• Raquel Callejas, Alfredo Panadero, Marc Vives, Paula Duque, Gemma Echarri, Pablo Monedero, and Renal Dysfunction in Cardiac Surgery Spanish Group (GEDRCC2).
• Department of Anesthesia and Critical Care, Clínica Universidad de Navarra, University of Navarra, Pamplona, Spain - rcallejas@unav.es.
• Minerva Anestesiol. 2019 Jan 1; 85 (1): 34-44.

BackgroundPredictive models of acute kidney injury after cardiac surgery (CS-AKI) include emergency surgery and patients with hemodynamic instability. Our objective was to evaluate the performance of validated predictive models (Thakar and Demirjian) in elective cardiac surgery and to propose a better score in the case of poor performance.MethodsA prospective, multicenter, observational study was designed. Data were collected from 942 patients undergoing cardiac surgery, after excluding emergency surgery and patients with an intra-aortic balloon pump. The main outcome measure was CS-AKI defined by the composite of requiring dialysis or doubling baseline creatinine values.ResultsBoth models showed poor discrimination in elective surgery (Thakar's model, AUC=0.57, 95% CI: 0.50-0.64 and Demirjian's model, AUC=0.64, 95% CI: 0.58-0.71). We generated a new model whose significant independent predictors were: anemia, age, hypertension, obesity, congestive heart failure, previous cardiac surgery and type of surgery. It classifies patients with scores 0-3 as at low risk (<5%), patients with scores 4-7 as at medium risk (up to 15%), and patients with scores >8 as at high risk (>30%) of developing CS-AKI with a statistically significant correlation (P<0.001). Our model reflects acceptable discriminatory ability (AUC=0.72, 95% CI: 0.66-0.78) which is significantly better than Thakar and Demirjian's models (P<0.01).ConclusionsWe developed a new simple predictive model of CS-AKI in elective surgery based on available preoperative information. Our new model is easy to calculate and can be an effective tool for communicating risk to patients and guiding decision-making in the perioperative period. The study requires external validation.

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