The multifactorial dynamic perfusion index: A predictive tool of cardiac surgery associated acute kidney injury

Abstract

Introduction cardiac surgery associated acute kidney injury (CSA-AKI) has a number of preoperative and intraoperative risk factors. Cardiopulmonary bypass (CPB) factors have not yet been elucidated in a single multivariate model. The aim of this study is to develop a dynamic predictive model for CSA-AKI. Methods retrospective study on 910 consecutive adult cardiac surgery patients. Baseline data were used to settle a preoperative CSA-AKI risk model (static risk model, SRM); CPB related data were assessed for association with CSA-AKI. CPB duration, nadir oxygen delivery, time of exposure to a low oxygen delivery, nadir mean arterial pressure, peak lactates and red blood cell transfusion were included in a multivariate dynamic perfusion risk (DPR). SRM and DPR were merged into a final logistic regression model (multifactorial dynamic perfusion index, MDPI). The three risk models were assessed for discrimination and calibration. Results the SRM model had an AUC of 0.696 (95% CI 0.663–0.727), the DPR model of 0.723 (95% CI 0.691–0.753), and the MDPI model an AUC of 0.769 (95% CI 0.739–0.798). The difference in AUC between SRM and DPR was not significant ( p = 0.495) whereas the AUC of MDPI was significantly larger than that of SRM ( p = 0.004) and DPR ( p = 0.015). Conclusions inclusion of dynamic indices of the quality of CPB improves the discrimination and calibration of the preoperative risk scores. The MDPI has better predictive ability than the existing static risk models and is a promising tool to integrate different factors into an advanced concept of goal-directed perfusion.