BACKGROUND: Worldwide implementation of risk-based cardiovascular disease (CVD) prevention requires risk prediction tools that are contemporarily recalibrated for the target country and can be used where laboratory measurements are unavailable. We present two cardiovascular risk scores, with and without laboratory-based measurements, and the corresponding risk charts for 182 countries to predict 10-year risk of fatal and non-fatal CVD in adults aged 40-74 years. METHODS: Based on our previous laboratory-based prediction model (Globorisk), we used data from eight prospective studies to estimate coefficients of the risk equations using proportional hazard regressions. The laboratory-based risk score included age, sex, smoking, blood pressure, diabetes, and total cholesterol; in the non-laboratory (office-based) risk score, we replaced diabetes and total cholesterol with BMI. We recalibrated risk scores for each sex and age group in each country using country-specific mean risk factor levels and CVD rates. We used recalibrated risk scores and data from national surveys (using data from adults aged 40-64 years) to estimate the proportion of the population at different levels of CVD risk for ten countries from different world regions as examples of the information the risk scores provide; we applied a risk threshold for high risk of at least 10% for high-income countries (HICs) and at least 20% for low-income and middle-income countries (LMICs) on the basis of national and international guidelines for CVD prevention. We estimated the proportion of men and women who were similarly categorised as high risk or low risk by the two risk scores. FINDINGS: Predicted risks for the same risk factor profile were generally lower in HICs than in LMICs, with the highest risks in countries in central and southeast Asia and eastern Europe, including China and Russia. In HICs, the proportion of people aged 40-64 years at high risk of CVD ranged from 1% for South Korean women to 42% for Czech men (using a ≥10% risk threshold), and in low-income countries ranged from 2% in Uganda (men and women) to 13% in Iranian men (using a ≥20% risk threshold). More than 80% of adults were similarly classified as low or high risk by the laboratory-based and office-based risk scores. However, the office-based model substantially underestimated the risk among patients with diabetes. INTERPRETATION: Our risk charts provide risk assessment tools that are recalibrated for each country and make the estimation of CVD risk possible without using laboratory-based measurements. FUNDING: National Institutes of Health.

Baker IDI Heart and Diabetes Institute Melbourne VIC Australia

Center for Cardiovascular Prevention Charles University Prague 1st Faculty of Medicine and Thomayer Hospital Prague Czech Republic

Center for International Collaboration and Partnership National Institute of Health and Nutrition National Institutes of Biomedical Innovation Health and Nutrition Tokyo Japan

Central Department of Primary Health Care Ministry of Health Kuwait City Kuwait

Centro de Investigación en Salud Poblacional Instituto Nacional de Salud Publica Cuernavaca Mexico

Department of Endocrinology and Metabolism Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán Mexico City Mexico

Department of Epidemiology and Biostatistics School of Public Health Imperial College London London UK

Department of Global Health and Population Harvard School of Public Health Boston MA USA

Department of Global Health and Population Harvard School of Public Health Boston MA USA; Department of Epidemiology Harvard School of Public Health Boston MA USA

Department of Information Evidence and Research WHO Geneva Switzerland

Department of Internal Medicine Cleveland Clinic Cleveland OH USA

Department of Preventive Medicine and Public Health School of Medicine Universidad Autónoma de Madrid Idipaz and CIBER of Epidemiology and Public Health Madrid Spain

Department of Public Health Faculty of Medicine Pontifical Catholic University of Chile Santiago Chile

Division of Health and Nutrition Survey Korea Centers for Disease Control and Prevention Cheongwon gun South Korea

División Salud Pública y Medicina Familiar Pontificia Universidad Católica de Chile Santiago Chile

Endocrine Research Center Research Institute for Endocrine Sciences Shahid Beheshti University of Medical Sciences Tehran Iran

Epidemiology Research Unit Caribbean Institute for Health Research The University of the West Indies Kingston Jamaica

Institute of Health Policy and Management Seoul National University College of Medicine Seoul South Korea

MRC PHE Centre for Environment and Health Imperial College London London UK; Department of Epidemiology and Biostatistics School of Public Health Imperial College London London UK

