Cardiovascular disease risk assessment in the general population: rationale for selection of prognostic model regarding age
https://doi.org/10.65249/1027-7218-2025-12-4-14
Abstract
This article conducts an analysis of risk scores for total cardiovascular risk assessment in the general population. Currently, a plethora of prognostic models for evaluating cardiovascular risk are available, especially for middle-aged and elderly individuals. Considering the latest recommendations, it is optimal to use SCORE2 and SCORE2-OP risk scores for total cardiovascular risk assessment in the general population and SCORE2-Diabetes – for patients with type 2 diabetes. Predicting cardiovascular risk in young individuals is a challenging task, as most risk scores are limited to those aged 40 and above. To date, for the estimation of total 10-year cardiovascular risk in individuals under 40, the following risk scores could be used: the modified Framingham scale 2008 (from age 30) and QRISK3 (from age 25) which are not yet validated in the Republic of Belarus. Conducting validation of risk score for cardiovascular risk assessment in young individuals is an important scientific and clinical task considering the excess cardiovascular morbidity within this demographic.
About the Authors
A. ShepelkevichБеларусь
D. Baalbaki
Беларусь
A. Pristrom
Беларусь
A. Yurenia
Беларусь
References
1. World Health Organization. Cardiovascular diseases. Available at: https://www.who.int/ru/health-topics/cardiovasculardiseases#tab=tab_1 (accessed: 10.08.2025)
2. Cesare M.Di, Bixby H., Gaziano T., et al. World Heart Report 2023: Confronting the world’s number one killer. World Heart Federation. 2023.
3. Chong B., Jayabaskaran J., Jauhari S.M., et al. Global burden of cardiovascular diseases: projections from 2025 to 2050. Eur J Preventive Cardiol. 2024. doi: 10.1093/eurjpc/zwae281.
4. Lindstrom M., DeCleene N., Dorsey H., et al. Global burden of cardiovascular diseases and risks collaboration, 1990–2021. J Am Coll Cardiol. 2022; 80. doi: 10.1016/j.jacc.2022.11.001.
5. Cenko E., Manfrini O., Fabin N., et al. Clinical determinants of ischemic heart disease in Eastern Europe. The Lancet Regional Health – Europe. 2023; 33. doi: 10.1016/j.lanepe.2023.100698.
6. European health information gateway. European mortality database. World Health Organization. Available at: https://gateway.euro.who.int/en/datasets/european-mortality-database/ (accessed: 19.07.2025)
7. Hajar R. Framingham contribution to cardiovascular disease. Heart Views. 2016; 17(2): 78–81.
8. Prevalence of risk factors for non-communicable diseases in the Republic of Belarus STEPS 2020. Vsemirnaya organizaciya zdravoohraneniya : Evropejskij region. 2020. 89. Available at: https://cdn.who.int/media/docs/default-source/ncds/ncd-surveillance/data-reporting/belarus/belarus_steps_report_2020_ru.pdf (accessed: 10.09.2025) (in Russian)
9. IDF Diabetes Atlas. Brussels. 2025. Available at: https://diabetesatlas.org/media/uploads/sites/3/2025/04/IDF_Atlas_11th_Edition_2025.pdf (accessed: 10.09.2025).
10. Global Health Estimates. WHO. 2023. Available at: https://www.who.int/data/global-health-estimates (accessed: 01.09.2025).
11. Antini C., Caixeta R., Luciani S., Hennis A. Diabetes mortality: trends and multi-country analysis of the Americas from 2000 to 2019. Int J Epidemiol. 2024; 53(1). doi: 10.1093/ije/dyad182.
12. Salko O.B., Solntseva A.V., Shepel'kevich A.P., et al. Current state of endocrinology service of the Republic of Belarus. Retsept. 2023; 26(5): 515–533. (in Russian)
13. Morrish N.J., Wang S.L., Stevens L.K., et al. Mortality and causes of death in the WHO multinational study of vascular disease in diabetes. Diabetologia. 2001; 44: 14–21.
14. Rose G. Sick individuals and sick populations. Int J Epidemiol. 1985; 14(1): 32–38.
15. Puska P., Vartiainen E., Laatikainen T., et al. The North Karelia project: from North Karelia to national action. The National Institute for Health and Welfare (THL). Helsinki. 2009; 309. Available at: https://www.julkari.fi/bitstream/handle/10024/80109/731beafd-b544-42b2-b853-baa87db6a046.pdf. (accessed: 01.09.2025).
16. Fuster V., Kelly B.B (ed.). Promoting cardiovascular health in the developing world: a critical challenge to achieve global health. Institute of Medicine (US) Committee on Preventing the Global Epidemic of Cardiovascular Disease: Meeting the Challenges in Developing Countries. Washington (DC). 2010; 5. doi: 10.17226/12815.
