title: Prediction of acute kidney injury within 30 days of cardiac surgery.
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.
authors.affiliation: Department of Cardiothoracic Surgery, Monash Medical Centre, and Department of Surgery, Monash University Faculty of Medicine, Nursing and Health Sciences, Melbourne, Australia.
authors.forename: Shu Yi
authors.initials: SY
authors.lastname: Ng
authors.affiliation: Department of Epidemiology and Preventive Medicine, Monash University Faculty of Medicine, Nursing and Health Sciences, Melbourne, Australia. Electronic address: masoumeh.sanagou@monash.edu.
authors.forename: Masoumeh
authors.initials: M
authors.lastname: Sanagou
authors.affiliation: Department of Epidemiology and Preventive Medicine, Monash University Faculty of Medicine, Nursing and Health Sciences, Melbourne, Australia.
authors.forename: Rory
authors.initials: R
authors.lastname: Wolfe
authors.affiliation: Department of Cardiothoracic Surgery, Monash Medical Centre, and Department of Surgery, Monash University Faculty of Medicine, Nursing and Health Sciences, Melbourne, Australia.
authors.forename: Andrew
authors.initials: A
authors.lastname: Cochrane
authors.affiliation: Department of Cardiothoracic Surgery, Monash Medical Centre, and Department of Surgery, Monash University Faculty of Medicine, Nursing and Health Sciences, Melbourne, Australia.
authors.forename: Julian A
authors.initials: JA
authors.lastname: Smith
authors.affiliation: Department of Epidemiology and Preventive Medicine, Monash University Faculty of Medicine, Nursing and Health Sciences, Melbourne, Australia.
authors.forename: Christopher Michael
authors.initials: CM
authors.lastname: Reid
mesh_terms: D058186:Acute Kidney Injury / Q000453:epidemiology* / Q000401:mortality; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D001315:Australia / Q000453:epidemiology; D006348:Cardiac Surgical Procedures / Q000009:adverse effects* / Q000401:mortality; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D015994:Incidence; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D016017:Odds Ratio; D012372:ROC Curve; D012042:Registries; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D013997:Time Factors; D016896:Treatment Outcome