A Better Predictor of Acute Kidney Injury After Cardiac Surgery: The Largest Area Under the Curve Below the Oxygen Delivery Threshold During Cardiopulmonary Bypass. Issue 15 (4th August 2020) - Oshita, Tomoya; Hiraoka, Arudo; Nakajima, Kosuke; Muraki, Ryosuke; Arimichi, Masahisa; Chikazawa, Genta; Yoshitaka, Hidenori; Sakaguchi, Taichi
Abstract : Background: The aim of this study was to compare the predictive accuracy of acute kidney injury (AKI) after cardiac surgery using cardiopulmonary bypass for the largest area under the curve (AUC) below the oxygen delivery (DO2 ) threshold and the cumulative AUC below the DO2 threshold. Methods and Results: From March 2017 to October 2019, 202 patients who had undergone cardiac surgery with cardiopulmonary bypass were enrolled. The perfusion parameters were recorded every 20 seconds, and the DO2 (10×pump flow index [L/min per m 2 ]×[hemoglobin (g/dL)×1.36×arterial oxygen saturation (%)+partial pressure of arterial oxygen (mm Hg)×0.003]) threshold of 300 mL/min per m 2 was considered to define sufficient DO2 . The nadir DO2, the cumulative AUC below the DO 2 300, and the largest AUC below the DO 2 300 were used to predict the incidence of AKI. Postoperative AKI was observed in 12.4% of patients (25/202). By multivariable analysis, the largest AUC below the DO 2 300 ≥880 (odds ratio [OR], 4.9; 95% CI, 1.2–21.5 [ P =0.022]), preoperative hemoglobin concentration ≤11.6 g/dL (OR, 7.6; 95% CI, 2.0–32.3 [ P =0.004]), and red blood cell transfusions during cardiopulmonary bypass ≥2 U (OR, 3.3; 95% CI, 1.0–11.1 [ P =0.041]) were detected as independent risk factors for AKI. Receiver operating curve analysis revealed that the largest AUC below the DO 2 300 was more accurate to predict postoperative AKI compared with the nadir DO2 and the cumulative AUC below the DO 2 300 (differences between areas, 0.0691 [ P =0.006] and 0.0395 [ P =0.001]). Conclusions: These data suggest that a high AUC below the DO 2 300 is an important independent risk factor for AKI after cardiopulmonary bypass, which could be considered for risk prediction models of AKI.