronary heart disease, or stroke event. We recalibrated models using age-specific and sex-specific incidences and risk factor values available from 21 global regions. For external validation, we analysed individual participant data from studies distinct from those used in model derivation. We illustrated models by analysing data on a further 123 743 individuals from surveys in 79 countries collected with the WHO STEPwise Approach to Surveillance.

Findings. Our risk model derivation involved 376 177 individuals from 85 cohorts, and 19 333 incident cardiovascular events recorded during 10 years of follow-up. The derived risk prediction models discriminated well in external validation cohorts (19 cohorts, 1 096 061 individuals, 25 950 cardiovascular disease events), with Harrell's C indices ranging from 0·685 (95% CI 0·629–0·741) to 0·833 (0·783–0·882). For a given risk factor profile, we found substantial variation across global regions in the estimated 10-year predicted risk. For example, estimated cardiovascular disease risk for a 60-year-old male smoker without diabetes and with systolic blood pressure of 140 mm Hg and total cholesterol of 5 mmol/L ranged from 11% in Andean Latin America to 30% in central Asia. When applied to data from 79 countries (mostly low-income and middle-income countries), the proportion of individuals aged 40–64 years estimated to be at greater than 20% risk ranged from less than 1% in Uganda to more than 16% in Egypt.

Interpretation. We have derived, calibrated, and validated new WHO risk prediction models to estimate cardiovascular disease risk in 21 Global Burden of Disease regions. The widespread use of these models could enhance the accuracy, practicability, and sustainability of efforts to reduce the burden of cardiovascular disease worldwide.

Funding. World Health Organization, British Heart Foundation (BHF), BHF Cambridge Centre for Research Excellence, UK Medical Research Council, and National Institute for Health Research.

References 55

  1. 1.

    United Nations. Transforming our world: the 2030 agenda for sustainable development. 2015.

    Not in our library

  2. 2.
    Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the Global Burden of Disease Study 2017 · 2018
    No notice
  3. 3.

    WHO. Global action plan for the prevention and control of NCDs 2013–2020. 2013.

    Not in our library10.2471/blt.08.053348

  4. 4.

    WHO. HEARTS technical package. 2018.

    Not in our library

  5. 5.

    WHO. Package of essential noncommunicable disease interventions in primary health care.

    Not in our library

  6. 6.

    Cooney MT, Dudina AL, Graham IM. Value and limitations of existing scores for the assessment of cardiovascular risk: a review for clinicians. J Am Coll Cardiol. 2009;54:1209-1227.

    Not in our library10.1016/j.jacc.2009.07.020

  7. 7.

    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.

    Not in our library10.1002/14651858.cd006887.pub4

  8. 8.

    Conroy RM, Pyorala K, Fitzgerald AP. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J. 2003;24:987-1003.

    Not in our library10.1016/s0195-668x(03)00114-3

  9. 9.

    D'Agostino RB, Vasan RS, Pencina MJ. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117:743-753.

    Not in our library10.1161/circulationaha.107.699579

  10. 10.

    US Preventive Services Task Force. Statin use for the primary prevention of cardiovascular disease in adults: Us Preventive Services task force recommendation statement. JAMA. 2016;316:1997-2007.

    Not in our library10.1001/jama.2016.15450

  11. 11.

    Piepoli MF, Hoes AW, Agewall S. 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 and Rehabilitation (EACPR). Eur Heart J. 2016;37:2315-2381.

    Not in our library10.1093/eurheartj/ehw106

  12. 12.

    Pylypchuk R, Wells S, Kerr A. Cardiovascular disease risk prediction equations in 400 000 primary care patients in New Zealand: a derivation and validation study. Lancet. 2018;391:1897-1907.

    Not in our library10.1016/s0140-6736(18)30664-0

  13. 13.
    Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study · 2017
    No notice
  14. 14.

    D'Agostino RB, Pencina MJ, Massaro JM, Coady S. Cardiovascular disease risk assessment: insights from Framingham. Glob Heart. 2013;8:11-23.

    Not in our library10.1016/j.gheart.2013.01.001

  15. 15.

    Ueda P, Woodward M, Lu Y. Laboratory-based and office-based risk scores and charts to predict 10-year risk of cardiovascular disease in 182 countries: a pooled analysis of prospective cohorts and health surveys. Lancet Diabetes Endocrinol. 2017;5:196-213.

    Not in our library10.1016/s2213-8587(17)30015-3

  16. 16.

    Assmann G, Schulte H, Cullen P, Seedorf U. Assessing risk of myocardial infarction and stroke: new data from the Prospective Cardiovascular Munster (PROCAM) study. Eur J Clin Invest. 2007;37:925-932.

    Not in our library10.1111/j.1365-2362.2007.01888.x

  17. 17.

    Cooney MT, Dudina A, D'Agostino R, Graham IM. Cardiovascular risk-estimation systems in primary prevention: do they differ? Do they make a difference? Can we see the future? Circulation. 2010;122:300-310.

