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Rheumatoid Arthritis: Atherosclerosis Imaging and Cardiovascular Risk Assessment Using Machine and Deep Learning–Based Tissue Characterization

Abstract

Purpose of the Review

Rheumatoid arthritis (RA) is a chronic, autoimmune disease which may result in a higher risk of cardiovascular (CV) events and stroke. Tissue characterization and risk stratification of patients with rheumatoid arthritis are a challenging problem. Risk stratification of RA patients using traditional risk factor–based calculators either underestimates or overestimates the CV risk. Advancements in medical imaging have facilitated early and accurate CV risk stratification compared to conventional cardiovascular risk calculators.

Recent Finding

In recent years, a link between carotid atherosclerosis and rheumatoid arthritis has been widely discussed by multiple studies. Imaging the carotid artery using 2-D ultrasound is a noninvasive, economic, and efficient imaging approach that provides an atherosclerotic plaque tissue–specific image. Such images can help to morphologically characterize the plaque type and accurately measure vital phenotypes such as media wall thickness and wall variability. Intelligence-based paradigms such as machine learning– and deep learning–based techniques not only automate the risk characterization process but also provide an accurate CV risk stratification for better management of RA patients.

Summary

This review provides a brief understanding of the pathogenesis of RA and its association with carotid atherosclerosis imaged using the B-mode ultrasound technique. Lacunas in traditional risk scores and the role of machine learning–based tissue characterization algorithms are discussed and could facilitate cardiovascular risk assessment in RA patients. The key takeaway points from this review are the following: (i) inflammation is a common link between RA and atherosclerotic plaque buildup, (ii) carotid ultrasound is a better choice to characterize the atherosclerotic plaque tissues in RA patients, and (iii) intelligence-based paradigms are useful for accurate tissue characterization and risk stratification of RA patients.

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References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. 1.

    Symmons D, Mathers C, Pfleger B: The global burden of rheumatoid arthritis in the year 2000. World Health Organization 2006.

  2. 2.

    van Vollenhoven RF. Sex differences in rheumatoid arthritis: more than meets the eye. BMC Med. 2009;7(1):12.

    PubMed  PubMed Central  Google Scholar 

  3. 3.

    Rudan I, Sidhu S, Papana A, et al. Prevalence of rheumatoid arthritis in low-and middle-income countries: a systematic review and analysis. J Global Health 2015, 5(1).

  4. 4.

    Hunter TM, Boytsov NN, Zhang X, Schroeder K, Michaud K, Araujo AB. Prevalence of rheumatoid arthritis in the United States adult population in healthcare claims databases, 2004–2014. Rheumatol Int. 2017;37(9):1551–7.

    PubMed  Google Scholar 

  5. 5.

    Padyukov L, Silva C, Stolt P, Alfredsson L, Klareskog L. A gene-environment interaction between smoking and shared epitope genes in HLA-DR provides a high risk of seropositive rheumatoid arthritis. Arthritis Rheum. 2004;50(10):3085–92.

    CAS  PubMed  Google Scholar 

  6. 6.

    Klareskog L, Padyukov L, Lorentzen J, Alfredsson L. Mechanisms of disease: genetic susceptibility and environmental triggers in the development of rheumatoid arthritis. Nat Rev Rheumatol. 2006;2(8):425–33.

    CAS  Google Scholar 

  7. 7.

    Klareskog L, Stolt P, Lundberg K, Källberg H, Bengtsson C, Grunewald J, et al. A new model for an etiology of rheumatoid arthritis: smoking may trigger HLA-DR (shared epitope)-restricted immune reactions to autoantigens modified by citrullination. Arthritis Rheum. 2006;54(1):38–46.

    CAS  PubMed  Google Scholar 

  8. 8.

    Wagner CA, Sokolove J, Lahey LJ, Bengtsson C, Saevarsdottir S, Alfredsson L, et al. Identification of anticitrullinated protein antibody reactivities in a subset of anti-CCP-negative rheumatoid arthritis: association with cigarette smoking and HLA-DRB1 ‘shared epitope’ alleles. Ann Rheum Dis. 2015;74(3):579–86.

    CAS  PubMed  Google Scholar 

  9. 9.

    Crowson CS, Matteson EL, Roger VL, Therneau TM, Gabriel SE. Usefulness of risk scores to estimate the risk of cardiovascular disease in patients with rheumatoid arthritis. Am J Cardiol. 2012;110(3):420–4.

    PubMed  PubMed Central  Google Scholar 

  10. 10.

    Bonek K, Głuszko P. Cardiovascular risk assessment in rheumatoid arthritis—controversies and the new approach. Reumatologia. 2016;54(3):128–35.

    PubMed  PubMed Central  Google Scholar 

  11. 11.

    Solomon D, Greenberg J, Curtis J, 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 Rheum. 2015;67(8):1995–2003.

    CAS  Google Scholar 

  12. 12.

    Peters MJ, Symmons D, McCarey D, et al. EULAR evidence-based recommendations for cardiovascular risk management in patients with rheumatoid arthritis and other forms of inflammatory arthritis. Ann Rheum Dis. 2010;69(2):325–31.

    CAS  PubMed  Google Scholar 

  13. 13.

    Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, Minhas R, Sheikh A, et al. Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2. BMJ. 2008;336(7659):1475–82.

    PubMed  PubMed Central  Google Scholar 

  14. 14.

    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.

    CAS  PubMed  Google Scholar 

  15. 15.

    Crowson CS, Rollefstad S, Ikdahl E, Kitas GD, van Riel P, Gabriel SE, et al. Impact of risk factors associated with cardiovascular outcomes in patients with rheumatoid arthritis. Ann Rheum Dis. 2018;77(1):48–54.

