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    A score for the prediction of cardiovascular events in the hypertensive aged

    Access Status
    Open access via publisher
    Authors
    Nelson, M.
    Ramsay, E.
    Ryan, P.
    Willson, K.
    Tonkin, A.
    Wing, L.
    Simons, L.
    Reid, Christopher
    Date
    2012
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Nelson, M. and Ramsay, E. and Ryan, P. and Willson, K. and Tonkin, A. and Wing, L. and Simons, L. et al. 2012. A score for the prediction of cardiovascular events in the hypertensive aged. American Journal of Hypertension. 25 (2): pp. 190-194.
    Source Title
    American Journal of Hypertension
    DOI
    10.1038/ajh.2011.192
    ISSN
    0895-7061
    School
    Department of Health Policy and Management
    URI
    http://hdl.handle.net/20.500.11937/31264
    Collection
    • Curtin Research Publications
    Abstract

    Background: With few exceptions, tools used to estimate cardiovascular disease (CVD) risk in those without prior events are based mainly on data from middle-aged subjects. Given the ever increasing number of older people, many with hypertension, a risk score relevant to this group is warranted. Our aim was to develop a cardiovascular risk equation suitable for risk prediction in elderly, hypertensive populations. Methods We utilized cardiovascular end point data from 4.1 years median follow-up in 5,426 hypertensive subjects without previous CVD from the Second Australian National Blood Pressure Study (ANBP2). Our risk model, based on Cox regression, was developed using 75% of subjects without evident CVD (n = 4,072), randomly selected and stratified by age and gender, and internally validated using the remaining 25%. The model was also externally validated against the Dubbo Study dataset. Results The final model included sex, age, physical activity in the 2 weeks prior to entry into study, family history, use of anticoagulants, centrally acting antihypertensive agents or diabetes medication, and an interaction term for sex and diabetes medication. The C-statistic was 0.65 (0.62–0.67) for our predictive model on the model development dataset and 0.62 (0.57–0.67) on the internal validation dataset. The Dubbo Data C-statistic for CVD was 0.68 (95% CI 0.65–0.71). Conclusions All models performed similarly. Because of greater ease of implementation, we recommend that existing algorithms be extended into older age groups.

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