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    An international model to predict recurrent cardiovascular disease

    Access Status
    Fulltext not available
    Authors
    Wilson, P.
    D'Agostino, R.
    Bhatt, D.
    Eagle, K.
    Pencina, M.
    Smith, S.
    Alberts, M.
    Dallongeville, J.
    Goto, S.
    Hirsch, A.
    Liau, C.
    Ohman, E.
    Röther, J.
    Reid, Christopher
    Mas, J.
    Steg, P.
    Date
    2012
    Type
    Journal Article
    
    Metadata
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    Citation
    Wilson, P. and D'Agostino, R. and Bhatt, D. and Eagle, K. and Pencina, M. and Smith, S. and Alberts, M. et al. 2012. An international model to predict recurrent cardiovascular disease. American Journal of Medicine. 125 (7): pp. 695-703.
    Source Title
    American Journal of Medicine
    DOI
    10.1016/j.amjmed.2012.01.014
    ISSN
    0002-9343
    School
    Department of Health Policy and Management
    URI
    http://hdl.handle.net/20.500.11937/42802
    Collection
    • Curtin Research Publications
    Abstract

    Background: Prediction models for cardiovascular events and cardiovascular death in patients with established cardiovascular disease are not generally available. Methods: Participants from the prospective REduction of Atherothrombosis for Continued Health (REACH) Registry provided a global outpatient population with known cardiovascular disease at entry. Cardiovascular prediction models were estimated from the 2-year follow-up data of 49,689 participants from around the world. Results: A developmental prediction model was estimated from 33,419 randomly selected participants (2394 cardiovascular events with 1029 cardiovascular deaths) from the pool of 49,689. The number of vascular beds with clinical disease, diabetes, smoking, low body mass index, history of atrial fibrillation, cardiac failure, and history of cardiovascular event(s) <1 year before baseline examination increased risk of a subsequent cardiovascular event. Statin (hazard ratio 0.75; 95% confidence interval, 0.69-0.82) and acetylsalicylic acid therapy (hazard ratio 0.90; 95% confidence interval, 0.83-0.99) also were significantly associated with reduced risk of cardiovascular events. The prediction model was validated in the remaining 16,270 REACH subjects (1172 cardiovascular events, 494 cardiovascular deaths). Risk of cardiovascular death was similarly estimated with the same set of risk factors. Simple algorithms were developed for prediction of overall cardiovascular events and for cardiovascular death. Conclusions: This study establishes and validates a risk model to predict secondary cardiovascular events and cardiovascular death in outpatients with established atherothrombotic disease. Traditional risk factors, burden of disease, lack of treatment, and geographic location all are related to an increased risk of subsequent cardiovascular morbidity and cardiovascular mortality.

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