    CAS  PubMed  Google Scholar 

  16. 16.

    Arts E, Popa C, Den Broeder A, et al: Performance of four current risk algorithms in predicting cardiovascular events in patients with early rheumatoid arthritis. Ann Rheum Dis 2014:annrheumdis-2013-204024.

  17. 17.

    •• Fent GJ, Greenwood JP, Plein S, Buch MH. The role of non-invasive cardiovascular imaging in the assessment of cardiovascular risk in rheumatoid arthritis: where we are and where we need to be. Ann Rheum Dis. 2017;76(7):1169 It is an important article that provides the role of noninvasive imaging modalities for cardiovascular risk stratification of rheumatoid arthritis patients.

    PubMed  Google Scholar 

  18. 18.

    Maintz D, Ozgun M, Hoffmeier A, et al. Selective coronary artery plaque visualization and differentiation by contrast-enhanced inversion prepared MRI. Eur Heart J. 2006;27(14):1732–6.

    PubMed  Google Scholar 

  19. 19.

    Saremi F, Achenbach S. Coronary plaque characterization using CT. Am J Roentgenol. 2015;204(3):W249–60.

    Google Scholar 

  20. 20.

    Glaudemans AW, de Vries EF, Galli F, Dierckx RA, Slart RH, Signore A. The use of F-FDG-PET/CT for diagnosis and treatment monitoring of inflammatory and infectious diseases. Clin Dev Immunol. 2013;2013:1–14.

    Google Scholar 

  21. 21.

    Furer V, Fayad ZA, Mani V, Calcagno C, Farkouh ME, Greenberg JD: Noninvasive cardiovascular imaging in rheumatoid arthritis: current modalities and the emerging role of magnetic resonance and positron emission tomography imaging. In: Semin Arthritis Rheumatism: 2012: Elsevier; 2012: 676–688.

  22. 22.

    Bezerra HG, Costa MA, Guagliumi G, Rollins AM, Simon DI. Intracoronary optical coherence tomography: a comprehensive review: clinical and research applications. J Am Coll Cardiol Intv. 2009;2(11):1035–46.

    Google Scholar 

  23. 23.

    Corrales A, González-Juanatey C, Peiró ME, Blanco R, Llorca J, González-Gay MA: Carotid ultrasound is useful for the cardiovascular risk stratification of patients with rheumatoid arthritis: results of a population-based study. Ann Rheum Dis 2013:annrheumdis-2012-203101.

  24. 24.

    González-Gay MA, González-Juanatey C, Llorca J. Carotid ultrasound in the cardiovascular risk stratification of patients with rheumatoid arthritis: when and for whom? Ann Rheum Dis 2012:annrheumdis-2011-201209.

  25. 25.

    Saba L, Banchhor SK, Araki T, Viskovic K, Londhe ND, Laird JR, et al. Intra- and inter-operator reproducibility of automated cloud-based carotid lumen diameter ultrasound measurement. Indian Heart J. 2018;70:649–64.

    PubMed  Google Scholar 

  26. 26.

    •• Libby P. Role of inflammation in atherosclerosis associated with rheumatoid arthritis. Am J Med. 2008;121(10):S21–31 Inflammation is a common link between rheumatoid arthritis and atherosclerosis. This concept has been elaborated in this article.

    CAS  PubMed  Google Scholar 

  27. 27.

    • Skeoch S, Bruce IN. Atherosclerosis in rheumatoid arthritis: is it all about inflammation? Nat Rev Rheumatol. 2015;11(7):390 Patients with rheumatoid arthritis are at elevated risk of rupture of atherosclerotic plaque. This concept has been disccused in this article.

    CAS  PubMed  Google Scholar 

  28. 28.

    Sattar N, McCarey DW, Capell H, McInnes IB. Explaining how “high-grade” systemic inflammation accelerates vascular risk in rheumatoid arthritis. Circulation. 2003;108(24):2957–63.

    PubMed  Google Scholar 

  29. 29.

    Libby P, Ridker PM, Maseri A. Inflammation and atherosclerosis. Circulation. 2002;105(9):1135–43.

    CAS  PubMed  Google Scholar 

  30. 30.

    Libby P. Vascular biology of atherosclerosis: overview and state of the art. Am J Cardiol. 2003;91(3):3–6.

    Google Scholar 

  31. 31.

    Libby P, Clinton SK. The role of macrophages in atherogenesis. Curr Opin Lipidol. 1993;4(5):355–63.

    CAS  Google Scholar 

  32. 32.

    Doran AC, Meller N, McNamara CA. Role of smooth muscle cells in the initiation and early progression of atherosclerosis. Arterioscler Thromb Vasc Biol. 2008;28(5):812–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Hansson GK, Libby P, Tabas I. Inflammation and plaque vulnerability. J Intern Med. 2015;278(5):483–93.

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Virmani R, Burke AP, Farb A, Kolodgie FD. Pathology of the vulnerable plaque. J Am Coll Cardiol. 2006;47(8 Supplement):C13–8.

    CAS  PubMed  Google Scholar 

  35. 35.

    Vuilleumier N, Bratt J, Alizadeh R, Jogestrand T, Hafström I, Frostegård J. Anti-apoA-1 IgG and oxidized LDL are raised in rheumatoid arthritis (RA): potential associations with cardiovascular disease and RA disease activity. Scand J Rheumatol. 2010;39(6):447–53.

    CAS  PubMed  Google Scholar 

  36. 36.

