Accuracy of Pre-existing Risk Scoring Models for Predicting Acute Kidney Injury in Patients Who Underwent Aortic Surgery Using a Gray Zone Approach

Accuracy of Pre-existing Risk Scoring Models for Predicting Acute Kidney Injury in Patients Who Underwent Aortic Surgery Using a Gray Zone Approach

Acute kidney injury after cardiac surgery has been reported to increase morbidity and mortality. Several risk scoring models for prediction of aortic kidney injury after cardiac surgery have been developed. However, predictive accuracy of these models is stil unclear. The aim of this study is to evaluate the accuracy of four pre-existing prediction models using a gray zone approach in patients who underwent aortic surgery in our institution.

Acute kidney injury after cardiac surgery has been reported to increase morbidity and mortality. Several risk scoring models for prediction of aortic kidney injury after cardiac surgery have been developed. However, predictive accuracy of these models is stil unclear. The aim of this study is to evaluate the accuracy of four pre-existing prediction models (AKICS, Wijeysundera, Mehta, and Thakar model)using a gray zone approach in patients who underwent aortic surgery in our institution. Based on receiver operating characteristic (ROC) curve analysis, we will construct a gray zone using the cut-off values with a sensitivity of < 90%, and a specificity of < 90% (diagnostic tolerance of 10%).

Inclusion Criteria:

• Those who underwent elective or emergency aortic surgery in Samsung Medical Center during between 2004 and 2010.

Exclusion Criteria:

• missing laboratory data
• preoperative hemodialysis
• death during or within 48 hours after surgery