Keith Colaco1,2, Vanessa Ocampo1,2, Ana Patricia Ayala1,2, Paula Harvey1,2, Dafna D Gladman1,2, Vincent Piguet1,2, Lihi Eder3,4. 1. From Women's College Research Institute, Women's College Hospital; Centre for Prognosis Studies in the Rheumatic Diseases, Toronto Western Hospital; Institute of Medical Science, Department of Medicine, and Gerstein Science Information Centre, University of Toronto, Toronto, Ontario, Canada. 2. K. Colaco, MSc, Women's College Research Institute, Women's College Hospital, and Centre for Prognosis Studies in the Rheumatic Diseases, Toronto Western Hospital, and Institute of Medical Science, University of Toronto; V. Ocampo, MD, Centre for Prognosis Studies in the Rheumatic Diseases, Toronto Western Hospital, and Department of Medicine, University of Toronto; A.P. Ayala, MISt, Gerstein Science Information Centre, University of Toronto; P. Harvey, BMBS, PhD, Women's College Research Institute, Women's College Hospital, and Department of Medicine, University of Toronto; D.D. Gladman, MD, Centre for Prognosis Studies in the Rheumatic Diseases, Toronto Western Hospital, and Institute of Medical Science, and Department of Medicine, University of Toronto; V. Piguet, MD, PhD, Women's College Research Institute, Women's College Hospital, and Centre for Prognosis Studies in the Rheumatic Diseases, Toronto Western Hospital, and Department of Medicine, University of Toronto; L. Eder, MD, PhD, Women's College Research Institute, Women's College Hospital, and Department of Medicine, University of Toronto. 3. From Women's College Research Institute, Women's College Hospital; Centre for Prognosis Studies in the Rheumatic Diseases, Toronto Western Hospital; Institute of Medical Science, Department of Medicine, and Gerstein Science Information Centre, University of Toronto, Toronto, Ontario, Canada. lihi.eder@wchospital.ca. 4. K. Colaco, MSc, Women's College Research Institute, Women's College Hospital, and Centre for Prognosis Studies in the Rheumatic Diseases, Toronto Western Hospital, and Institute of Medical Science, University of Toronto; V. Ocampo, MD, Centre for Prognosis Studies in the Rheumatic Diseases, Toronto Western Hospital, and Department of Medicine, University of Toronto; A.P. Ayala, MISt, Gerstein Science Information Centre, University of Toronto; P. Harvey, BMBS, PhD, Women's College Research Institute, Women's College Hospital, and Department of Medicine, University of Toronto; D.D. Gladman, MD, Centre for Prognosis Studies in the Rheumatic Diseases, Toronto Western Hospital, and Institute of Medical Science, and Department of Medicine, University of Toronto; V. Piguet, MD, PhD, Women's College Research Institute, Women's College Hospital, and Centre for Prognosis Studies in the Rheumatic Diseases, Toronto Western Hospital, and Department of Medicine, University of Toronto; L. Eder, MD, PhD, Women's College Research Institute, Women's College Hospital, and Department of Medicine, University of Toronto. lihi.eder@wchospital.ca.
Abstract
OBJECTIVE: We performed a systematic review of the literature to describe current knowledge of cardiovascular (CV) risk prediction algorithms in rheumatic diseases. METHODS: A systematic search of MEDLINE, EMBASE, and Cochrane Central databases was performed. The search was restricted to original publications in English, had to include clinical CV events as study outcomes, assess the predictive properties of at least 1 CV risk prediction algorithm, and include patients with rheumatoid arthritis (RA), ankylosing spondylitis (AS), systemic lupus erythematosus (SLE), psoriatic arthritis (PsA), or psoriasis. By design, only cohort studies that followed participants for CV events were selected. RESULTS: Eleven of 146 identified manuscripts were included. Studies evaluated the predictive performance of the Framingham Risk Score, QRISK2, Systematic Coronary Risk Evaluation (SCORE), Reynolds Risk Score, American College of Cardiology/American Heart Association Pooled Cohort Equations (PCE), Expanded Cardiovascular Risk Prediction Score for Rheumatoid Arthritis (ERS-RA), and the Italian Progetto CUORE score. Approaches to improve predictive performance of general risk algorithms in patients with RA included the use of multipliers, biomarkers, disease-specific variables, or a combination of these to modify or develop an algorithm. In both SLE and PsA patients, multipliers were applied to general risk algorithms. In studies of RA and SLE patients, efforts to include nontraditional risk factors, disease-related variables, multipliers, and biomarkers largely failed to substantially improve risk estimates. CONCLUSION: Our study confirmed that general risk algorithms mostly underestimate and at times overestimate CV risk in rheumatic patients. We did not find studies that evaluated models for psoriasis or AS, which further demonstrates a need for research in these populations.
OBJECTIVE: We performed a systematic review of the literature to describe current knowledge of cardiovascular (CV) risk prediction algorithms in rheumatic diseases. METHODS: A systematic search of MEDLINE, EMBASE, and Cochrane Central databases was performed. The search was restricted to original publications in English, had to include clinical CV events as study outcomes, assess the predictive properties of at least 1 CV risk prediction algorithm, and include patients with rheumatoid arthritis (RA), ankylosing spondylitis (AS), systemic lupus erythematosus (SLE), psoriatic arthritis (PsA), or psoriasis. By design, only cohort studies that followed participants for CV events were selected. RESULTS: Eleven of 146 identified manuscripts were included. Studies evaluated the predictive performance of the Framingham Risk Score, QRISK2, Systematic Coronary Risk Evaluation (SCORE), Reynolds Risk Score, American College of Cardiology/American Heart Association Pooled Cohort Equations (PCE), Expanded Cardiovascular Risk Prediction Score for Rheumatoid Arthritis (ERS-RA), and the Italian Progetto CUORE score. Approaches to improve predictive performance of general risk algorithms in patients with RA included the use of multipliers, biomarkers, disease-specific variables, or a combination of these to modify or develop an algorithm. In both SLE and PsA patients, multipliers were applied to general risk algorithms. In studies of RA and SLEpatients, efforts to include nontraditional risk factors, disease-related variables, multipliers, and biomarkers largely failed to substantially improve risk estimates. CONCLUSION: Our study confirmed that general risk algorithms mostly underestimate and at times overestimate CV risk in rheumaticpatients. We did not find studies that evaluated models for psoriasis or AS, which further demonstrates a need for research in these populations.
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