    Maziere C, Auclair M, Maziere J-C. Tumor necrosis factor enhances low density lipoprotein oxidative modification by monocytes and endothelial cells. FEBS Lett. 1994;338(1):43–6.

    CAS  PubMed  Google Scholar 

  37. 37.

    Nakamura T. Amyloid A amyloidosis secondary to rheumatoid arthritis: pathophysiology and treatments. Clin Exp Rheumatol. 2011;29(5):850–7.

    PubMed  Google Scholar 

  38. 38.

    Targońska-Stępniak B, Majdan M. Serum amyloid A as a marker of persistent inflammation and an indicator of cardiovascular and renal involvement in patients with rheumatoid arthritis. Mediat Inflamm. 2014;2014:1–7.

    Google Scholar 

  39. 39.

    • Gonzalez A, Kremers HM, Crowson CS, et al. Do cardiovascular risk factors confer the same risk for cardiovascular outcomes in rheumatoid arthritis patients as in non-rheumatoid arthritis patients? Ann Rheum Dis. 2008;67(1):64–9 It is an important article demonstrating that the prevention of cardiovascular diseases and mortalities by controlling the traditional risk factors may not be beneficial in patients with rheumatoid arthritis compared to the general population.

    CAS  PubMed  Google Scholar 

  40. 40.

    Semb A, Kvien T, Aastveit A, et al: Lipids, myocardial infarction and ischaemic stroke in patients with rheumatoid arthritis in the Apolipoprotein-related Mortality RISk (AMORIS) Study. Ann Rheum Dis 2010:annrheumdis126128.

  41. 41.

    Escalante A, Haas RW, del Rincón I. Paradoxical effect of body mass index on survival in rheumatoid arthritis: role of comorbidity and systemic inflammation. Arch Intern Med. 2005;165(14):1624–9.

    PubMed  Google Scholar 

  42. 42.

    Kremers HM, Nicola PJ, Crowson CS, Ballman KV, Gabriel SE. Prognostic importance of low body mass index in relation to cardiovascular mortality in rheumatoid arthritis. Arthritis Rheum. 2004;50(11):3450–7.

    PubMed  Google Scholar 

  43. 43.

    Roubenoff R, Roubenoff RA, Cannon JG, Kehayias JJ, Zhuang H, Dawson-Hughes B, et al. Rheumatoid cachexia: cytokine-driven hypermetabolism accompanying reduced body cell mass in chronic inflammation. J Clin Investig. 1994;93(6):2379–86.

    CAS  PubMed  Google Scholar 

  44. 44.

    Sokka T, Häkkinen A, Kautiainen H, Maillefert JF, Toloza S, MØrk hansen T, et al. Physical inactivity in patients with rheumatoid arthritis: data from twenty-one countries in a cross-sectional, international study. Arthritis Care Res. 2008;59(1):42–50.

    Google Scholar 

  45. 45.

    Boo S, Oh H, Froelicher ES, Suh C-H. Knowledge and perception of cardiovascular disease risk among patients with rheumatoid arthritis. PLoS One. 2017;12(4):e0176291.

    PubMed  PubMed Central  Google Scholar 

  46. 46.

    Kitas GD, Gabriel SE. Cardiovascular disease in rheumatoid arthritis: state of the art and future perspectives. Ann Rheum Dis. 2011;70(1):8–14.

    PubMed  Google Scholar 

  47. 47.

    Summers GD, Metsios GS, Stavropoulos-Kalinoglou A, Kitas GD. Rheumatoid cachexia and cardiovascular disease. Nat Rev Rheumatol. 2010;6(8):445–51.

    PubMed  Google Scholar 

  48. 48.

    Qin B, Yang M, Fu H, Ma N, Wei T, Tang Q, et al. Body mass index and the risk of rheumatoid arthritis: a systematic review and dose-response meta-analysis. Arthritis Res Therapy. 2015;17(1):86.

    Google Scholar 

  49. 49.

    Kurihara O, Takano M, Inami T, Murakami D, Munakata R, Ohba T, et al. Impact of low body mass index on coronary atherosclerosis: multivessel angioscopic study. Can J Cardiol. 2015;31(10):S11–2.

    Google Scholar 

  50. 50.

    Stavropoulos-Kalinoglou A, Metsios GS, Koutedakis Y, Kitas GD. Obesity in rheumatoid arthritis. Rheumatology. 2010;50(3):450–62.

    PubMed  Google Scholar 

  51. 51.

    Metsios GS, Stavropoulos-Kalinoglou A, Panoulas VF, Wilson M, Nevill AM, Koutedakis Y, et al. Association of physical inactivity with increased cardiovascular risk in patients with rheumatoid arthritis. Eur J Cardiovasc Prev Rehabil. 2009;16(2):188–94.

    PubMed  Google Scholar 

  52. 52.

    Myasoedova E, Crowson CS, Kremers HM, Fitz-Gibbon PD, Therneau TM, Gabriel SE. Total cholesterol and LDL levels decrease before rheumatoid arthritis. Ann Rheum Dis. 2010;69(7):1310–4.

    CAS  PubMed  Google Scholar 

  53. 53.

    Toms TE, Panoulas VF, Douglas KM, et al. Are lipid ratios less susceptible to change with systemic inflammation than individual lipid components in patients with rheumatoid arthritis? Angiology. 2011;62(2):167–75.

    CAS  PubMed  Google Scholar 

  54. 54.

