Predictive models for kidney disease: improving global outcomes (KDIGO) defined acute kidney injury in UK cardiac surgery

Introduction: Acute kidney injury (AKI) risk prediction scores are an objective and transparent means to enable cohort enrichment in clinical trials or to risk stratify patients preoperatively. Existing scores are limited in that they have been designed to predict only severe, or non-consensus AKI definitions and not less severe stages of AKI, which also have prognostic significance. The aim of this study was to develop and validate novel risk scores that could identify all patients at risk of AKI. Methods: Prospective routinely collected clinical data (n?=?30,854) were obtained from 3 UK cardiac surgical centres (Bristol, Birmingham and Wolverhampton). AKI was defined as per the Kidney Disease: Improving Global Outcomes (KDIGO) Guidelines. The model was developed using the Bristol and Birmingham datasets, and externally validated using the Wolverhampton data. Model discrimination was estimated using the area under the ROC curve (AUC). Model calibration was assessed using the Hosmer¿Lemeshow test and calibration plots. Diagnostic utility was also compared to existing scores. Results: The risk prediction score for any stage AKI (AUC?=?0.74 (95% confidence intervals (CI) 0.72, 0.76)) demonstrated better discrimination compared to the Euroscore and the Cleveland Clinic Score, and equivalent discrimination to the Mehta and Ng scores. The any stage AKI score demonstrated better calibration than the four comparison scores. A stage 3 AKI risk prediction score also demonstrated good discrimination (AUC = 0.78 (95% CI 0.75, 0.80)) as did the four comparison risk scores, but stage 3 AKI scores were less well calibrated. Conclusions: This is the first risk score that accurately identifies patients at risk of any stage AKI. This score will be useful in the perioperative management of high risk patients as well as in clinical trial design.