Algorithm for assessing the total 10 years risk of death from cardiovascular diseases in women 25-64 years old in Tyumen (Tyumen risk scale)
https://doi.org/10.38109/2225-1685-2021-3-14-21
Abstract
Purpose: To define total 10-year cardiovascular mortality risk in Russian females in dependence on traditional and psychosocial risk factors (RF) and to design the algorithm of its estimation.
Methods. The study included non-organized population of Central Administrative district of Tyumen city. Epidemiological study, based on the representative selection of 1000 females aged 25-64 years. Screening respond was 81.3%. Cardiovascular mortality rate within 10 years was studied. Totally, 31 cases of cardiovascular death were registered in female cohort within 10year follow-up. We used a multivariate Cox regression model to estimate hazard ratio (HR) and confidence interval (CI). Relations between mortality rate and factors such as age, smoking, education, occupation, marital status, systolic and diastolic blood pressure (SBP and DBP), body mass index, total cholesterol, cholesterol of low and high density lipoproteins were analyzed.
Results. To build a model of total cardiovascular risk, six statistically significant indicators were selected: age (HR – 1.099, 95% CI 1.032-1.1.69), SBP (1.026, 95% CI 1.011-1.041), primary education (4.315, 95% CI 1.878-9.910), work associated with heavy physical labor (4.073, 95% CI 1.324-12.528), executives (3.822, 95% CI 1.386-10.537) and marital status (2.978, 95% CI 1.197-7.409). Based on these data, model for total cardiovascular mortality risk in females was designed with good predictive accuracy (AUC was 0.882, 95% CI – 0.833 – 0.930).
Conclusion. Thus, created mathematical model, built based on statistically significant traditional and psychosocial RF, makes it possible to effectively predict the total cardiovascular risk at the individual level in the female population.
About the Authors
G. S. PushkarevRussian Federation
Georgiy S. Pushkarev, Cand. of Sci. (Med.), Scientific Researcher, Laboratory of Instrumental Diagnostics, Scientific Department of Instrumental Research Methods
111 Melnikaite Str., Tyumen 625026
S. T. Matskeplishvili
Russian Federation
Simon T. Matskeplishvili, Dr. of Sci. (Med.), FESC, FACC, Professor of cardiology, Member of the Russian academy of sciences. Director for science and research and Head of department of biomedical Informatics
27/10 Lomonosovsky prospect, Moscow 119234
V. A. Kuznetsov
Russian Federation
Vadim A. Kuznetsov, Dr. of Sci. (Med.), Professor, Honored Scientist, Scientific Consultant
111 Melnikaite Str., Tyumen 625026
E. V. Akimova
Russian Federation
Ekaterina V. Akimova, Dr. of Sci. (Med.), Head of the Laboratory of Epidemiology and Prevention of Cardiovascular Diseases of the Scientific Department of Instrumental Research Methods
111 Melnikaite Str., Tyumen 625026
References
1. Oganov R.G., Shalnova S.A., Kalinina A.M. Prevention of cardiovascular disease: the guide. M.: GEOTAR-Mediya, 2009. P. 216 (In Russ.). ISBN 978-5-9704-1110-0
2. Chepurina N.A., Mamedov M.N., Deev A.D., Кisseleva N.V. Ten-year dynamics of risk factors and total cardiovascular risk in а cohort of male intellectual workers. Cardiovascular Therapy and Prevention. 2008; 7(7): 27-33. (In Russ.)