    • Myasoedova E, Crowson CS, Kremers HM, et al. Lipid paradox in rheumatoid arthritis: the impact of serum lipid measures and systemic inflammation on the risk of cardiovascular disease. Ann Rheum Dis. 2011;70(3):482–7 The contradictory behavior of lipids in rheumatoid arthritis has been explained in this paper.

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Panoulas VF, Douglas KM, Milionis HJ, et al. Prevalence and associations of hypertension and its control in patients with rheumatoid arthritis. Rheumatology. 2007;46(9):1477–82.

    CAS  PubMed  Google Scholar 

  56. 56.

    Panoulas VF, Metsios GS, Pace AV, John H, Treharne GJ, Banks MJ, et al. Hypertension in rheumatoid arthritis. Rheumatology. 2008;47(9):1286–98.

    CAS  PubMed  Google Scholar 

  57. 57.

    Protogerou AD, Panagiotakos DB, Zampeli E, Argyris AA, Arida K, Konstantonis GD, et al. Arterial hypertension assessed “out-of-office” in a contemporary cohort of rheumatoid arthritis patients free of cardiovascular disease is characterized by high prevalence, low awareness, poor control and increased vascular damage-associated “white coat” phenomenon. Arthritis Res Ther. 2013;15(5):R142.

    PubMed  PubMed Central  Google Scholar 

  58. 58.

    Boyer J-F, Gourraud P-A, Cantagrel A, Davignon J-L, Constantin A. Traditional cardiovascular risk factors in rheumatoid arthritis: a meta-analysis. Joint Bone Spine. 2011;78(2):179–83.

    PubMed  Google Scholar 

  59. 59.

    Balsa A, Lojo-Oliveira L, Alperi-López M, García-Manrique M, Ordóñez-Cañizares C, Pérez L, et al. Prevalence of comorbidities in rheumatoid arthritis and evaluation of their monitoring in clinical practice: the Spanish cohort of the COMORA study. Reumatologia Clinica. 2017.

  60. 60.

    Panoulas VF, Douglas KM, Smith JP, et al. Polymorphisms of the endothelin-1 gene associate with hypertension in patients with rheumatoid arthritis. Endothelium: J Endothelial Cell Res. 2008;15(4):203–12.

    CAS  Google Scholar 

  61. 61.

    Wei Z-H, Du Y-H. Transforming growth factor-β1-509C/T polymorphism might be associated with chronic periodontitis risk. Biomed Res. 2017:28(18).

  62. 62.

    Hackshaw A, Morris JK, Boniface S, Tang J-L, Milenković D. Low cigarette consumption and risk of coronary heart disease and stroke: meta-analysis of 141 cohort studies in 55 study reports. BMJ. 2018;360:j5855.

    PubMed  PubMed Central  Google Scholar 

  63. 63.

    Bergström U, Jacobsson LTH, Nilsson J-Å, Berglund G, Turesson C. Pulmonary dysfunction, smoking, socioeconomic status and the risk of developing rheumatoid arthritis. Rheumatology. 2011;50(11):2005–13.

    PubMed  Google Scholar 

  64. 64.

    Terao C, Ohmura K, Ikari K, Kawaguchi T, Takahashi M, Setoh K, et al. Effects of smoking and shared epitope on the production of anti-citrullinated peptide antibody in a Japanese adult population. Arthritis Care Res. 2014;66(12):1818–27.

    CAS  Google Scholar 

  65. 65.

    Wolfe F. The effect of smoking on clinical, laboratory, and radiographic status in rheumatoid arthritis. J Rheumatol. 2000;27(3):630–7.

    CAS  PubMed  Google Scholar 

  66. 66.

    Stavropoulos-Kalinoglou A, Metsios GS, Panoulas VF, Douglas KMJ, Nevill AM, Jamurtas AZ, et al. Cigarette smoking associates with body weight and muscle mass of patients with rheumatoid arthritis: a cross-sectional, observational study. Arthritis Res Ther. 2008;10(3):R59.

    PubMed  PubMed Central  Google Scholar 

  67. 67.

    Sugiyama D, Nishimura K, Tamaki K, Tsuji G, Nakazawa T, Morinobu A, et al. Impact of smoking as a risk factor for developing rheumatoid arthritis: a meta-analysis of observational studies. Ann Rheum Dis. 2010;69(01):70–81.

    CAS  PubMed  Google Scholar 

  68. 68.

    Baghdadi LR, Woodman RJ, Shanahan EM, Mangoni AA. The impact of traditional cardiovascular risk factors on cardiovascular outcomes in patients with rheumatoid arthritis: a systematic review and meta-analysis. PLoS One. 2015;10(2):e0117952.

    PubMed  PubMed Central  Google Scholar 

  69. 69.

    Peters MJ, Van Halm VP, Voskuyl AE, et al. Does rheumatoid arthritis equal diabetes mellitus as an independent risk factor for cardiovascular disease? A prospective study. Arthritis Care Res. 2009;61(11):1571–9.

    Google Scholar 

  70. 70.

    Magda S. Rheumatoid arthritis vs. diabetes mellitus as risk factors for cardiovascular disease: the CARRE study. Maedica. 2010;5(2):147.

    PubMed  PubMed Central  Google Scholar 

  71. 71.

    Lindhardsen J, Ahlehoff O, Gislason GH, Madsen OR, Olesen JB, Torp-Pedersen C, et al. The risk of myocardial infarction in rheumatoid arthritis and diabetes mellitus: a Danish nationwide cohort study. Ann Rheum Dis. 2011;70(6):929–34.

    PubMed  Google Scholar 

  72. 72.