    Not in our library10.1161/circulationaha.109.852756

  18. 18.

    Mortensen MB, Nordestgaard BG. Comparison of five major guidelines for statin use in primary prevention in a contemporary general population. Ann Intern Med. 2018;168:85-92.

    Not in our library10.7326/m17-0681

  19. 19.

    van Houwelingen HC. Validation, calibration, revision and combination of prognostic survival models. Stat Med. 2000;19:3401-3415.

    Not in our library10.1002/1097-0258(20001230)19:24<3401::aid-sim554>3.0.co;2-2

  20. 20.

    Farzadfar F, Finucane MM, Danaei G. National, regional, and global trends in serum total cholesterol since 1980: systematic analysis of health examination surveys and epidemiological studies with 321 country-years and 3·0 million participants. Lancet. 2011;377:578-586.

    Not in our library10.1016/s0140-6736(10)62038-7

  21. 21.
    Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017 · 2018
    No notice
  22. 22.
    Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults · 2017
    No notice
  23. 23.
    Worldwide trends in blood pressure from 1975 to 2015: a pooled analysis of 1479 population-based measurement studies with 19·1 million participants · 2017
    Corrected
  24. 24.
    Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4·4 million participants · 2016
    No notice
  25. 25.

    WHO. STEPwise approach to surveillance (STEPS). 2017.

    Not in our library

  26. 26.

    Gaziano TA, Abrahams-Gessel S, Alam S. Comparison of nonblood-based and blood-based total CV risk scores in global populations. Glob Heart. 2016;11:37-46.

    Not in our library10.1016/j.gheart.2015.12.003

  27. 27.

    Danesh J, Erqou S, Walker M. The Emerging Risk Factors Collaboration: analysis of individual data on lipid, inflammatory and other markers in over 1·1 million participants in 104 prospective studies of cardiovascular diseases. Eur J Epidemiol. 2007;22:839-869.

    Not in our library10.1007/s10654-007-9165-7

  28. 28.

    Di Angelantonio E, Kaptoge S, Wormser D. Association of cardiometabolic multimorbidity with mortality. JAMA. 2015;314:52-60.

    Not in our library10.1001/jama.2015.7008

  29. 29.

    Murray CJ, Ezzati M, Flaxman AD. GBD 2010: design, definitions, and metrics. Lancet. 2012;380:2063-2066.

    Not in our library10.1016/s0140-6736(12)61899-6

  30. 30.
    Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016 · 2017
    No notice
  31. 31.

    Woodward M, Huxley R, Ueshima H, Fang X, Kim HC, Lam T-H. The Asia Pacific Cohort Studies Collaboration: a decade of achievements. Glob Heart. 2012;7:343-351.

    Not in our library10.1016/j.gheart.2012.10.001

  32. 32.

    Wang Y, Liu J, Wang W. Lifetime risk for cardiovascular disease in a Chinese population: the Chinese Multi–Provincial Cohort Study. Eur J Prev Cardiol. 2015;22:380-388.

    Not in our library10.1177/2047487313516563

  33. 33.

    Suebsamran P, Choenchoopon H, Rojanasaksothorn S, Loiha S, Chamnan P. Association between alcohol consumption and pre-diabetes among 383,442 Thai population aged 15 years and older in Ubon Ratchathani: analytical cross-sectional study. J Med Assoc Thai. 2016;99(suppl 1):S35-S42.

    Not in our library

  34. 34.

    Khalili D, Hadaegh F, Soori H, Steyerberg EW, Bozorgmanesh M, Azizi F. Clinical usefulness of the Framingham cardiovascular risk profile beyond its statistical performance: the Tehran Lipid and Glucose Study. Am J Epidemiol. 2012;176:177-186.

    Not in our library10.1093/aje/kws204

  35. 35.
    UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age · 2015
    No notice
  36. 36.

    Di Angelantonio E, Sarwar N, Perry P. Major lipids, apolipoproteins, and risk of vascular disease. JAMA. 2009;302:1993-2000.

    Not in our library10.1001/jama.2009.1619

  37. 37.

    Sarwar N, Gao P, Seshasai SR. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet. 2010;375:2215-2222.

    Not in our library10.1016/s0140-6736(10)60484-9

  38. 38.

    Lewington S, Clarke R, Qizilbash N, Peto R, Collins R. Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies. Lancet. 2002;360:1903-1913.

    Not in our library10.1016/s0140-6736(02)11911-8

  39. 39.

    Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21:1539-1558.

    Not in our library10.1002/sim.1186

  40. 40.

    Thompson S, Kaptoge S, White I. Statistical methods for the time-to-event analysis of individual participant data from multiple epidemiological studies. Int J Epidemiol. 2010;39:1345-1359.

    Not in our library10.1093/ije/dyq063

All 55 references

Cited by papers we hold 8

Reads

Read by people on this page

None yet.

Checked or read by AI through naulon

None yet.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.

Are you an author?

Is this your paper?

Claim it to add your note and be paid when agents cite it. Free.