Prediction of acute renal failure after cardiac surgery: retrospective cross-validation of a clinical algorithm

Nephrol Dial Transplant. 2003 Jan;18(1):77-81. doi: 10.1093/ndt/18.1.77.


Background: Acute renal failure (ARF) after cardiac surgery is associated with high costs and a poor prognosis. Based on the results of a large US study, an algorithm has been developed for predicting ARF from pre-operative risk factors. The aim of this study was to cross-validate this algorithm in a patient population from Europe, and to assess its usefulness as a clinical tool.

Methods: All coronary bypass and valvular surgery patients from a 5-year period were included. Data on pre-operative risk factors for all patients who developed dialysis-dependent ARF and for a random sample of patients without ARF were retrospectively obtained from hospital databases and medical records. For each patient, a risk score for ARF was calculated on the basis of the algorithm. The sensitivity, specificity, positive and negative predictive values and area under receiver operating characteristic (ROC) curve of the score's ability to predict ARF were estimated.

Results: 2037 patients were included. The risk of ARF was 1.9%, and the area under the ROC curve 0.71. For a risk score of 6 or higher, the sensitivity was 0.53, the specificity 0.71, the positive predictive value 0.03 and the negative predictive value 0.99.

Conclusions: The validity of the algorithm was confirmed in a population differing in several aspects from the US populations where it was developed. Although it is useful for estimating the risk of ARF in groups of patients, the low risk of ARF limits the algorithm's ability to predict the outcome for individual patients.

MeSH terms

  • Acute Kidney Injury / epidemiology*
  • Algorithms
  • Coronary Artery Bypass / adverse effects*
  • Female
  • Humans
  • Male
  • Multivariate Analysis
  • Predictive Value of Tests
  • Prognosis
  • Reproducibility of Results
  • Retrospective Studies
  • Vascular Surgical Procedures / adverse effects*