Prediction of Subclinical Coronary Atherosclerosis in Patients with High and Very High Cardiovascular Risk
https://doi.org/10.18087/cardio.2020.2.n964
Abstract
Objective To develop a diagnostic rule for detection of patients (pts) with high probability of subclinical atherosclerosis among those with high or very high cardiovascular (CV) risk.
Materials and Methods This cross-sectional study enrolled 52 pts (32 men [62 %]), aged 40 to 65 years [mean age 54.6±8.0]) with high or very high CV risk (5–9 and ≥10 % by The Systematic Coronary Risk Estimation Scale [SCORE], respectively). All participants underwent cardiac computed tomography (CT) angiography and calcium scoring. Traditional risk factors (RFs) (family history of premature CVD, smoking, overweight / obesity and abdominal obesity, hypertension, type 2 diabetes mellitus, lipids parameters (total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides) and lipids-related markers (apolipoprotein A1, apolipoprotein B, ApoB / ApoA1 ratio), biomarkers of inflammation (high-sensitivity C-reactive protein [hs CRP], fibrinogen), indicator carbohydrate metabolism (glucose), ankle-brachial index, stress-test, carotid plaques according to ultrasound were evaluated in all pts. Psychological RFs were evaluated using Hospital Anxiety and Depression Scale and DS-14 for type D personality.
Results All pts were divided into 2 groups according to the CT angiography results: pts in the main group (n=21) had any non-obstructive lesions or calcium score >0, pts in the control group (n=31) had intact coronary arteries. The groups did not differ in age or gender. 26 multiple linear logistic models for any subclinical atherosclerosis were developed based on obtained diagnostic features. Taking into account R-square = 0.344 (p=0.0008), the best fitting model was follows: subclinical coronary atherosclerosis= –1.576 + 0.234 x SCORE ≥5 % + 0.541 x hs CRP >2 g / l +0.015 x heart rate (bpm) +0.311 family history of premature CVD. The developed algorithm had sensitivity of 63 % and specificity of 80 %.
Conclusion The created diagnostic model diagnostic model suggests the presence of subclinical coronary atherosclerosis in patients with high / very high CV risk with a high degree of probability. This easy-to-use method can be used in routine clinical practice to improve risk stratification and management choices in high-risk pts.
About the Authors
N. V. PogosovaRussian Federation
Moscow
Y. M. Yufereva
Russian Federation
Moscow
N. P. Kachanova
Russian Federation
Moscow
V. A. Metelskaya
Russian Federation
Moscow
I. Y. Koltunov
Russian Federation
Moscow
V. P. Voronina
Russian Federation
Moscow
A. P. Mazaev
Russian Federation
Moscow
A. A. Arutyunov
Russian Federation
Moscow
V. A. Vygodin
Russian Federation
Moscow
References
1. Boytsov S.A., Pogosova N.V., Bubnova M.G., Drapkina O.M., Gavrilova N.E., Yeganyan R.A. et al. Cardiovascular prevention 2017. National guidelines. Russian Journal of Cardiology. 2018;23(6):7–122. In Russian DOI: 10.15829/1560-4071-2018-6-7-122
2. Piepoli MF, Hoes AW, Agewall S, Albus C, Brotons C, Catapano AL et al. 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 & Rehabilitation (EACPR). European Heart Journal. 2016;37(29):2315–81. DOI: 10.1093/eurheartj/ehw106
3. Helfand M, Buckley DI, Freeman M, Fu R, Rogers K, Fleming C et al. Emerging Risk Factors for Coronary Heart Disease: A Summary of Systematic Reviews Conducted for the U.S. Preventive Services Task Force. Annals of Internal Medicine. 2009;151(7):496–507. DOI: 10.7326/0003-4819-151-7-200910060-00010
4. Khot UN. Prevalence of Conventional Risk Factors in Patients With Coronary Heart Disease. JAMA. 2003;290(7):898–904. DOI: 10.1001/jama.290.7.898
