Применение шкал сердечно-сосудистого риска для идентификации атеросклероза сонных артерий у больных ревматоидным артритом
Применение шкал сердечно-сосудистого риска для идентификации атеросклероза сонных артерий у больных ревматоидным артритом
Герасимова Е.В., Попкова Т.В., Герасимова Д.А., Глухова С.И., Насонов Е.Л., Лила А.М. Применение шкал сердечно-сосудистого риска для идентификации атеросклероза сонных артерий у больных ревматоидным артритом. Терапевтический архив. 2021; 93 (5): 561–567. DOI: 10.26442/00403660.2021.05.200787
DOI: 10.26442/00403660.2021.05.200787
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DOI: 10.26442/00403660.2021.05.200787
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Аннотация
Цель. Оценить сердечно-сосудистый риск (ССР) и проанализировать его связь с выявлением раннего атеросклеротического поражения сонных артерий у больных ревматоидным артритом (РА).
Материалы и методы. В исследование включены 109 пациентов с РА в возрасте от 45 до 60 лет без установленных сердечно-сосудистых заболеваний (ССЗ). Медиана возраста составила 52 [48; 54] года, продолжительности РА – 120 [36; 204] мес; DAS28 – 4,7 [3,5; 5,6] балла. Пациентам определен суммарный ССР по шкалам mSCORE, Reynolds Risk Score (RRS), ASSIGN, QRISK3, ERS-RА, и выполнена ультразвуковая допплерография сонных артерий.
Результаты. По результатам расчета ССР по шкалам mSCORE, RRS, ASSIGN, QRISK3, ERS-RA высокий риск обнаружен у 5, 5, 14, 6
и 38% больных соответственно. У 30% больных выявлены атеросклеротические бляшки сонных артерий. Продемонстрирована связь толщины комплекса интима–медиа (КИМ) сонных артерий со всеми калькуляторами ССР, а также с возрастом, показателями систолического и диастолического артериального давления, уровнями холестерина, скорости оседания эритроцитов, интерлейкина-6. Чувствительность
и специфичность алгоритмов ССР в прогнозировании атеросклеротического поражения сонных артерий составили для mSCORE 73 и 67%, для RRS – 64 и 63%, для ASSIGN – 64 и 56%, для QRISK3 – 73 и 49% соответственно, р<0,05 во всех случаях, для ERS-RА – 67 и 50%, р=0,06.
Заключение. Калькуляторы mSCORE, RRS, ASSIGN, QRISK3 в равной степени прогнозируют атеросклеротическое поражение сонных артерий у больных РА. Оптимальное соотношение специфичности и чувствительности показано для шкалы mSCORE. Стратификация риска ССЗ у больных РА должна включать оценку толщины КИМ сонных артерий. Для идентификации риска развития ССЗ у больных РА наиболее информативными являются вычисление mSCORE и определение толщины КИМ сонных артерий.
Ключевые слова: ревматоидный артрит, сердечно-сосудистые заболевания, шкала SCORE, шкала риска Рейнольдса, шкала ASSIGN,
алгоритм QRISK3, шкала ERS-RA
Materials and methods. One hundred and nine RA patients aged 45 to 60 without established cardiovascular diseases (CVD) were included in the study. The median age was 52 [48; 54] years, duration of RA was 120 [36; 204] months, DAS28 was 4.7 [3.5; 5.6] points. CVD risk was calculated with mSCORE, Reynolds Risk Score (RRS), ASSIGN, QRISK3, ERS-RA scales and Carotid Artery Doppler Ultrasound Exam was performed for all patients.
Results. High risk was found in 5, 5, 14, 6, and 38% of patients according to mSCORE, RRS, ASSIGN, QRISK3, ERS-RA scales, respectively. Atherosclerotic plaques of carotid arteries were found in 30% of patients. It was found that carotid intima-media thickness is correlated to all CVR calculators, age, systolic and diastolic blood pressure, cholesterol, erythrocyte sedimentation rate, interleukin-6 levels. The sensitivity and specificity of the CVR algorithms in prognostication of atherosclerotic carotid artery lesions were 73 and 67% for mSCORE, 64 and 63% for RRS, 64 and 56% for ASSIGN, 73 and 49% for QRISK3, respectively, p<0.05 in all cases, 67 and 50% for ERS-RA, p=0.06.
Conclusion. RRS, mSCORE, ASSIGN, QRISK3 calculators equally predict atherosclerotic carotid artery damage in RA patients. The optimal ratio of specificity and sensitivity is shown for the mSCORE scale. Stratification of CVR in RA patients should include assessment of the carotid intima-media thickness. To identify CVR in RA patients, the most informative methods are mSCORE calculation and carotid intima-media thickness determination.
Keywords: rheumatoid arthritis, cardiovascular diseases, SCORE, Reynolds Risk Score, ASSIGN, QRISK3, ERS-RA
Материалы и методы. В исследование включены 109 пациентов с РА в возрасте от 45 до 60 лет без установленных сердечно-сосудистых заболеваний (ССЗ). Медиана возраста составила 52 [48; 54] года, продолжительности РА – 120 [36; 204] мес; DAS28 – 4,7 [3,5; 5,6] балла. Пациентам определен суммарный ССР по шкалам mSCORE, Reynolds Risk Score (RRS), ASSIGN, QRISK3, ERS-RА, и выполнена ультразвуковая допплерография сонных артерий.
Результаты. По результатам расчета ССР по шкалам mSCORE, RRS, ASSIGN, QRISK3, ERS-RA высокий риск обнаружен у 5, 5, 14, 6
и 38% больных соответственно. У 30% больных выявлены атеросклеротические бляшки сонных артерий. Продемонстрирована связь толщины комплекса интима–медиа (КИМ) сонных артерий со всеми калькуляторами ССР, а также с возрастом, показателями систолического и диастолического артериального давления, уровнями холестерина, скорости оседания эритроцитов, интерлейкина-6. Чувствительность
и специфичность алгоритмов ССР в прогнозировании атеросклеротического поражения сонных артерий составили для mSCORE 73 и 67%, для RRS – 64 и 63%, для ASSIGN – 64 и 56%, для QRISK3 – 73 и 49% соответственно, р<0,05 во всех случаях, для ERS-RА – 67 и 50%, р=0,06.
