Literature DB >> 33342731

Association Between Intraoperative Hyperoxia and Acute Kidney Injury After Cardiac Surgery: A Retrospective Observational Study.

Jinyoung Bae1, Jay Kim1, Seohee Lee1, Jae-Woo Ju1, Youn Joung Cho1, Tae Kyong Kim2, Yunseok Jeon1, Karam Nam3.   

Abstract

OBJECTIVE: Optimal oxygen management during cardiac surgery has not been established, and studies on the effects of perioperative hyperoxia on postoperative acute kidney injury (AKI) are scarce. The association between intraoperative hyperoxia and AKI after cardiac surgery involving cardiopulmonary bypass was evaluated for the present study.
DESIGN: Retrospective observational study.
SETTING: A tertiary teaching hospital. PARTICIPANTS: Adult patients who underwent cardiac surgery with cardiopulmonary bypass from November 2006-December 2018.
INTERVENTIONS: None.
MEASUREMENTS AND MAIN RESULTS: The area above arterial oxygen partial pressure (PaO2) threshold of 300 mmHg (AOT300, mmHg × h) was used as a metric of intraoperative hyperoxia and was associated with postoperative AKI, using the logistic regression analysis. Data also were fitted using the restricted cubic spline model. Sensitivity analyses were conducted using different PaO2 thresholds (150, 200, 250, and 350 mmHg). A total of 2,926 patients were analyzed. Intraoperative AOT300 independently was associated with the risk of AKI (odds ratio 1.0009; 95% confidence interval 1.0002-1.0015). A PaO2 increment of 100 mmHg above PaO2 300 mmHg for an hour was associated with an increased risk of AKI by 9.4% (1.0009100 ≈ 1.094). In the spline model, the log-odds of AKI increased as AOT300 increased. In the sensitivity analyses, AOT250 and AOT350 also significantly were associated with the risk of AKI, whereas AOT150 and AOT200 were not. As the PaO2 threshold increased from 150 to 350 mmHg, the odds ratio gradually increased.
CONCLUSIONS: Intraoperative hyperoxia significantly was associated with the risk of AKI after cardiac surgery involving cardiopulmonary bypass.
Copyright © 2020 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  acute kidney injury; cardiac surgery; cardiopulmonary bypass; hyperoxia; oxygen

Year:  2020        PMID: 33342731     DOI: 10.1053/j.jvca.2020.11.054

Source DB:  PubMed          Journal:  J Cardiothorac Vasc Anesth        ISSN: 1053-0770            Impact factor:   2.628


  1 in total

1.  Using a machine learning model to predict the development of acute kidney injury in patients with heart failure.

Authors:  Wen Tao Liu; Xiao Qi Liu; Ting Ting Jiang; Meng Ying Wang; Yang Huang; Yu Lin Huang; Feng Yong Jin; Qing Zhao; Qin Yi Wu; Bi Cheng Liu; Xiong Zhong Ruan; Kun Ling Ma
Journal:  Front Cardiovasc Med       Date:  2022-09-07
  1 in total

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