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SP0091 Accuracy of Cardiovascular Risk Algorithms in Rheumatoid Arthritis and Its Management: Next Steps to a Targeted Approach
  1. G.D. Kitas1,2
  1. 1Arthritis Research UK Epidemiology Unit, University of Manchester, Manchester
  2. 2Research and Development, The Dudley Group NHS Foundation Trust, West Midlands, United Kingdom

Abstract

People with Rheumatoid Arthritis (RA) have increased cardiovascular disease (CVD) morbidity and mortality. This appears to be due to increased prevalence, insufficient identification and poor control of classical CVD risk factors including dyslipidaemia, hypertension, physical inactivity, obesity and insulin resistance; and the presence of additional risk factors, particularly systemic inflammation (and its direct or indirect effects on the vasculature), and many of the anti-rheumatic drugs that have significant vasoactive and cardio-metabolic effects, amongst others. Their combination may lead to acceleration of atherosclerosis, plaque instability and rupture and pro-thrombotic phenomena expressed clinically as the whole range of syndromes, from stable ischaemia to acute CVD events and death. Processes other than atherothrombosis, including myocarditis, vasculitis, microvascular dysfunction, and cardiac autonomic dysfunction are now receiving increasing attention. At the clinical level, the question remains as to how we can identify early on the high-risk patients and what primary prevention strategies we can implement to reduce their risk. Algorithms developed in the general population appear to underestimate risk in people with RA, and development of RA-specific CVD risk algorithms has proven very challenging predominantly due to massive heterogeneity of data in different RA populations. The effects of primary prevention strategies including lifestyle modification, pharmacological reduction of classical CVD risk factors (mainly lipids and blood pressure) and of inflammation (using antirheumatic medications) on cardiovascular outcomes have not been assessed through randomised controlled trials; available data are limited to small interventional studies assessing surrogates of CVD and to retrospective analysis of observational studies. On such a backdrop, a practical approach will be presented that can be used in many different clinical settings.

Disclosure of Interest None declared

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