Заключение. Калькуляторы mSCORE, RRS, ASSIGN, QRISK3 в равной степени прогнозируют атеросклеротическое поражение сонных артерий у больных РА. Оптимальное соотношение специфичности и чувствительности показано для шкалы mSCORE. Стратификация риска ССЗ у больных РА должна включать оценку толщины КИМ сонных артерий. Для идентификации риска развития ССЗ у больных РА наиболее информативными являются вычисление mSCORE и определение толщины КИМ сонных артерий.
Ключевые слова: ревматоидный артрит, сердечно-сосудистые заболевания, шкала SCORE, шкала риска Рейнольдса, шкала ASSIGN,
алгоритм QRISK3, шкала ERS-RA
________________________________________________
Materials and methods. One hundred and nine RA patients aged 45 to 60 without established cardiovascular diseases (CVD) were included in the study. The median age was 52 [48; 54] years, duration of RA was 120 [36; 204] months, DAS28 was 4.7 [3.5; 5.6] points. CVD risk was calculated with mSCORE, Reynolds Risk Score (RRS), ASSIGN, QRISK3, ERS-RA scales and Carotid Artery Doppler Ultrasound Exam was performed for all patients.
Results. High risk was found in 5, 5, 14, 6, and 38% of patients according to mSCORE, RRS, ASSIGN, QRISK3, ERS-RA scales, respectively. Atherosclerotic plaques of carotid arteries were found in 30% of patients. It was found that carotid intima-media thickness is correlated to all CVR calculators, age, systolic and diastolic blood pressure, cholesterol, erythrocyte sedimentation rate, interleukin-6 levels. The sensitivity and specificity of the CVR algorithms in prognostication of atherosclerotic carotid artery lesions were 73 and 67% for mSCORE, 64 and 63% for RRS, 64 and 56% for ASSIGN, 73 and 49% for QRISK3, respectively, p<0.05 in all cases, 67 and 50% for ERS-RA, p=0.06.
Conclusion. RRS, mSCORE, ASSIGN, QRISK3 calculators equally predict atherosclerotic carotid artery damage in RA patients. The optimal ratio of specificity and sensitivity is shown for the mSCORE scale. Stratification of CVR in RA patients should include assessment of the carotid intima-media thickness. To identify CVR in RA patients, the most informative methods are mSCORE calculation and carotid intima-media thickness determination.
Keywords: rheumatoid arthritis, cardiovascular diseases, SCORE, Reynolds Risk Score, ASSIGN, QRISK3, ERS-RA
Список литературы
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9. Crowson CS, Rollefstad S, Ikdahl E, et al. A Trans-Atlantic Cardiovascular Consortium for Rheumatoid Arthritis (ATACC-RA). Ann Rheum Dis. 2018;77(1):48-54. DOI:10.1136/annrheumdis-2017-211735.5
10. D’Agostino RB Sr, Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117(6):743-53. DOI:10.1161/CIRCULATIONAHA.107.699579
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13. Goff DC Jr, 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. Circulation. 2014;129(25 Suppl.2):S49-73. DOI:10.1161/01.cir.0000437741.48606.98
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18. Woodward M, Brindle P, Tunstall-Pedoe H. Adding social deprivation and family history to cardio-vascular risk assessment: the ASSIGN score from the Scottish Heart Health Extended Cohort (SHHEC). Heart. 2007;93:172-6.
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22. Solomon DH, Greenberg J, Curtis JR, et al. Derivation and internal validation of an expanded cardiovascular risk prediction score for rheumatoid arthritis: a Consortium of Rheumatology Researchers of North America Registry Study. Arthritis Rheumatol. 2015;67(8):1995-2003. DOI:10.1002/art.39195
23. Saba L, Jamthikar A, Gupta D, et al. Global perspective on carotid intima-media thickness and plaque: should the current measurement guidelines be revisited? Int Angiol. 2019;38(6):451-65. DOI:10.23736/s0392-9590.19.04267-6
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2. Panafidina TA, Kondratyeva LV, Gerasimova EV, et al. Comorbidity in rheumatoid arthritis. Rheumatology Science and Practice. 2014;52(3):283-9 (in Russian)
DOI:10.14412/1995-4484-2014-283-289
3. Gordeev AV, Galushko EA, Savushkina NM, et al. Assessing the multimorbid profile (CIRS) in rheumatoid arthritis. First results. Mod Rheumatol. 2019;13(3):10-6 (in Russian) DOI:10.14412/1996-7012-2019-3-10-16
4. Balsa A, Lojo-Oliveira L, Alperi-López M, et al. Prevalence of comorbidities in rheumatoid arthritis and evaluation of their monitoring in clinical practice: the spanish cohort of the COMORA study. Reumatol Clin. 2019;15(2):102-8.