    Montagna GL, Cacciapuoti F, Buono R, Manzella D, Mennillo GA, Arciello A, et al. Insulin resistance is an independent risk factor for atherosclerosis in rheumatoid arthritis. Diab Vasc Dis Res. 2007;4(2):130–5.

    PubMed  Google Scholar 

  73. 73.

    Giles JT, Danielides S, Szklo M, et al. Insulin resistance in rheumatoid arthritis: disease-related indicators and associations with the presence and progression of subclinical atherosclerosis. Arthritis Rheumatol (Hoboken, NJ). 2015;67(3):626–36.

    CAS  Google Scholar 

  74. 74.

    Ammirati E, Moroni F, Norata GD, Magnoni M, Camici PG. Markers of inflammation associated with plaque progression and instability in patients with carotid atherosclerosis. Mediat Inflamm. 2015;2015:1–15.

    Google Scholar 

  75. 75.

    Smolen JS, Aletaha D, McInnes IB. Rheumatoid arthritis. Lancet. 388(10055):2023–38.

  76. 76.

    Wallberg-Jonsson S, Ohman M, Dahlqvist SR. Cardiovascular morbidity and mortality in patients with seropositive rheumatoid arthritis in northern Sweden. J Rheumatol. 1997;24(3):445–51.

    CAS  PubMed  Google Scholar 

  77. 77.

    Wållberg-Jonsson S, Johansson H, Ohman M, Rantapää-Dahlqvist S. Extent of inflammation predicts cardiovascular disease and overall mortality in seropositive rheumatoid arthritis. A retrospective cohort study from disease onset. J Rheumatol. 1999;26(12):2562–71.

    PubMed  Google Scholar 

  78. 78.

    Majka DS, Vu TT, Pope RM, et al. Association of rheumatoid factors with subclinical and clinical atherosclerosis in African American women: the multiethnic study of atherosclerosis. Arthritis Care Res. 2017;69(2):166–74.

    CAS  Google Scholar 

  79. 79.

    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;10:49–59.

    CAS  PubMed  PubMed Central  Google Scholar 

  80. 80.

    Goodson NJ, Symmons DP, Scott DG, Bunn D, Lunt M, Silman AJ. Baseline levels of C-reactive protein and prediction of death from cardiovascular disease in patients with inflammatory polyarthritis: a ten-year followup study of a primary care-based inception cohort. Arthritis Rheum. 2005;52(8):2293–9.

    CAS  PubMed  Google Scholar 

  81. 81.

    Gonzalez-Gay MA, Gonzalez-Juanatey C, Piñeiro A, Garcia-Porrua C, Testa A, Llorca J. High-grade C-reactive protein elevation correlates with accelerated atherogenesis in patients with rheumatoid arthritis. J Rheumatol. 2005;32(7):1219–23.

    CAS  PubMed  Google Scholar 

  82. 82.

    Taverner D, Vallvé J-C, Ferré R, Paredes S, Masana L, Castro A. Variables associated with subclinical atherosclerosis in a cohort of rheumatoid arthritis patients: sex-specific associations and differential effects of disease activity and age. PLoS One. 2018;13(3):e0193690.

    PubMed  PubMed Central  Google Scholar 

  83. 83.

    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.

    PubMed  Google Scholar 

  84. 84.

    Conroy R, Pyörälä K, Fitzgerald Ae, et al: Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J 2003, 24(11):987–1003.

  85. 85.

    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.

    PubMed  PubMed Central  Google Scholar 

  86. 86.

    Goff DC, Lloyd-Jones DM, Bennett G, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk. Circulation. 2014;129(25 suppl 2):S49–73.

    PubMed  Google Scholar 

  87. 87.

    Dessein PH, Joffe BI, Veller MG, et al. Traditional and nontraditional cardiovascular risk factors are associated with atherosclerosis in rheumatoid arthritis. J Rheumatol. 2005;32(3):435–42.

    PubMed  Google Scholar 

  88. 88.

    van der Heijde D, Ramiro S, Landewé R, et al: 2016 Update of the ASAS-EULAR management recommendations for axial spondyloarthritis. Ann Rheum Dis 2017:annrheumdis-2016-210770.

  89. 89.

    Agca R, Heslinga S, 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 2016:annrheumdis-2016-209775.

  90. 90.

    •• Crowson CS, Gabriel SE, Semb AG, et al. Rheumatoid arthritis-specific cardiovascular risk scores are not superior to general risk scores: a validation analysis of patients from seven countries. Rheumatology. 2017;56(7):1102–10 A comparative study of cardiovascular risk calculators has been presented in this article to demonstrate that the RA-specific calculators may either underestimate or overestimate the risk in the general population.

    CAS  PubMed  Google Scholar 

  91. 91.

    Stone NJ, Robinson JG, Lichtenstein AH, et al. ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol 2014. 2013;63(25 Part B):2889–934.

    Google Scholar 

  92. 92.

    Campos OAMd, Nazário NO, Fialho SCdMS, et al: Assessment of cardiovascular risk in patients with rheumatoid arthritis using the SCORE risk index. Rev Bras Reumatol 2016, 56(2):138–144.

  93. 93.

    Arts E, Popa C, Den Broeder A, et al: Prediction of cardiovascular risk in rheumatoid arthritis: performance of original and adapted SCORE algorithms. Ann Rheum Dis 2015:annrheumdis-2014-206879.

  94. 94.

    Hamilton-Craig C, Liew G, Chan J, et al: Coronary Artery Calcium Scoring—position statement.

  95. 95.

    Hou Z-h, Lu B, Gao Y, et al. Prognostic value of coronary CT angiography and calcium score for major adverse cardiac events in outpatients. JACC Cardiovasc Imaging. 2012;5(10):990–9.