5. Vasan RS. Biomarkers of Cardiovascular Disease: Molecular Basis and Practical Considerations. Circulation. 2006;113(19):2335–62. DOI: 10.1161/CIRCULATIONAHA.104.482570
6. Schlendorf KH, Nasir K, Blumenthal RS. Limitations of the Framingham risk score are now much clearer. Preventive Medicine. 2009;48(2):115–6. DOI: 10.1016/j.ypmed.2008.12.002
7. Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obuchowski N et al. Assessing the Performance of Prediction Models: A Framework for Traditional and Novel Measures. Epidemiology. 2010;21(1):128–38. DOI: 10.1097/EDE.0b013e3181c30fb2
8. Langlois MR. Laboratory approaches for predicting and managing the risk of cardiovascular disease: postanalytical opportunities of lipid and lipoprotein testing. Clinical Chemistry and Laboratory Medicine. 2012;50(7):1169–81. DOI: 10.1515/cclm-2011-0636
9. Wang TJ, Gona P, Larson MG, Tofler GH, Levy D, Newton-Cheh C et al. Multiple Biomarkers for the Prediction of First Major Cardiovascular Events and Death. New England Journal of Medicine. 2006;355(25):2631–9. DOI: 10.1056/NEJMoa055373
10. Melander O. Novel and Conventional Biomarkers for Prediction of Incident Cardiovascular Events in the Community. JAMA. 2009;302(1):49–57. DOI: 10.1001/jama.2009.943
11. Blankenberg S, Zeller T, Saarela O, Havulinna AS, Kee F, Tunstall-Pedoe H et al. Contribution of 30 Biomarkers to 10-Year Cardiovascular Risk Estimation in 2 Population Cohorts: The MONICA, Risk, Genetics, Archiving, and Monograph (MORGAM) Biomarker Project. Circulation. 2010;121(22):2388–97. DOI: 10.1161/CIRCULATIONAHA.109.901413
12. Wang TJ. Assessing the Role of Circulating, Genetic, and Imaging Biomarkers in Cardiovascular Risk Prediction. Circulation. 2011;123(5):551–65. DOI: 10.1161/CIRCULATIONAHA.109.912568
13. Metelskaya V.A., Gavrilova N.E., Yarovaya E.A., Boytsov S.A. An integrative boimarker: opportunities for non-invasive diagnostics of coronary atherosclerosis. Russian Journal of Cardiology. 2017;22(6):132–8. In Russian DOI: 10.15829/1560-4071-2017-6-132-138
14. Pogosova N.V., Yufereva Yu.M., Kachanova N.P., Metelskaya V.A., Koltunov I.Y., Voronina V.P. et al. An exploration of potential approaches to improve the diagnosis of subclinical atherosclerosis in patients with high cardiovascular risk. Kardiologiia. 2019;59(11S):53–62. In Russian DOI: 10.18087/cardio.n471
15. Zigmond AS, Snaith RP. The hospital anxiety and depression scale. Acta Psychiatrica Scandinavica. 1983;67(6):361–70. PMID: 6880820
16. Denollet J. DS14: Standard Assessment of Negative Affectivity, Social Inhibition, and Type D Personality. Psychosomatic Medicine. 2005;67(1):89–97. DOI: 10.1097/01.psy.0000149256.81953.49
17. Van Bortel LM, Laurent S, Boutouyrie P, Chowienczyk P, Cruickshank JK, De Backer T et al. Expert consensus document on the measurement of aortic stiffness in daily practice using carotid-femoral pulse wave velocity. Journal of Hypertension. 2012;30(3):445–8. DOI: 10.1097/HJH.0b013e32834fa8b0
18. Silber S. Comparison of spiral and electron beam tomography in the evaluation of coronary calcification in asymptomatic persons. International Journal of Cardiology. 2002;82(3):297–8. DOI: 10.1016/S0167-5273(01)00627-1
19. Agatston AS, Janowitz WR, Hildner FJ, Zusmer NR, Viamonte M, Detrano R. Quantification of coronary artery calcium using ultrafast computed tomography. Journal of the American College of Cardiology. 1990;15(4):827–32. DOI: 10.1016/0735-1097(90)90282-T
Review
For citations:
Pogosova N.V., Yufereva Y.M., Kachanova N.P., Metelskaya V.A., Koltunov I.Y., Voronina V.P., Mazaev A.P., Arutyunov A.A., Vygodin V.A. Prediction of Subclinical Coronary Atherosclerosis in Patients with High and Very High Cardiovascular Risk. Kardiologiia. 2020;60(2):75-82. https://doi.org/10.18087/cardio.2020.2.n964