DOI:10.1016/j.reuma.2017.06.002
5. Avina-Zubieta JA, Thomas J, Sadatsafavi M, et al. Risk of incident cardiovascular events in patients with rheumatoid arthritis: a meta-analysis of observational studies. Ann Rheum Dis. 2012;71(9):1524-9. DOI:10.1136/annrheumdis-2011-200726
6. Wang H, Li X, Gong G. Cardiovascular outcomes in patients with co-existing coronary artery disease and rheumatoid arthritis: A systematic review and meta-analysis. Medicine (Baltimore). 2020;99(14):e19658. DOI:10.1097/MD.0000000000019658
7. Dadoun S, Zeboulon-Ktorza N, Combescure C, et al. Mortality in rheumatoid arthritis over the last fifty years: systematic review and meta-analysis. Joint Bone Spine. 2013;80(1):29-33. DOI:10.1016/j.jbspin.2012.02.005
8. Pappas DA, Nyberg F, Kremer JM, et al. Prevalence of cardiovascular disease and major risk factors in patients with rheumatoid arthritis:
a multinational cross-sectional study. Clin Rheumatol. 2018;37(9):2331-40. DOI:10.1007/s10067-018-4113-3
9. Crowson CS, Rollefstad S, Ikdahl E, et al. A Trans-Atlantic Cardiovascular Consortium for Rheumatoid Arthritis (ATACC-RA). Ann Rheum Dis. 2018;77(1):48-54. DOI:10.1136/annrheumdis-2017-211735.5
10. D’Agostino RB Sr, Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117(6):743-53. DOI:10.1161/CIRCULATIONAHA.107.699579
11. 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(11):987-1003. DOI:10.1016/s0195-668x(03)00114-3
12. Ridker PM, Buring JE, Rifai N, Cook NR. Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score. JAMA. 2007;297(6):611-9. DOI:10.1001/jama.297.6.611
13. Goff DC Jr, 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. Circulation. 2014;129(25 Suppl.2):S49-73. DOI:10.1161/01.cir.0000437741.48606.98
14. Ridker PM, Paynter NP, Rifai N, et al. C-reactive protein and parental history improve global cardiovascular risk prediction: the Reynolds Risk Score for men. Circulation. 2008;118:2243-51.
15. Arts EE, Popa C, Den Broeder AA, et al. Performance of four current risk algorithms in predicting cardiovascular events in patients with early rheumatoid arthritis. Ann Rheum Dis. 2015;74(4):668-74. DOI:10.1136/annrheumdis-2013-204024
16. Crowson CS, Matteson EL, Roger VL, et al. Usefulness of risk scores to estimate the risk of cardiovascular disease in patients with rheumatoid arthritis. Am J Cardiol. 2012;110(3):420-4. DOI:10.1016/j.amjcard.2012.03.044
17. Agca R, Heslinga SC, Rollefstad S, et al. EULAR recommendations for cardiovascular disease risk management in patients with rheumatoid arthritis and other forms of inflammatory joint disorders: 2015/2016 update. Ann Rheum Dis. 2017;76(1):17-28.
DOI:10.1136/annrheumdis-2016-209775
18. Woodward M, Brindle P, Tunstall-Pedoe H. Adding social deprivation and family history to cardio-vascular risk assessment: the ASSIGN score from the Scottish Heart Health Extended Cohort (SHHEC). Heart. 2007;93:172-6.
19. Hippisley-Cox J, Coupland C, Vinogradova Y, et al. Derivation and validationof QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohortstudy. BMJ. 2007;335:136.
20. Hippisley-Cox J, Coupland C, Vinogradova Y, et al. Performance of the QRISK cardiovascularrisk prediction algorithm in an independent UK sample of patients from general practice: a valida-tion study. Heart. 2008;94:34-9.
21. Hippisley-Cox J, Coupland C, Brindle P. Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study. BMJ. 2017;357:j2099. DOI:10.1136/bmj.j2099
22. Solomon DH, Greenberg J, Curtis JR, et al. Derivation and internal validation of an expanded cardiovascular risk prediction score for rheumatoid arthritis: a Consortium of Rheumatology Researchers of North America Registry Study. Arthritis Rheumatol. 2015;67(8):1995-2003. DOI:10.1002/art.39195
23. Saba L, Jamthikar A, Gupta D, et al. Global perspective on carotid intima-media thickness and plaque: should the current measurement guidelines be revisited? Int Angiol. 2019;38(6):451-65. DOI:10.23736/s0392-9590.19.04267-6
24. O’Leary DH, Bots ML. Imaging of atherosclerosis: carotid intima-media thickness. Eur Heart J. 2010;31(14):1682-9. DOI:10.1093/eurheartj/ehq185
25. Khanna NN, Jamthikar AD, Gupta D, et al. Performance evaluation of 10-year ultrasound image-based stroke/cardiovascular (CV) risk calculator by comparing against ten conventional CV risk calculators: a diabetic study. Comput Biol Med. 2019;105:125-43. DOI:10.1016/j.compbiomed
26. Dessein PH, Semb AG. Could cardiovascular disease risk stratification and management in rheumatoid arthritis be enhanced? Ann Rheum Dis. 2013;72(11):1743-6. DOI:10.1136/annrheumdis-2013-203911
27. Colaco K, Ocampo V, Ayala AP, et al. Predictive Utility of Cardiovascular Risk Prediction Algorithms in Inflammatory Rheumatic Diseases: A Systematic Review. J Rheumatol. 2020;47(6):928-38. DOI:10.3899/jrheum.190261
28. Galarza-Delgado DA, Azpiri-Lopez JR, Colunga-Pedraza IJ, et al. Assessment of six cardiovascular risk calculators in Mexican mestizo patients with rheumatoid arthritis according to the EULAR 2015/2016 recommendations for cardiovascular risk management. Clin Rheumatol. 2017;36(6):1387-93. DOI:10.1007/s10067-017-3551-7
29. Cacciapaglia F, Fornaro M, Venerito V, et al. Cardiovascular risk estimation with 5 different algorithms before and after 5 years of bDMARD treatment in rheumatoid arthritis. Eur J Clin Invest. 2020:e13343. DOI:10.1111/eci.13343
30. Wahlin B, Innala L, Magnusson S, et al. Performance of the Expanded Cardiovascular Risk Prediction Score for Rheumatoid Arthritis Is Not Superior to the ACC/AHA Risk Calculator. J Rheumatol. 2019;46(2):130-7. DOI:10.3899/jrheum.171008
31. Kerekes G, Soltész P, Nurmohamed MT, et al. Validated methods for assessment of subclinical atherosclerosis in rheumatology. Nat Rev Rheumatol. 2012;8:224-34.