    PubMed  Google Scholar 

  96. 96.

    Giles JT, Szklo M, Post W, Petri M, Blumenthal RS, Lam G, et al. Coronary arterial calcification in rheumatoid arthritis: comparison with the multi-ethnic study of atherosclerosis. Arthritis Res Ther. 2009;11(2):R36.

    PubMed  PubMed Central  Google Scholar 

  97. 97.

    Wahlin B, Meedt T, Jonsson F, Henein MY, Wållberg-Jonsson S. Coronary artery calcification is related to inflammation in rheumatoid arthritis: a long-term follow-up study. Biomed Res Int. 2016;2016:1–8.

    Google Scholar 

  98. 98.

    de González AB, Mahesh M, Kim K-P, et al. Projected cancer risks from computed tomographic scans performed in the United States in 2007. Arch Intern Med. 2009;169(22):2071–7.

    PubMed Central  Google Scholar 

  99. 99.

    Mavrogeni SI, Kitas GD, Dimitroulas T, Sfikakis PP, Seo P, Gabriel S, et al. Cardiovascular magnetic resonance in rheumatology: current status and recommendations for use. Int J Cardiol. 2016;217:135–48.

    PubMed  Google Scholar 

  100. 100.

    Hamdan A, Asbach P, Wellnhofer E, et al. A prospective study for comparison of MR and CT imaging for detection of coronary artery stenosis. JACC Cardiovasc Imaging. 2011;4(1):50–61.

    PubMed  Google Scholar 

  101. 101.

    Fayad ZA. MR imaging for the noninvasive assessment of atherothrombotic plaques. Magn Reson Imaging Clin. 2003;11(1):101–13.

    Google Scholar 

  102. 102.

    Cai J-M, Hatsukami TS, Ferguson MS, Small R, Polissar NL, Yuan C. Classification of human carotid atherosclerotic lesions with in vivo multicontrast magnetic resonance imaging. Circulation. 2002;106(11):1368–73.

    PubMed  Google Scholar 

  103. 103.

    Park YB, Ahn CW, Choi HK, Lee SH, in BH, Lee HC, et al. Atherosclerosis in rheumatoid arthritis: morphologic evidence obtained by carotid ultrasound. Arthritis Rheum. 2002;46(7):1714–9.

    PubMed  Google Scholar 

  104. 104.

    Del Rincon I. Atherosclerosis in rheumatoid arthritis: what to look for in studies using carotid ultrasound. J Rheumatol. 2010.

  105. 105.

    Patel S, Bhatt K, Patel A, et al: A study of carotid Intimomedial thickness as a primary marker of atherosclerosis in patients with rheumatoid arthritis. In: Int Cardiovasc Forum J: 2017; 2017.

  106. 106.

    Kablak-Ziembicka A, Tracz W, Przewlocki T, Pieniazek P, Sokolowski A, Konieczynska M. Association of increased carotid intima-media thickness with the extent of coronary artery disease. Heart. 2004;90(11):1286–90.

    CAS  PubMed  PubMed Central  Google Scholar 

  107. 107.

    Polak JF, Pencina MJ, Meisner A, Pencina KM, Brown LS, Wolf PA, et al. Associations of carotid artery intima-media thickness (IMT) with risk factors and prevalent cardiovascular disease. J Ultrasound Med. 2010;29(12):1759–68.

    PubMed  PubMed Central  Google Scholar 

  108. 108.

    Bots ML, Baldassarre D, Simon A, de Groot E, O’Leary DH, Riley W, et al. Carotid intima-media thickness and coronary atherosclerosis: weak or strong relations? Eur Heart J. 2007;28(4):398–406.

    PubMed  Google Scholar 

  109. 109.

    •• Wang P, Guan S-Y, Xu S-Z, et al. Increased carotid intima-media thickness in rheumatoid arthritis: an update meta-analysis. Clin Rheumatol. 2016;35(2):315–23 It is an important article that indicated the elevated thickness between intima and media layers in patients with rheumatoid arthritis compared to controls.

    PubMed  Google Scholar 

  110. 110.

    van Sijl AM, Peters MJ, Knol DK, et al: Carotid intima media thickness in rheumatoid arthritis as compared to control subjects: a meta-analysis. In: Semin Arthritis Rheum: 2011: Elsevier; 2011: 389–397.

  111. 111.

    Gonzalez-Juanatey C, Llorca J, Martin J, Gonzalez-Gay MA: Carotid intima-media thickness predicts the development of cardiovascular events in patients with rheumatoid arthritis. In: Seminars in Arthritis and Rheumatism: 2009: Elsevier; 2009: 366–371.

  112. 112.

    • Hannawi S, Haluska B, Marwick TH, Thomas R. Atherosclerotic disease is increased in recent-onset rheumatoid arthritis: a critical role for inflammation. Arthritis Res Therapy. 2007;9(6):R116 RA patients have more carotid plaque, and increased carotid intima-media thickness has been demonstrated by the authors.

    Google Scholar 

  113. 113.

    Narayan N, Owen DR, Taylor PC. Advances in positron emission tomography for the imaging of rheumatoid arthritis. Rheumatology. 2017;56(11):1837–46.

    CAS  PubMed  Google Scholar 

  114. 114.

    Evans NR, Tarkin JM, Chowdhury MM, Warburton EA, Rudd JH. PET imaging of atherosclerotic disease: advancing plaque assessment from anatomy to pathophysiology. Curr Atheroscler Rep. 2016;18(6):30.

    PubMed  PubMed Central  Google Scholar 

  115. 115.

