Prediction of acute kidney injury within 30 days of cardiac surgery
MetadataShow full item record
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.
Showing items related by title, author, creator and subject.
A preoperative risk prediction model for 30-day mortality following cardiac surgery in an Australian cohortBillah, B.; Reid, Christopher; Shardey, G.; Smith, J. (2010)Background: Population-specific risk models are required to build consumer and provider confidence in clinical service delivery, particularly when the risks may be life-threatening. Cardiac surgery carries such risks. ...
An Australian risk prediction model for 30-day mortality after isolated coronary artery bypass: The AusSCOREReid, Christopher; Billah, B.; Dinh, D.; Smith, J.; Skillington, P.; Yii, M.; Seevanayagam, S.; Mohajeri, M.; Shardey, G. (2009)Objective: Our objective was to identify risk factors associated with 30-day mortality after isolated coronary artery bypass grafting in the Australian context and to develop a preoperative model for 30-day mortality risk ...
Acute Risk Change for Cardiothoracic Admissions to Intensive Care (ARCTIC index): A new measure of quality in cardiac surgeryCoulson, T.; Bailey, M.; Reid, Christopher; Tran, L.; Mullany, D.; Smith, J.; Pilcher, D. (2014)Background: Quality of cardiac surgical care may vary between institutions. Mortality is low and large numbers are required to discriminate between hospitals. Measures other than mortality may provide better comparisons. ...