32. Corrales A, Vegas-Revenga N, Rueda-Gotor J, et al. Carotid plaques as predictors of cardiovascular events in patients with Rheumatoid Arthritis. Results from a 5-year-prospective follow-up study. Semin Arthritis Rheum. 2020;50(6):1333-8. DOI:10.1016/j.semarthrit.2020.03.011
33. Kobayashi H, Giles JT, Polak JF, et al. Increased prevalence of carotid artery atherosclerosis in rheumatoid arthritis is artery-specific. J Rheumatol. 2010;37(4):730-9. DOI:10.3899/jrheum.090670
34. Semb AG, Rollefstad S, Provan SA, et al. Carotid plaque characteristics and disease activity in rheumatoid arthritis. J Rheumatol. 2013;40(4):359-68.
DOI:10.3899/jrheum.120621
35. Ristić GG, Lepić T, Glišić B, et al. Rheumatoid arthritis is an independent risk factor for increased carotid intima-media thickness: impact of anti-inflammatory treatment. Rheumatology. 2010;49(6):1076-81. DOI:10.1093/rheumatology/kep456
36. Dehghan P, Rajaei A, Moeineddin R, Alizadeh AM. Prevalence of atherosclerosis in patients with inactive rheumatoid arthritis. Clin Rheumatol. 2015;34(8):1363-6. DOI:10.1007/s10067-015-2996-9
37. Hannawi SM, Hannawi H, Alokaily F, Al Salmi I. Subclinical atherosclerosis in rheumatoid arthritis patients of the Gulf Cooperated Council. Saudi Med J. 2020;41(9):1022-5. DOI:10.15537/smj.2020.9.25319
38. Dalbeni A, Giollo A, Bevilacqua M, et al. Traditional cardiovascular risk factors and residual disease activity are associated with atherosclerosis progression in rheumatoid arthritis patients. Hypertens Res. 2020;43(9):922-8. DOI:10.1038/s41440-020-0441-1
39. Del Rincon I, Polak JF, O’Leary DH, et al. Systemic inflammation and cardiovascular risk factors predict rapid progression of atherosclerosis in rheumatoid arthritis. Ann Rheum Dis. 2015;74(6):1118-23. DOI:10.1136/annrheumdis-2013-205058
40. Pope JE, Nevskaya T, Barra L, Parraga G. Carotid artery atherosclerosis in patients with active rheumatoid arthritis: predictors of plaque occurrence and progression over 24 weeks. Open Rheumatol J. 2016;10:49. DOI:10.2174/1874312901610010049
41. Jamthikar AD, Puvvula A, Gupta D, et al. Cardiovascular disease and stroke risk assessment in patients with chronic kidney disease using integration of estimated glomerular filtration rate, ultrasonic image phenotypes, and artificial intelligence: a narrative review. Int Angiol. 2020;40(2):150-64. DOI:10.23736/S0392-9590.20.04538-1
42. Ridker PM. Targeting inflammatory pathways for the treatment of cardiovascular disease. Eur Heart J. 2014;35(9):540-3. DOI:10.1093/eurheartj/eht398
43. Pauli N, Puchałowicz K, Kuligowska A, et al. between IL-6 and Echo-Parameters in Patients with Early Onset Coronary Artery Disease. Diagnostics (Basel). 2019;9(4):189. DOI:10.3390/diagnostics9040189
44. Gotsman I, Stabholz A, Planer D, et al. Serum cytokine tumor necrosis factor-alpha and interleukin-6 associated with the severity of coronary artery disease: indicators of an active inflammatory burden? Isr Med Assoc J. 2008;10(7):494-8.
45. Corrales A, Vegas-Revenga N, Atienza-Mateo B, et al. Combined use of QRISK3 and SCORE as predictors of carotid plaques in patients with rheumatoid arthritis. Rheumatology (Oxford). 2020:keaa718. DOI:10.1093/rheumatology/keaa718
46. Salaffi F, Carotti M, Di Carlo M, et al. The Expanded Risk Score in Rheumatoid Arthritis (ERS-RA): performance of a disease-specific calculator in comparison with the traditional prediction scores in the assessment of the 10-year risk of cardiovascular disease in patients with rheumatoid arthrit. Swiss Med Wkly. 2018;148:w14656.