MRC PHE Centre for Environment and Health Imperial College London London UK; Department of Epidemiology and Biostatistics School of Public Health Imperial College London London UK; Department of Natural Sciences School of Science and Technology Middlsex University London UK

MRC PHE Centre for Environment and Health Imperial College London London UK; Department of Epidemiology and Biostatistics School of Public Health Imperial College London London UK; Non Communicable Diseases Research Center Endocrinology and Metabolism Population Sciences Institute Tehran University of Medical Sciences Tehran Iran

MRC PHE Centre for Environment and Health Imperial College London London UK; Department of Epidemiology and Biostatistics School of Public Health Imperial College London London UK; WHO Collaborating Centre on NCD Surveillance and Epidemiology Imperial College London London UK; Wellcome Trust Centre for Global Health Research London UK

National Institute of Public Health University of Southern Denmark Copenhagen Denmark

Non communicable Diseases Prevention and Control Program at the Ministry of Health Kampala Uganda

Non Communicable Diseases Research Center Endocrinology and Metabolism Population Sciences Institute Tehran University of Medical Sciences Tehran Iran; Endocrinology and Metabolism Research Center Endocrinology and Metabolism Research Institute Tehran University of Medical Sciences Tehran Iran

Prevention of Metabolic Disorders Research Center Research Institute for Endocrine Sciences Shahid Beheshti University of Medical Sciences Tehran Iran

Statistical Unit Institute for Clinical and Experimental Medicine Prague Czech Republic

The George Institute for Global Health University of Oxford Oxford UK; The George Institute for Global Health University of Sydney Sydney NSW Australia; Department of Epidemiology Johns Hopkins University Baltimore MD USA

University of Health Sciences Phnom Penh Cambodia

WHO Malawi Country Office Lilongwe Malawi

Yale Yale New Haven Hospital Center for Outcomes Research and Evaluation New Haven CT USA

Lancet Diabetes Endocrinol. 2017 Mar;5(3):155-157. doi: 10.1016/S2213-8587(17)30016-5. PubMed

Zobrazit více v PubMed

WHO. Geneva: World Health Organization; 2014. Global status report on noncommunicable diseases 2014.

WHO. Geneva: World Health Organization; 2013. Global action plan for the prevention and control of noncommunicable diseases 2013–2020.

WHO. Geneva: World Health Organization; 2007. Prevention of cardiovascular disease—guidelines for assessment and management of cardiovascular risk.

Cook NR, Paynter NP, Eaton CB, et al. Comparison of the Framingham and Reynolds Risk scores for global cardiovascular risk prediction in the multiethnic Women’s Health Initiative. Circulation. 2012;125:1748–1756. S1–S11. PubMed PMC

Neuhauser HK, Ellert U, Kurth B-M. A comparison of Framingham and SCORE-based cardiovascular risk estimates in participants of the German National Health Interview and Examination Survey 1998. Eur J Cardiovasc Prev Rehabil. 2005;12:442–450. PubMed

D’Agostino RB, Grundy S, Sullivan LM, Wilson P. Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. JAMA. 2001;286:180–187. PubMed

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. PubMed

Chen L, Tonkin AM, Moon L, et al. Recalibration and validation of the SCORE risk chart in the Australian population: the AusSCORE chart. Eur J Cardiovasc Prev Rehabil. 2009;16:562–570. PubMed

Hajifathalian K, Ueda P, Lu Y, et al. A novel risk score to predict cardiovascular disease risk in national populations (Globorisk): a pooled analysis of prospective cohorts and health examination surveys. Lancet Diabetes Endocrinol. 2015;3:339–355. PubMed PMC

Korn EL, Graubard BI, Midthune D. Time-to-event analysis of longitudinal follow-up of a survey: choice of the time-scale. Am J Epidemiol. 1997;145:72–80. PubMed

Singh GM, Danaei G, Farzadfar F, et al. The age-specific quantitative effects of metabolic risk factors on cardiovascular diseases and diabetes: a pooled analysis. PLoS One. 2013;8:e65174. PubMed PMC