17. Visseren L.F., Mach F., Smulders Y.M., et al. ESC Scientific Document Group, 2021 ESC Guidelines on cardiovascular disease prevention in clinical practice. Eur Heart J. 2021; 42(34). doi: 10.1093/eurheartj/ehab484.
18. Arnett D.K., Blumenthal R.S., Albert M.A., et al. 2019 ACC/AHA guideline on the primary prevention of cardiovascular disease. Circulation. 2019; 140(11): doi: 10.1161/CIR.0000000000000725.
19. Van Daalen K.R., Zhang D., Kaptoge S., et al. Risk estimation for the primary prevention of cardiovascular disease: considerations for appropriate risk prediction model selection. The Lancet Global Health. 2024; 12(8). doi: 10.1016/S2214-109X(24)00210-9.
20. Collins G.S., Dhiman P., Andaur Navarro C.L., et al. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open. 2021; 11. doi: 10.1136/bmjopen-2020-048008.
21. National guidelines for the prevalence of cardiovascular diseases in clinical practice. Minsk. 2010; 20. (in Russian)
22. O poryadke provedeniya dispanserizacii vzroslogo naseleniya. postanovlenie Ministerstva zdravookhraneniya Respubliki Belarus' 16.12.2024 №173. Available at: https://pravo.by/document/?guid=12551&p0=W22442590 (accessed: 01.09.2025). (in Russian)
23. Conroy R.M., Pyorala K., Fitzgerald A., et al. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J. 2003; 24: 987–1003.
24. SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe. Eur Heart J. 2021; 42. doi: 10.1093/eurheartj/ehab309.
25. SCORE2-OP risk prediction algorithms: estimating incident cardiovascular event risk in older persons in four geographical risk regions. Eur Heart J. 2021; 42. doi: 10.1093/eurheartj/ehab312.
26. World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions. Lancet Glob Health. 2019; 7. doi: 10.1016/S2214-109X(19)30318-3.
27. Hajifathalian K., Ueda P., Lu Y., et al. A novel risk score to predict cardiovascular disease risk in national populations (Globorisk). Lancet Diabetes Endocrinol. 2015; 3: 339–355. doi: 10.1016/S2213-8587(15)00081-9.
28. McGorrian C., Yusuf S., Islam S., et al. Estimating modifiable coronary heart disease risk in multiple regions of the world: the INTERHEART modifiable risk score. Eur Heart J. 2011; 32: 581–589.
29. Eisen A., Giugliano R.P., Braunwald E. Updates on acute coronary syndrome: a review. JAMA Cardiol. 2016; 1: 718–730.
30. Gupta A., Wang Y., Spertus J.A., et al. Trends in acute myocardial infarction in young patients and differences by sex and race, 2001 to 2010. J Am Coll Cardiol. 2014; 64: 337–345. doi: 10.1016/j.jacc.2014.04.054.
31. Gooding H.C., Gidding S.S., Moran A.E., et al. Challenges and opportunities for the prevention and treatment of cardiovascular disease among young adults. J Am Hear Assoc Cardiovasc Cerebrovasc Dis. 2020; 9(19). doi: 10.1161/JAHA.120.016115.
32. Arora S., Stouffer G.A., Kucharska-Newton A.M., et al. Twenty-year trends and sex differences in young adults hospitalized with acute myocardial infarction. Circulation. 2019; 139: 1047–1056.
33. Bucholz E.M., Strait K.M., Dreyer R.P., et al. Sex differences in young patients with acute myocardial infarction. Eur Heart J Acute Cardiovasc Care. 2017; 6: 610–622.
34. Singh A., Collins B.L., Gupta A., et al. Cardiovascular risk and statin eligibility of young adults after an MI. J Am Coll Cardiol. 2018; 71(3): 292–302.
35. D'Agostino R.B., Vasan R.S., Pencina M.J., et al. General cardiovascular risk profile for use in primary care. Circulation. 2008; 117(6): 743–753.
36. Bastuji-Garin S., Deverly A., Moyse D., et al. The Framingham prediction rule is not valid in a European population of treated hypertensive patients. J Hypertension. 2002; 20(10): 1973–1980.
37. Empana J.P., Ducimetiere P., Arveiler D., et al. Are the Framingham and PROCAM coronary heart disease risk functions applicable to different European populations? The PRIME Study. Eur Heart J. 2003; 24(21): 1903–1911.
38. Hippisley-Cox J., Coupland C., Brindle P. Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease. BMJ. 2017; 357. doi: 10.1136/bmj.j2099.
Review
For citations:
Shepelkevich A., Baalbaki D., Pristrom A., Yurenia A. Cardiovascular disease risk assessment in the general population: rationale for selection of prognostic model regarding age. Healthcare. 2025;(12):4-14. (In Russ.) https://doi.org/10.65249/1027-7218-2025-12-4-14
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