DOI:10.4414/smw.2018.14656
2. Panafidina TA, Kondratyeva LV, Gerasimova EV, et al. Comorbidity in rheumatoid arthritis. Rheumatology Science and Practice. 2014;52(3):283-9 (in Russian)
DOI:10.14412/1995-4484-2014-283-289
3. Gordeev AV, Galushko EA, Savushkina NM, et al. Assessing the multimorbid profile (CIRS) in rheumatoid arthritis. First results. Mod Rheumatol. 2019;13(3):10-6 (in Russian) DOI:10.14412/1996-7012-2019-3-10-16
4. Balsa A, Lojo-Oliveira L, Alperi-López M, et al. Prevalence of comorbidities in rheumatoid arthritis and evaluation of their monitoring in clinical practice: the spanish cohort of the COMORA study. Reumatol Clin. 2019;15(2):102-8. DOI:10.1016/j.reuma.2017.06.002
5. Avina-Zubieta JA, Thomas J, Sadatsafavi M, et al. Risk of incident cardiovascular events in patients with rheumatoid arthritis: a meta-analysis of observational studies. Ann Rheum Dis. 2012;71(9):1524-9. DOI:10.1136/annrheumdis-2011-200726
6. Wang H, Li X, Gong G. Cardiovascular outcomes in patients with co-existing coronary artery disease and rheumatoid arthritis: A systematic review and meta-analysis. Medicine (Baltimore). 2020;99(14):e19658. DOI:10.1097/MD.0000000000019658
7. Dadoun S, Zeboulon-Ktorza N, Combescure C, et al. Mortality in rheumatoid arthritis over the last fifty years: systematic review and meta-analysis. Joint Bone Spine. 2013;80(1):29-33. DOI:10.1016/j.jbspin.2012.02.005
8. Pappas DA, Nyberg F, Kremer JM, et al. Prevalence of cardiovascular disease and major risk factors in patients with rheumatoid arthritis:
a multinational cross-sectional study. Clin Rheumatol. 2018;37(9):2331-40. DOI:10.1007/s10067-018-4113-3
9. Crowson CS, Rollefstad S, Ikdahl E, et al. A Trans-Atlantic Cardiovascular Consortium for Rheumatoid Arthritis (ATACC-RA). Ann Rheum Dis. 2018;77(1):48-54. DOI:10.1136/annrheumdis-2017-211735.5
10. D’Agostino RB Sr, Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117(6):743-53. DOI:10.1161/CIRCULATIONAHA.107.699579
11. 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(11):987-1003. DOI:10.1016/s0195-668x(03)00114-3
12. Ridker PM, Buring JE, Rifai N, Cook NR. Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score. JAMA. 2007;297(6):611-9. DOI:10.1001/jama.297.6.611
13. Goff DC Jr, 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. Circulation. 2014;129(25 Suppl.2):S49-73. DOI:10.1161/01.cir.0000437741.48606.98
14. Ridker PM, Paynter NP, Rifai N, et al. C-reactive protein and parental history improve global cardiovascular risk prediction: the Reynolds Risk Score for men. Circulation. 2008;118:2243-51.
15. Arts EE, Popa C, Den Broeder AA, et al. Performance of four current risk algorithms in predicting cardiovascular events in patients with early rheumatoid arthritis. Ann Rheum Dis. 2015;74(4):668-74. DOI:10.1136/annrheumdis-2013-204024
16. Crowson CS, Matteson EL, Roger VL, et al. Usefulness of risk scores to estimate the risk of cardiovascular disease in patients with rheumatoid arthritis. Am J Cardiol. 2012;110(3):420-4. DOI:10.1016/j.amjcard.2012.03.044
17. Agca R, Heslinga SC, Rollefstad S, et al. EULAR recommendations for cardiovascular disease risk management in patients with rheumatoid arthritis and other forms of inflammatory joint disorders: 2015/2016 update. Ann Rheum Dis. 2017;76(1):17-28.
DOI:10.1136/annrheumdis-2016-209775
18. Woodward M, Brindle P, Tunstall-Pedoe H. Adding social deprivation and family history to cardio-vascular risk assessment: the ASSIGN score from the Scottish Heart Health Extended Cohort (SHHEC). Heart. 2007;93:172-6.
19. Hippisley-Cox J, Coupland C, Vinogradova Y, et al. Derivation and validationof QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohortstudy. BMJ. 2007;335:136.
20. Hippisley-Cox J, Coupland C, Vinogradova Y, et al. Performance of the QRISK cardiovascularrisk prediction algorithm in an independent UK sample of patients from general practice: a valida-tion study. Heart. 2008;94:34-9.
21. Hippisley-Cox J, Coupland C, Brindle P. Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study. BMJ. 2017;357:j2099. DOI:10.1136/bmj.j2099
22. Solomon DH, Greenberg J, Curtis JR, et al. Derivation and internal validation of an expanded cardiovascular risk prediction score for rheumatoid arthritis: a Consortium of Rheumatology Researchers of North America Registry Study. Arthritis Rheumatol. 2015;67(8):1995-2003. DOI:10.1002/art.39195
23. Saba L, Jamthikar A, Gupta D, et al. Global perspective on carotid intima-media thickness and plaque: should the current measurement guidelines be revisited? Int Angiol. 2019;38(6):451-65. DOI:10.23736/s0392-9590.19.04267-6
24. O’Leary DH, Bots ML. Imaging of atherosclerosis: carotid intima-media thickness. Eur Heart J. 2010;31(14):1682-9. DOI:10.1093/eurheartj/ehq185
25. Khanna NN, Jamthikar AD, Gupta D, et al. Performance evaluation of 10-year ultrasound image-based stroke/cardiovascular (CV) risk calculator by comparing against ten conventional CV risk calculators: a diabetic study. Comput Biol Med. 2019;105:125-43. DOI:10.1016/j.compbiomed
26. Dessein PH, Semb AG. Could cardiovascular disease risk stratification and management in rheumatoid arthritis be enhanced? Ann Rheum Dis. 2013;72(11):1743-6. DOI:10.1136/annrheumdis-2013-203911
27. Colaco K, Ocampo V, Ayala AP, et al. Predictive Utility of Cardiovascular Risk Prediction Algorithms in Inflammatory Rheumatic Diseases:
A Systematic Review. J Rheumatol. 2020;47(6):928-38. DOI:10.3899/jrheum.190261
28. Galarza-Delgado DA, Azpiri-Lopez JR, Colunga-Pedraza IJ, et al. Assessment of six cardiovascular risk calculators in Mexican mestizo patients with rheumatoid arthritis according to the EULAR 2015/2016 recommendations for cardiovascular risk management. Clin Rheumatol. 2017;36(6):1387-93. DOI:10.1007/s10067-017-3551-7
29. Cacciapaglia F, Fornaro M, Venerito V, et al. Cardiovascular risk estimation with 5 different algorithms before and after 5 years of bDMARD treatment in rheumatoid arthritis. Eur J Clin Invest. 2020:e13343. DOI:10.1111/eci.13343
30. Wahlin B, Innala L, Magnusson S, et al. Performance of the Expanded Cardiovascular Risk Prediction Score for Rheumatoid Arthritis Is Not Superior to the ACC/AHA Risk Calculator. J Rheumatol. 2019;46(2):130-7. DOI:10.3899/jrheum.171008
31. Kerekes G, Soltész P, Nurmohamed MT, et al. Validated methods for assessment of subclinical atherosclerosis in rheumatology. Nat Rev Rheumatol. 2012;8:224-34.