    Rosenbaum D, Millon A, Fayad ZA. Molecular imaging in atherosclerosis: FDG PET. Curr Atheroscler Rep. 2012;14(5):429–37.

    PubMed  PubMed Central  Google Scholar 

  116. 116.

    Lin E, Alessio A. What are the basic concepts of temporal, contrast, and spatial resolution in cardiac CT? J Cardiovasc Comput Tomogr. 2009;3(6):403–8.

    PubMed  PubMed Central  Google Scholar 

  117. 117.

    Saba L, Suri JS: Multi-detector CT imaging: abdomen, pelvis, and CAD applications, vol. 2: CRC; 2013.

  118. 118.

    Kalyan K, Jakhia B, Lele RD, Joshi M, Chowdhary A. Artificial neural network application in the diagnosis of disease conditions with liver ultrasound images. Adv Bioinforma. 2014;2014:1–14.

    Google Scholar 

  119. 119.

    Karimi A, Rahmati SM, Sera T, Kudo S, Navidbakhsh M: A combination of constitutive damage model and artificial neural networks to characterize the mechanical properties of the healthy and atherosclerotic human coronary arteries. Artif Organs 2017, 41(9).

  120. 120.

    El-Baz A, Gimel’farb G, Suzuki K. Machine learning applications in medical image analysis. Comput Math Methods Med. 2017;2017:1–2.

    Google Scholar 

  121. 121.

    Ambale-Venkatesh B, Yang X, Wu CO, Liu K, Hundley WG, McClelland R, et al. Cardiovascular event prediction by machine learning: the multi-ethnic study of atherosclerosis. Circ Res. 2017;121(9):1092–101.

    CAS  PubMed  PubMed Central  Google Scholar 

  122. 122.

    Acharya UR, Faust O, Sree SV, Molinari F, Saba L, Nicolaides A, et al. An accurate and generalized approach to plaque characterization in 346 carotid ultrasound scans. IEEE Trans Instrum Meas. 2012;61(4):1045–53.

    Google Scholar 

  123. 123.

    Acharya RU, Faust O, Alvin APC, Sree SV, Molinari F, Saba L, et al. Symptomatic vs. asymptomatic plaque classification in carotid ultrasound. J Med Syst. 2012;36(3):1861–71.

    PubMed  Google Scholar 

  124. 124.

    •• Tsiaparas NN, Golemati S, Andreadis I, Stoitsis JS, Valavanis I, Nikita KS: Comparison of multiresolution features for texture classification of carotid atherosclerosis from B-mode ultrasound. IEEE Trans Inf Technol Biomed 2011, 15(1):130–137. An automated machine learning–based approach for carotid plaque characterization using texture-based features has been presented in this article.

  125. 125.

    Acharya UR, Krishnan MMR, Sree SV, et al. Plaque tissue characterization and classification in ultrasound carotid scans: a paradigm for vascular feature amalgamation. IEEE Trans Instrum Meas. 2013;62(2):392–400.

    Google Scholar 

  126. 126.

    Acharya UR, Mookiah MRK, Sree SV, et al. Atherosclerotic plaque tissue characterization in 2D ultrasound longitudinal carotid scans for automated classification: a paradigm for stroke risk assessment. Med Biol Eng Comput. 2013;51(5):513–23.

    PubMed  Google Scholar 

  127. 127.

    Acharya UR, Faust O, Alvin A, et al. Understanding symptomatology of atherosclerotic plaque by image-based tissue characterization. Comput Methods Prog Biomed. 2013;110(1):66–75.

    Google Scholar 

  128. 128.

    Pazinato DV, Stein BV, de Almeida WR, de O. Werneck R, Junior PRM, Penatti OAB, et al. Pixel-level tissue classification for ultrasound images. IEEE J Biomed Health Inform. 2016;20(1):256–67.

    PubMed  Google Scholar 

  129. 129.

    Huang X, Zhang Y, Qian M, Meng L, Xiao Y, Niu L, et al. Classification of carotid plaque echogenicity by combining texture features and morphologic characteristics. J Ultrasound Med. 2016;35(10):2253–61.

    PubMed  Google Scholar 

  130. 130.

    Araki T, Jain PK, Suri HS, Londhe ND, Ikeda N, el-Baz A, et al. Stroke risk stratification and its validation using ultrasonic echolucent carotid wall plaque morphology: a machine learning paradigm. Comput Biol Med. 2017;80:77–96.

    PubMed  Google Scholar 

  131. 131.

    Qian C, Yang X. An integrated method for atherosclerotic carotid plaque segmentation in ultrasound image. Comput Methods Prog Biomed. 2018;153:19–32.

    Google Scholar 

  132. 132.

    •• Lekadir K, Galimzianova A, Betriu À, et al. A convolutional neural network for automatic characterization of plaque composition in carotid ultrasound. IEEE J Biomed Health Inform. 2017;21(1):48–55 Deep learning–based carotid plaque tissue characterization has been presented in this paper.

    PubMed  Google Scholar 

  133. 133.

    Boi A, Jamthikar AD, Saba L, Gupta D, Sharma A, Loi B, et al. A survey on coronary atherosclerotic plaque tissue characterization in intravascular optical coherence tomography. Curr Atheroscler Rep. 2018;20(7):33.

    PubMed  Google Scholar 

  134. 134.

    Christodoulou CI, Kyriacou E, Pattichis MS, Pattichis CS, Nicolaides A: A comparative study of morphological and other texture features for the characterization of atherosclerotic carotid plaques. In: International Conference on Computer Analysis of Images and Patterns: 2003: Springer; 2003: 503–511.

