BACKGROUND: Acute kidney injury (AKI) requiring renal replacement therapy (RRT) in patients undergoing cardiac surgery is associated strongly with adverse patient outcomes. Recently, 3 predictive risk models for RRT have been developed. The aims of our study are to validate the predictive scoring models for patients requiring postoperative RRT and test applicability to the broader spectrum of patients with postoperative severe AKI. STUDY DESIGN: Diagnostic test study. SETTING & PARTICIPANTS: 12,096 patients undergoing cardiac surgery with cardiopulmonary bypass at Mayo Clinic, Rochester, MN, from 2000 through 2007. INDEX TEST: Cleveland Clinic score, Mehta score, and Simplified Renal Index (SRI) score. REFERENCE TEST OR OUTCOME: Incidence of postoperative RRT or composite outcome of severe AKI, defined as serum creatinine level >2.0 mg/dL, and a 2-fold increase compared with the preoperative baseline creatinine level or RRT. RESULTS: RRT was used in 254 (2.1%) patients, whereas severe AKI was present in 467 (3.9%). Discrimination for the prediction of RRT and severe AKI was good for all scoring models measured using areas under the receiver operating characteristic curve (AUROCs): 0.86 (95% CI, 0.84-0.88) for RRT and 0.81 (95% CI, 0.79-0.83) for severe AKI using the Cleveland score, 0.81 (95% CI, 0.78-0.86) and 0.76 (95% CI, 0.73-0.80) using the Mehta score, and 0.79 (95% CI, 0.77-0.82) and 0.75 (95% CI, 0.72-0.77) using the SRI score. The Cleveland score and Mehta score consistently showed significantly better discrimination compared with the SRI score (P < 0.001). Despite lower AUROCs for the prediction of severe AKI, the Cleveland score AUROC was still >0.80. The Mehta score is applicable in only a subgroup of patients. LIMITATIONS: Single-center retrospective cohort study. CONCLUSIONS: The Cleveland scoring system offers the best discriminative value to predict postoperative RRT and covers most patients undergoing cardiac surgery. It also can be used for prediction of the composite end point of severe AKI, which enables broader application to patients at risk of postoperative kidney dysfunction.
BACKGROUND:Acute kidney injury (AKI) requiring renal replacement therapy (RRT) in patients undergoing cardiac surgery is associated strongly with adverse patient outcomes. Recently, 3 predictive risk models for RRT have been developed. The aims of our study are to validate the predictive scoring models for patients requiring postoperative RRT and test applicability to the broader spectrum of patients with postoperative severe AKI. STUDY DESIGN: Diagnostic test study. SETTING & PARTICIPANTS: 12,096 patients undergoing cardiac surgery with cardiopulmonary bypass at Mayo Clinic, Rochester, MN, from 2000 through 2007. INDEX TEST: Cleveland Clinic score, Mehta score, and Simplified Renal Index (SRI) score. REFERENCE TEST OR OUTCOME: Incidence of postoperative RRT or composite outcome of severe AKI, defined as serum creatinine level >2.0 mg/dL, and a 2-fold increase compared with the preoperative baseline creatinine level or RRT. RESULTS: RRT was used in 254 (2.1%) patients, whereas severe AKI was present in 467 (3.9%). Discrimination for the prediction of RRT and severe AKI was good for all scoring models measured using areas under the receiver operating characteristic curve (AUROCs): 0.86 (95% CI, 0.84-0.88) for RRT and 0.81 (95% CI, 0.79-0.83) for severe AKI using the Cleveland score, 0.81 (95% CI, 0.78-0.86) and 0.76 (95% CI, 0.73-0.80) using the Mehta score, and 0.79 (95% CI, 0.77-0.82) and 0.75 (95% CI, 0.72-0.77) using the SRI score. The Cleveland score and Mehta score consistently showed significantly better discrimination compared with the SRI score (P < 0.001). Despite lower AUROCs for the prediction of severe AKI, the Cleveland score AUROC was still >0.80. The Mehta score is applicable in only a subgroup of patients. LIMITATIONS: Single-center retrospective cohort study. CONCLUSIONS: The Cleveland scoring system offers the best discriminative value to predict postoperative RRT and covers most patients undergoing cardiac surgery. It also can be used for prediction of the composite end point of severe AKI, which enables broader application to patients at risk of postoperative kidney dysfunction.
Authors: Jeremiah R Brown; Robert S Kramer; Todd A MacKenzie; Steven G Coca; Kyaw Sint; Chirag R Parikh Journal: Ann Thorac Surg Date: 2011-12-28 Impact factor: 4.330
Authors: Joseph L Alge; Nithin Karakala; Benjamin A Neely; Michael G Janech; James A Tumlin; Lakhmir S Chawla; Andrew D Shaw; John M Arthur Journal: Clin J Am Soc Nephrol Date: 2012-11-08 Impact factor: 8.237
Authors: Colin R Lenihan; Maria E Montez-Rath; Christina T Mora Mangano; Glenn M Chertow; Wolfgang C Winkelmayer Journal: Ann Thorac Surg Date: 2012-12-25 Impact factor: 4.330