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- Tomoya Oshita, Arudo Hiraoka, Kosuke Nakajima, Ryosuke Muraki, Masahisa Arimichi, Genta Chikazawa, Hidenori Yoshitaka, and Taichi Sakaguchi.
- Department of Clinical Engineering The Sakakibara Heart Institute of Okayama Japan.
- J Am Heart Assoc. 2020 Aug 4; 9 (15): e015566.
AbstractBackground 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 m2]×[hemoglobin (g/dL)×1.36×arterial oxygen saturation (%)+partial pressure of arterial oxygen (mm Hg)×0.003]) threshold of 300 mL/min per m2 was considered to define sufficient DO2. The nadir DO2, the cumulative AUC below the [Formula: see text], and the largest AUC below the [Formula: see text] 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 [Formula: see text] ≥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 [Formula: see text] was more accurate to predict postoperative AKI compared with the nadir DO2 and the cumulative AUC below the [Formula: see text] (differences between areas, 0.0691 [P=0.006] and 0.0395 [P=0.001]). Conclusions These data suggest that a high AUC below the [Formula: see text] is an important independent risk factor for AKI after cardiopulmonary bypass, which could be considered for risk prediction models of AKI.
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