A Comparison of Three Prediction Models for Acute Kidney Injury Requiring Renal Replacement Therapy after Coronary Artery Bypass Graft Surgery
Background Acute kidney injury (AKI) following cardiac surgery is associated with increased post-operative morbidity and mortality. Scoring systems to predict acute kidney injury requiring renal replacement therapy (RRT) among patients undergoing cardiac surgery have been developed to assess risk pre-operatively and assist clinicians on the management post-operatively. These predictive models have good discriminative value. The significance of this study is to determine the most predictive model by comparing the 3 models to be able to be utilized and applied in our setting.
Objective To compare the Cleveland Score by Thakar, Simplified Renal Index (SRI) by Wijeysundera, and Simplified Bedside Risk tool by Mehta in predicting acute kidney injury requiring renal replacement therapy among patients who underwent cardiac surgery.
Design, Setting, and Participants Cross sectional analytic study of 427 patients who underwent coronary artery bypass graft surgery from St. Luke's Medical Center, Quezon City from January 2009-October 2014
Primary Outcome Acute Kidney Injury requiring Renal Replacement Therapy after cardiac surgery
Results A total of 427 patients who underwent coronary artery bypass graft surgery. Acute kidney injury was documented in 25.5% (n=109) of subjects, 13.3% (n=57) underwent post-operative renal replacement therapy (RRT), either intermittent hemodialysis or continuous renal replacement therapy. Discrimination for the prediction of RRT was good for the three scoring models using areas under the receiver operating characteristic curve (AUROCs): 0.94 (95% CI, 0.916 to 0.963) using Mehta; 0.92 (95% CI, 0.890 to 0.944) using Thakar, and 0.90 (95% CI, 0.867 to 0.926) using SRI. Mehta showed the highest predictive value, with significant difference with SRI (P = 0.0053).
Conclusion The Bedside Tool for Predicting Risk of Postoperative Dialysis by Mehta, et al., has the highest predictive value among the three models. However, compared to Cleveland score by Thakar, Mehta does better by only 2% and is not statistically significant. Mehta compared to SRI, does better by 4% and is statistically significant. Hence, among the three models, Mehta and Thakar have the greatest predictive value in assessing acute kidney injury requiring renal replacement therapy after coronary bypass.
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