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    Prediction of acute kidney injury within 30 days of cardiac surgery

    Access Status
    Open access via publisher
    Authors
    Ng, S.
    Sanagou, M.
    Wolfe, R.
    Cochrane, A.
    Smith, J.
    Reid, Christopher
    Date
    2014
    Type
    Journal Article
    
    Metadata
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    Citation
    Ng, S. and Sanagou, M. and Wolfe, R. and Cochrane, A. and Smith, J. and Reid, C. 2014. Prediction of acute kidney injury within 30 days of cardiac surgery. Journal of Thoracic and Cardiovascular Surgery. 147 (6): pp. 1875-1883.
    Source Title
    Journal of Thoracic and Cardiovascular Surgery
    DOI
    10.1016/j.jtcvs.2013.06.049
    ISSN
    0022-5223
    School
    Department of Health Policy and Management
    URI
    http://hdl.handle.net/20.500.11937/39989
    Collection
    • Curtin Research Publications
    Abstract

    Objective To predict acute kidney injury after cardiac surgery. Methods The study included 28,422 cardiac surgery patients who had had no preoperative renal dialysis from June 2001 to June 2009 in 18 hospitals. Logistic regression analyses were undertaken to identify the best combination of risk factors for predicting acute kidney injury. Two models were developed, one including the preoperative risk factors and another including the pre-, peri-, and early postoperative risk factors. The area under the receiver operating characteristic curve was calculated, using split-sample internal validation, to assess model discrimination. Results The incidence of acute kidney injury was 5.8% (1642 patients). The mortality for patients who experienced acute kidney injury was 17.4% versus 1.6% for patients who did not. On validation, the area under the curve for the preoperative model was 0.77, and the Hosmer-Lemeshow goodness-of-fit P value was.06. For the postoperative model area under the curve was 0.81 and the Hosmer-Lemeshow P value was.6. Both models had good discrimination and acceptable calibration. Conclusions Acute kidney injury after cardiac surgery can be predicted using preoperative risk factors alone or, with greater accuracy, using pre-, peri-, and early postoperative risk factors. The ability to identify high-risk individuals can be useful in preoperative patient management and for recruitment of appropriate patients to clinical trials. Prediction in the early stages of postoperative care can guide subsequent intensive care of patients and could also be the basis of a retrospective performance audit tool. © 2014 by The American Association for Thoracic Surgery.

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