  135. 135.

    Christodoulou CI, Pattichis CS, Pantziaris M, Nicolaides A. Texture-based classification of atherosclerotic carotid plaques. IEEE Trans Med Imaging. 2003;22(7):902–12.

    CAS  PubMed  Google Scholar 

  136. 136.

    Acharya UR, Sree SV, Krishnan MMR, et al. Atherosclerotic risk stratification strategy for carotid arteries using texture-based features. Ultrasound Med Biol. 2012;38(6):899–915.

    PubMed  Google Scholar 

  137. 137.

    Gupta A, Kesavabhotla K, Baradaran H, Kamel H, Pandya A, Giambrone AE, et al. Plaque echolucency and stroke risk in asymptomatic carotid stenosis: a systematic review and meta-analysis. Stroke. 2015;46(1):91–7.

    PubMed  Google Scholar 

  138. 138.

    Mathiesen EB, Bønaa KH, Joakimsen O. Echolucent plaques are associated with high risk of ischemic cerebrovascular events in carotid stenosis: the Tromsø study. Circulation. 2001;103(17):2171–5.

    CAS  PubMed  Google Scholar 

  139. 139.

    Naqvi TZ, Lee M-S. Carotid intima-media thickness and plaque in cardiovascular risk assessment. JACC Cardiovasc Imaging. 2014;7(10):1025–38.

    PubMed  Google Scholar 

  140. 140.

    Huang X, Zhang Y, Meng L, Abbott D, Qian M, Wong KKL, et al. Evaluation of carotid plaque echogenicity based on the integral of the cumulative probability distribution using gray-scale ultrasound images. PLoS One. 2017;12(10):e0185261.

    PubMed  PubMed Central  Google Scholar 

  141. 141.

    Kotsis V, Jamthikar AD, Araki T, Gupta D, Laird JR, Giannopoulos AA, et al. Echolucency-based phenotype in carotid atherosclerosis disease for risk stratification of diabetes patients. Diabetes Res Clin Pract. 2018;143:322–31.

    PubMed  Google Scholar 

  142. 142.

    Arnold J, Modaresi K, Thomas N, Taylor P, Padayachee T. Carotid plaque characterization by duplex scanning: observer error may undermine current clinical trials. Stroke. 1999;30(1):61–5.

    CAS  PubMed  Google Scholar 

  143. 143.

    Doonan R, Dawson A, Kyriacou E, et al. Association of ultrasonic texture and echodensity features between sides in patients with bilateral carotid atherosclerosis. Eur J Vasc Endovasc Surg. 2013;46(3):299–305.

    CAS  PubMed  Google Scholar 

  144. 144.

    Irie Y, Katakami N, Kaneto H, Takahara M, Nishio M, Kasami R, et al. The utility of ultrasonic tissue characterization of carotid plaque in the prediction of cardiovascular events in diabetic patients. Atherosclerosis. 2013;230(2):399–405.

    CAS  PubMed  Google Scholar 

  145. 145.

    Shen D, Wu G, Suk H-I. Deep learning in medical image analysis. Annu Rev Biomed Eng. 2017;19:221–48.

    CAS  PubMed  PubMed Central  Google Scholar 

  146. 146.

    Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60–88.

    PubMed  Google Scholar 

  147. 147.

    Im CH, Kim NR, Kang JW, et al. Inflammatory burden interacts with conventional cardiovascular risk factors for carotid plaque formation in rheumatoid arthritis. Rheumatology. 2014;54(5):808–15.

    PubMed  Google Scholar 

  148. 148.

    Biswas M, Kuppili V, Araki T, Edla DR, Godia EC, Saba L, et al. Deep learning strategy for accurate carotid intima-media thickness measurement: an ultrasound study on Japanese diabetic cohort. Comput Biol Med. 2018;98:100–17.

    PubMed  Google Scholar 

  149. 149.

    Saba L, Jain PK, Suri HS, Ikeda N, Araki T, Singh BK, et al. Plaque tissue morphology-based stroke risk stratification using carotid ultrasound: a polling-based PCA learning paradigm. J Med Syst. 2017;41(6):98.

    PubMed  Google Scholar 

  150. 150.

    Sigala F, Oikonomou E, Antonopoulos AS, Galyfos G, Tousoulis D: Coronary versus carotid artery plaques. Similarities and differences regarding biomarkers morphology and prognosis. Curr Opin Pharmacol 2018, 39:9–18.

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Correspondence to Jasjit S. Suri.

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Narendra N. Khanna, Ankush D. Jamthikar, Deep Gupta, Matteo Piga, Luca Saba, Carlo Carcassi, Argiris A. Giannopoulos, Andrew Nicolaides, John R. Laird, Harman S. Suri, Sophie Mavrogeni, A.D. Protogerou, Petros Sfikakis, and George D. Kitas declare no conflict of interest. Jasjit S. Suri is affiliated to AtheroPoint™, focused in the area of stroke and cardiovascular imaging.

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Khanna, N.N., Jamthikar, A.D., Gupta, D. et al. Rheumatoid Arthritis: Atherosclerosis Imaging and Cardiovascular Risk Assessment Using Machine and Deep Learning–Based Tissue Characterization. Curr Atheroscler Rep 21, 7 (2019). https://doi.org/10.1007/s11883-019-0766-x

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Keywords

  • Rheumatoid arthritis
  • Atherosclerosis
  • Cardiovascular risk assessment
  • Carotid ultrasound
  • Optical coherence tomography
  • Tissue characterization
  • Machine learning
  • Deep learning