32. Corrales A, Vegas-Revenga N, Rueda-Gotor J, et al. Carotid plaques as predictors of cardiovascular events in patients with Rheumatoid Arthritis. Results from a 5-year-prospective follow-up study. Semin Arthritis Rheum. 2020;50(6):1333-8. DOI:10.1016/j.semarthrit.2020.03.011
33. Kobayashi H, Giles JT, Polak JF, et al. Increased prevalence of carotid artery atherosclerosis in rheumatoid arthritis is artery-specific. J Rheumatol. 2010;37(4):730-9. DOI:10.3899/jrheum.090670
34. Semb AG, Rollefstad S, Provan SA, et al. Carotid plaque characteristics and disease activity in rheumatoid arthritis. J Rheumatol. 2013;40(4):359-68.
DOI:10.3899/jrheum.120621
35. Ristić GG, Lepić T, Glišić B, et al. Rheumatoid arthritis is an independent risk factor for increased carotid intima-media thickness: impact of anti-inflammatory treatment. Rheumatology. 2010;49(6):1076-81. DOI:10.1093/rheumatology/kep456
36. Dehghan P, Rajaei A, Moeineddin R, Alizadeh AM. Prevalence of atherosclerosis in patients with inactive rheumatoid arthritis. Clin Rheumatol. 2015;34(8):1363-6. DOI:10.1007/s10067-015-2996-9
37. Hannawi SM, Hannawi H, Alokaily F, Al Salmi I. Subclinical atherosclerosis in rheumatoid arthritis patients of the Gulf Cooperated Council. Saudi Med J. 2020;41(9):1022-5. DOI:10.15537/smj.2020.9.25319
38. Dalbeni A, Giollo A, Bevilacqua M, et al. Traditional cardiovascular risk factors and residual disease activity are associated with atherosclerosis progression in rheumatoid arthritis patients. Hypertens Res. 2020;43(9):922-8. DOI:10.1038/s41440-020-0441-1
39. Del Rincon I, Polak JF, O’Leary DH, et al. Systemic inflammation and cardiovascular risk factors predict rapid progression of atherosclerosis in rheumatoid arthritis. Ann Rheum Dis. 2015;74(6):1118-23. DOI:10.1136/annrheumdis-2013-205058
40. Pope JE, Nevskaya T, Barra L, Parraga G. Carotid artery atherosclerosis in patients with active rheumatoid arthritis: predictors of plaque occurrence and progression over 24 weeks. Open Rheumatol J. 2016;10:49. DOI:10.2174/1874312901610010049
41. Jamthikar AD, Puvvula A, Gupta D, et al. Cardiovascular disease and stroke risk assessment in patients with chronic kidney disease using integration of estimated glomerular filtration rate, ultrasonic image phenotypes, and artificial intelligence: a narrative review. Int Angiol. 2020;40(2):150-64. DOI:10.23736/S0392-9590.20.04538-1
42. Ridker PM. Targeting inflammatory pathways for the treatment of cardiovascular disease. Eur Heart J. 2014;35(9):540-3. DOI:10.1093/eurheartj/eht398
43. Pauli N, Puchałowicz K, Kuligowska A, et al. between IL-6 and Echo-Parameters in Patients with Early Onset Coronary Artery Disease. Diagnostics (Basel). 2019;9(4):189. DOI:10.3390/diagnostics9040189
44. Gotsman I, Stabholz A, Planer D, et al. Serum cytokine tumor necrosis factor-alpha and interleukin-6 associated with the severity of coronary artery disease: indicators of an active inflammatory burden? Isr Med Assoc J. 2008;10(7):494-8.
45. Corrales A, Vegas-Revenga N, Atienza-Mateo B, et al. Combined use of QRISK3 and SCORE as predictors of carotid plaques in patients with rheumatoid arthritis. Rheumatology (Oxford). 2020:keaa718. DOI:10.1093/rheumatology/keaa718
46. Salaffi F, Carotti M, Di Carlo M, et al. The Expanded Risk Score in Rheumatoid Arthritis (ERS-RA): performance of a disease-specific calculator in comparison with the traditional prediction scores in the assessment of the 10-year risk of cardiovascular disease in patients with rheumatoid arthrit. Swiss Med Wkly. 2018;148:w14656.
DOI:10.4414/smw.2018.14656
________________________________________________
2. Panafidina TA, Kondratyeva LV, Gerasimova EV, et al. Comorbidity in rheumatoid arthritis. Rheumatology Science and Practice. 2014;52(3):283-9 (in Russian)
DOI:10.14412/1995-4484-2014-283-289
3. Gordeev AV, Galushko EA, Savushkina NM, et al. Assessing the multimorbid profile (CIRS) in rheumatoid arthritis. First results. Mod Rheumatol. 2019;13(3):10-6 (in Russian) DOI:10.14412/1996-7012-2019-3-10-16
4. Balsa A, Lojo-Oliveira L, Alperi-López M, et al. Prevalence of comorbidities in rheumatoid arthritis and evaluation of their monitoring in clinical practice: the spanish cohort of the COMORA study. Reumatol Clin. 2019;15(2):102-8.
