Preoperative predictive model for acute kidney injury after elective cardiac surgery: a prospective multicenter cohort study

Abstract

Background: Predictive 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.

Methods: A 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.

Results: Both 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).

Conclusions: We 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.