Peters SA, Huxley RR, Woodward M. Diabetes as a risk factor for stroke in women compared with men: a systematic review and meta-analysis of 64 cohorts, including 775 385 individuals and 12 539 strokes. Lancet. 2014;383:1973–1980. PubMed

Huxley RR, Woodward M. Cigarette smoking as a risk factor for coronary heart disease in women compared with men: a systematic review and meta-analysis of prospective cohort studies. Lancet. 2011;378:1297–1305. PubMed

Lu Y, Hajifathalian K, Rimm EB, Ezzati M, Danaei G. Mediators of the effect of body mass index on coronary heart disease: decomposing direct and indirect effects. Epidemiology. 2015;26:153–162. PubMed

Mongraw-Chaffin ML, Peters SAE, Huxley RR, Woodward M. The sex-specific association between BMI and coronary heart disease: a systematic review and meta-analysis of 95 cohorts with 1·2 million participants. Lancet Diabetes Endocrinol. 2015;3:437–449. PubMed PMC

Farzadfar F, Finucane MM, Danaei G, et al. 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. PubMed

NCD Risk Factor Collaboration (NCD-RisC) Worldwide trends in blood pressure from 1975 to 2015: a pooled analysis of 1479 population-based measurement studies with 19·1 million participants. Lancet. 2017;389:37–55. PubMed PMC

Ng M, Freeman MK, Fleming TD, et al. Smoking prevalence and cigarette consumption in 187 countries, 1980–2012. JAMA. 2014;311:183–192. PubMed

NCD Risk Factor Collaboration. Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19·2 million participants. Lancet. 2016;387:1377–1396. PubMed PMC

NCD Risk Factor Collaboration. Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4·4 million participants. Lancet. 2016;387:1513–1530. PubMed PMC

WHO. Geneva: World Health Organization; 2016. Global health estimates: deaths by cause age, sex and country, 2000–2015 [Draft, October 2016]

Forouzanfar MH, Moran AE, Flaxman AD, et al. Assessing the global burden of ischemic heart disease, part 2: analytic methods and estimates of the global epidemiology of ischemic heart disease in 2010. Glob Heart. 2012;7:331–342. PubMed PMC

Feigin VL, Lawes CMM, Bennett DA, Barker-Collo SL, Parag V. Worldwide stroke incidence and early case fatality reported in 56 population-based studies: a systematic review. Lancet Neurol. 2009;8:355–369. PubMed

NICE. Cardiovascular risk assessment and the modification of blood lipids for the primary and secondary prevention of cardiovascular disease. London: National Institute for Health and Care Excellence; 2014. Lipid modification. PubMed

Goff DC, Lloyd-Jones DM, Bennett G, et al. 2013 ACC/AHA Guideline on the Assessment of Cardiovascular Risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2014;63:2935–2959. PubMed PMC

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. PubMed PMC

Yusuf S, Rangarajan S, Teo K, et al. Cardiovascular risk and events in 17 low-, middle-, and high-income countries. N Engl J Med. 2014;371:818–827. PubMed

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

Beagley J, Guariguata L, Weil C, Motala AA. Global estimates of undiagnosed diabetes in adults. Diabetes Res Clin Pract. 2014;103:150–160. PubMed

Matheny M, McPheeters M, Glasser A, et al. Systematic review of cardiovascular disease risk assessment tools. Evid Synth Assess. 2011;85:1–394. PubMed

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. PubMed

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

Jackson R, Kerr A, Wells S, et al. ‘Should we reconsider the role of age in treatment allocation for primary prevention of cardiovascular disease?’ No, but we can improve risk communication metrics. Eur Heart J. 2016;290:2277–2283. PubMed

WHO. Geneva: World Health Organization; 2017. WHO methods and data sources for global causes of death 2000–2015.

Woodward M, Huxley H, Lam TH, Barzi F, Lawes CMM, Ueshima H. A comparison of the associations between risk factors and cardiovascular disease in Asia and Australasia. Eur J Cardiovasc Prev Rehabil. 2005;12:484–491. PubMed