DOI:10.1016/j.reuma.2017.06.002
5. Avina-Zubieta JA, Thomas J, Sadatsafavi M, et al. Risk of incident cardiovascular events in patients with rheumatoid arthritis: a meta-analysis of observational studies. Ann Rheum Dis. 2012;71(9):1524-9. DOI:10.1136/annrheumdis-2011-200726
6. Wang H, Li X, Gong G. Cardiovascular outcomes in patients with co-existing coronary artery disease and rheumatoid arthritis: A systematic review and meta-analysis. Medicine (Baltimore). 2020;99(14):e19658. DOI:10.1097/MD.0000000000019658
7. Dadoun S, Zeboulon-Ktorza N, Combescure C, et al. Mortality in rheumatoid arthritis over the last fifty years: systematic review and meta-analysis. Joint Bone Spine. 2013;80(1):29-33. DOI:10.1016/j.jbspin.2012.02.005
8. Pappas DA, Nyberg F, Kremer JM, et al. Prevalence of cardiovascular disease and major risk factors in patients with rheumatoid arthritis:
a multinational cross-sectional study. Clin Rheumatol. 2018;37(9):2331-40. DOI:10.1007/s10067-018-4113-3
9. Crowson CS, Rollefstad S, Ikdahl E, et al. A Trans-Atlantic Cardiovascular Consortium for Rheumatoid Arthritis (ATACC-RA). Ann Rheum Dis. 2018;77(1):48-54. DOI:10.1136/annrheumdis-2017-211735.5
10. D’Agostino RB Sr, Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117(6):743-53. DOI:10.1161/CIRCULATIONAHA.107.699579
11. 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(11):987-1003. DOI:10.1016/s0195-668x(03)00114-3
12. Ridker PM, Buring JE, Rifai N, Cook NR. Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score. JAMA. 2007;297(6):611-9. DOI:10.1001/jama.297.6.611
13. Goff DC Jr, 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. Circulation. 2014;129(25 Suppl.2):S49-73. DOI:10.1161/01.cir.0000437741.48606.98
14. Ridker PM, Paynter NP, Rifai N, et al. C-reactive protein and parental history improve global cardiovascular risk prediction: the Reynolds Risk Score for men. Circulation. 2008;118:2243-51.
15. Arts EE, Popa C, Den Broeder AA, et al. Performance of four current risk algorithms in predicting cardiovascular events in patients with early rheumatoid arthritis. Ann Rheum Dis. 2015;74(4):668-74. DOI:10.1136/annrheumdis-2013-204024
16. Crowson CS, Matteson EL, Roger VL, et al. Usefulness of risk scores to estimate the risk of cardiovascular disease in patients with rheumatoid arthritis. Am J Cardiol. 2012;110(3):420-4. DOI:10.1016/j.amjcard.2012.03.044
17. Agca R, Heslinga SC, Rollefstad S, et al. EULAR recommendations for cardiovascular disease risk management in patients with rheumatoid arthritis and other forms of inflammatory joint disorders: 2015/2016 update. Ann Rheum Dis. 2017;76(1):17-28.
DOI:10.1136/annrheumdis-2016-209775
18. Woodward M, Brindle P, Tunstall-Pedoe H. Adding social deprivation and family history to cardio-vascular risk assessment: the ASSIGN score from the Scottish Heart Health Extended Cohort (SHHEC). Heart. 2007;93:172-6.
19. Hippisley-Cox J, Coupland C, Vinogradova Y, et al. Derivation and validationof QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohortstudy. BMJ. 2007;335:136.
20. Hippisley-Cox J, Coupland C, Vinogradova Y, et al. Performance of the QRISK cardiovascularrisk prediction algorithm in an independent UK sample of patients from general practice: a valida-tion study. Heart. 2008;94:34-9.
21. Hippisley-Cox J, Coupland C, Brindle P. Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study. BMJ. 2017;357:j2099. DOI:10.1136/bmj.j2099
22. Solomon DH, Greenberg J, Curtis JR, et al. Derivation and internal validation of an expanded cardiovascular risk prediction score for rheumatoid arthritis: a Consortium of Rheumatology Researchers of North America Registry Study. Arthritis Rheumatol. 2015;67(8):1995-2003. DOI:10.1002/art.39195
23. Saba L, Jamthikar A, Gupta D, et al. Global perspective on carotid intima-media thickness and plaque: should the current measurement guidelines be revisited? Int Angiol. 2019;38(6):451-65. DOI:10.23736/s0392-9590.19.04267-6
24. O’Leary DH, Bots ML. Imaging of atherosclerosis: carotid intima-media thickness. Eur Heart J. 2010;31(14):1682-9. DOI:10.1093/eurheartj/ehq185
25. Khanna NN, Jamthikar AD, Gupta D, et al. Performance evaluation of 10-year ultrasound image-based stroke/cardiovascular (CV) risk calculator by comparing against ten conventional CV risk calculators: a diabetic study. Comput Biol Med. 2019;105:125-43. DOI:10.1016/j.compbiomed
26. Dessein PH, Semb AG. Could cardiovascular disease risk stratification and management in rheumatoid arthritis be enhanced? Ann Rheum Dis. 2013;72(11):1743-6. DOI:10.1136/annrheumdis-2013-203911
27. Colaco K, Ocampo V, Ayala AP, et al. Predictive Utility of Cardiovascular Risk Prediction Algorithms in Inflammatory Rheumatic Diseases: A Systematic Review. J Rheumatol. 2020;47(6):928-38. DOI:10.3899/jrheum.190261
28. Galarza-Delgado DA, Azpiri-Lopez JR, Colunga-Pedraza IJ, et al. Assessment of six cardiovascular risk calculators in Mexican mestizo patients with rheumatoid arthritis according to the EULAR 2015/2016 recommendations for cardiovascular risk management. Clin Rheumatol. 2017;36(6):1387-93. DOI:10.1007/s10067-017-3551-7
29. Cacciapaglia F, Fornaro M, Venerito V, et al. Cardiovascular risk estimation with 5 different algorithms before and after 5 years of bDMARD treatment in rheumatoid arthritis. Eur J Clin Invest. 2020:e13343. DOI:10.1111/eci.13343
30. Wahlin B, Innala L, Magnusson S, et al. Performance of the Expanded Cardiovascular Risk Prediction Score for Rheumatoid Arthritis Is Not Superior to the ACC/AHA Risk Calculator. J Rheumatol. 2019;46(2):130-7. DOI:10.3899/jrheum.171008
31. Kerekes G, Soltész P, Nurmohamed MT, et al. Validated methods for assessment of subclinical atherosclerosis in rheumatology. Nat Rev Rheumatol. 2012;8:224-34.