3. Boytsov S.A., Shalnova S.A., Kontsevaya A.V. et al. Trends in simulated 10-year mortality rates and the evaluation of the socioeconomic efficiency of different scenarios of prevention. Preventive Medicine. 2016;19(3):12-18. (In Russ.) https://doi.org/10.17116/profmed201619312-18
4. Mamedov M.N., Chepurina N.A., Tokareva Z.N., Evdokimova A.A. Reduction of cumulative cardiovascular risk in patients with arterial hypertension: the role of angiotensin converting enzyme inhibitors according to the new European recommendations. Rational Pharmacother. Card. 2007; 3(3): 72-76. (In Russ.) https://doi.org/10.20996/1819-6446-2007-3-3-72-76
5. Boytsov S.A. Recent trends in and new data on the epidemiology and prevention of non-communicable diseases. Terapevticheskii Arkhiv. 2016;88(1):4-10. (In Russ.) https://doi.org/10.17116/terarkh20168814-10
6. Berger J.S., Jordan C.O., Lloyd-Jones D., Blumenthal R.S. Screening for cardiovascular risk in asymptomatic patients. J Am Coll Cardiol. 2010;55(12):1169-77. https://doi.org/10.1016/j.jacc.2009.09.066
7. Woodward M., Brindle P., Tunstall-Pedoe H. Adding social deprivation and family history to cardiovascular risk assessment: the ASSIGN score from the Scottish Heart Health Extended Cohort (SHHEC). Heart 2007; 93(2):172-6. https://doi.org/10.1136/hrt.2006.108167
8. Hernández-Orallo J. ROC curves for regression. Pattern Recognition. 2013; 46(12): 3395-3411. https://doi.org/10.1016/j.patcog.2013.06.014
9. Truhacheva N.V. Medical statistics. Textbook. Feniks, 2017. P. 324 (In Russ.) ISBN: 978-5-222-27580-1
10. Piepoli M., Hoes A., Agewall S., et al. 2016 European Guidelines on cardiovascular disease prevention in clinical practice. European Heart Journal. 2016;37(29):2315-2381. https://doi.org/10.1093/eurheartj/ehw106
11. Collins D.R., Tompson A.C., Onakpoya I.J. et al. Global cardiovascular risk assessment in the primary prevention of cardiovascular disease in adults: systematic review of systematic reviews. BMJ Open. 2017 Mar 24;7(3):e013650. http://dx.doi.org/10.1136/bmjopen-2016-013650
12. Belyalov F.I. Application of prediction scores in clinical medicine. Russ J Cardiol 2016, 12 (140): 23–27. (In Russ.) http://dx.doi.org/10.15829/1560-4071-2016-12-23-27
13. Gaziano T.A., Young C.R., Fitzmaurice G., et al. Laboratory-based versus non-laboratory-based method for assessment of cardiovascular disease risk: the NHANES I Follow-up Study cohort. Lancet. 2008;371(9616):923-31. http://dx.doi.org/10.1016/S0140-6736(08)60418-3
14. Berger J.S., Jordan C.O., Lloyd-Jones D., Blumenthal R.S. Screening for cardiovascular risk in asymptomatic patients. J Am Coll Cardiol. 2010;55(12):1169-1177. http://dx.doi.org/10.1016/j.jacc.2009.09.066
15. Selvarajah S., Kaur G., Haniff J., et al. Comparison of the Framingham Risk Score, SCORE and WHO/ISH cardiovascular risk prediction models in an Asian population. Int J Cardiol. 2014; 176(1):211-8. http://dx.doi.org/10.1016/j.ijcard.2014.07.066
16. de la Iglesia B., Potter J.F., Poulter N.R., et al. Performance of the ASSIGN cardiovascular disease risk score on a UK cohort of patients from general practice. Heart. 2011;97(6):491-9. http://dx.doi.org/10.1136/hrt.2010.203364
17. Packer S.J., Cairns S., Robertson C., et al. Determining the effect of social deprivation on the prevalence of healthcare-associated infections in acute hospitals: a multivariate analysis of a linked data set. J Hosp Infect. 2015; 91(4):351-7. http://dx.doi.org/10.1016/j.jhin.2015.06.014
18. Hippisley-Cox J., Coupland C., Vinogradova Y., et al. Performance of the QRISK cardiovascular risk prediction algorithm in an independent UK sample of patients from general practice: a validation study. Heart. 2008;94(1):34-9. http://dx.doi.org/10.1136/hrt.2007.134890
19. Shalnova S.A., Kalinina A.M., Deev A.D., Pustelenin A.V. Russian expert system ORISKON – assessment of the major non-communicable disease risk. Cardiovascular Therapy and Prevention. 2013;12(4):51-55. (In Russ.) https://doi.org/10.15829/1728-8800-2013-4-51-55
Review
For citations:
Pushkarev G.S., Matskeplishvili S.T., Kuznetsov V.A., Akimova E.V. Algorithm for assessing the total 10 years risk of death from cardiovascular diseases in women 25-64 years old in Tyumen (Tyumen risk scale). Eurasian heart journal. 2021;(3):14-21. (In Russ.) https://doi.org/10.38109/2225-1685-2021-3-14-21