AUTHOR=Xu Wang , Ouyang Xin , Lin Yingxin , Lai Xue , Zhu Junjiang , Chen Zeling , Liu Xiaolong , Jiang Xinyi , Chen Chunbo 

TITLE=Prediction of acute kidney injury after cardiac surgery with fibrinogen-to-albumin ratio: a prospective observational study

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 11 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2024.1336269

DOI=10.3389/fcvm.2024.1336269

ISSN=2297-055X

ABSTRACT=Background: Acute kidney injury (AKI) after cardiac surgery is frequent and associated with adverse outcomes, whereas its early detection remains a challenge. This study was performed to investigate whether the fibrinogen-to-albumin ratio (FAR), a novel inflammation-based risk index, can predict the occurrence of AKI in patients undergoing cardiac surgery.Methods: Patients who underwent cardiac surgery from February 2023 to March 2023 and were admitted to the Department of Cardiac Surgery Intensive Care Unit of a tertiary teaching hospital were included in this prospective observational study. AKI was defined according to the KDIGO criteria. The area under the receiver operating characteristic curve (AUC), continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were calculated to evaluate the diagnostic value of the FAR for AKI prediction.Results: Of the 260 enrolled patients, 85 manifested AKI with an incidence of 32.7%. Based on the multivariate logistic analyses, FAR at admission [odds ratio (OR), 1.197; 95% confidence interval (CI),  1.064-1.347, p = 0.003] was an independent risk factor for AKI. The receiver operating characteristic (ROC) curve indicated that FAR at admission was a significant predictor of AKI [AUC, 0.685, 95% CI: 0.616-0.754]. Although the AUC-ROC of the prediction model was not substantially enhanced by adding FAR, continuous NRI and IDI were significantly improved.FAR is independently associated with the occurrence of AKI after cardiac surgery and can significantly improve AKI prediction over the clinical prediction model.