32. Corrales A, Vegas-Revenga N, Rueda-Gotor J, et al. Carotid plaques as predictors of cardiovascular events in patients with Rheumatoid Arthritis. Results from a 5-year-prospective follow-up study. Semin Arthritis Rheum. 2020;50(6):1333-8. DOI:10.1016/j.semarthrit.2020.03.011
33. Kobayashi H, Giles JT, Polak JF, et al. Increased prevalence of carotid artery atherosclerosis in rheumatoid arthritis is artery-specific. J Rheumatol. 2010;37(4):730-9. DOI:10.3899/jrheum.090670
34. Semb AG, Rollefstad S, Provan SA, et al. Carotid plaque characteristics and disease activity in rheumatoid arthritis. J Rheumatol. 2013;40(4):359-68.
DOI:10.3899/jrheum.120621
35. Ristić GG, Lepić T, Glišić B, et al. Rheumatoid arthritis is an independent risk factor for increased carotid intima-media thickness: impact of anti-inflammatory treatment. Rheumatology. 2010;49(6):1076-81. DOI:10.1093/rheumatology/kep456
36. Dehghan P, Rajaei A, Moeineddin R, Alizadeh AM. Prevalence of atherosclerosis in patients with inactive rheumatoid arthritis. Clin Rheumatol. 2015;34(8):1363-6. DOI:10.1007/s10067-015-2996-9
37. Hannawi SM, Hannawi H, Alokaily F, Al Salmi I. Subclinical atherosclerosis in rheumatoid arthritis patients of the Gulf Cooperated Council. Saudi Med J. 2020;41(9):1022-5. DOI:10.15537/smj.2020.9.25319
38. Dalbeni A, Giollo A, Bevilacqua M, et al. Traditional cardiovascular risk factors and residual disease activity are associated with atherosclerosis progression in rheumatoid arthritis patients. Hypertens Res. 2020;43(9):922-8. DOI:10.1038/s41440-020-0441-1
39. Del Rincon I, Polak JF, O’Leary DH, et al. Systemic inflammation and cardiovascular risk factors predict rapid progression of atherosclerosis in rheumatoid arthritis. Ann Rheum Dis. 2015;74(6):1118-23. DOI:10.1136/annrheumdis-2013-205058
40. Pope JE, Nevskaya T, Barra L, Parraga G. Carotid artery atherosclerosis in patients with active rheumatoid arthritis: predictors of plaque occurrence and progression over 24 weeks. Open Rheumatol J. 2016;10:49. DOI:10.2174/1874312901610010049
41. Jamthikar AD, Puvvula A, Gupta D, et al. Cardiovascular disease and stroke risk assessment in patients with chronic kidney disease using integration of estimated glomerular filtration rate, ultrasonic image phenotypes, and artificial intelligence: a narrative review. Int Angiol. 2020;40(2):150-64. DOI:10.23736/S0392-9590.20.04538-1
42. Ridker PM. Targeting inflammatory pathways for the treatment of cardiovascular disease. Eur Heart J. 2014;35(9):540-3. DOI:10.1093/eurheartj/eht398
43. Pauli N, Puchałowicz K, Kuligowska A, et al. between IL-6 and Echo-Parameters in Patients with Early Onset Coronary Artery Disease. Diagnostics (Basel). 2019;9(4):189. DOI:10.3390/diagnostics9040189
44. Gotsman I, Stabholz A, Planer D, et al. Serum cytokine tumor necrosis factor-alpha and interleukin-6 associated with the severity of coronary artery disease: indicators of an active inflammatory burden? Isr Med Assoc J. 2008;10(7):494-8.
45. Corrales A, Vegas-Revenga N, Atienza-Mateo B, et al. Combined use of QRISK3 and SCORE as predictors of carotid plaques in patients with rheumatoid arthritis. Rheumatology (Oxford). 2020:keaa718. DOI:10.1093/rheumatology/keaa718
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DOI:10.4414/smw.2018.14656
Авторы
Е.В. Герасимова*1, Т.В. Попкова1, Д.А. Герасимова2, С.И. Глухова1, Е.Л. Насонов1,2, А.М. Лила1,3
1 ФГБНУ «Научно-исследовательский институт ревматологии им. В.А. Насоновой», Москва, Россия;
2 ФГАОУ ВО «Первый Московский государственный медицинский университет им. И.М. Сеченова» Минздрава России (Сеченовский Университет), Москва, Россия;
3 ФГБОУ ДПО «Российская медицинская академия непрерывного профессионального образования» Минздрава России, Москва, Россия
*gerasimovaev@list.ru
1 Nasonova Research Institute of Rheumatology, Moscow, Russia;
2 Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia;
3 Russian Medical Academy of Continuous Professional Education, Moscow, Russia
*gerasimovaev@list.ru
1 ФГБНУ «Научно-исследовательский институт ревматологии им. В.А. Насоновой», Москва, Россия;
2 ФГАОУ ВО «Первый Московский государственный медицинский университет им. И.М. Сеченова» Минздрава России (Сеченовский Университет), Москва, Россия;
3 ФГБОУ ДПО «Российская медицинская академия непрерывного профессионального образования» Минздрава России, Москва, Россия
*gerasimovaev@list.ru
________________________________________________
1 Nasonova Research Institute of Rheumatology, Moscow, Russia;
2 Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia;
3 Russian Medical Academy of Continuous Professional Education, Moscow, Russia
*gerasimovaev@list.ru
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