AUTHOR=Zhang Yichi , Zhao Haige , Su Qun , Wang Cuili , Chen Hongjun , Shen Lingling , Ma Liang , Zhu Tingting , Chen Wenqing , Jiang Hong , Chen Jianghua 

TITLE=Novel Plasma Biomarker-Based Model for Predicting Acute Kidney Injury After Cardiac Surgery: A Case Control Study

JOURNAL=Frontiers in Medicine

VOLUME=Volume 8 - 2021

YEAR=2022

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2021.799516

DOI=10.3389/fmed.2021.799516

ISSN=2296-858X

ABSTRACT=Abstract
Introduction: 
Acute kidney injury after cardiac surgery is independently associated with prolonged hospital stay, increased cost of care and increased postoperative mortality. Delayed elevation of serum creatinine levels requires novel biomarkers to provide prediction of acute kidney injury after cardiac surgery. Our objective was to find a novel blood biomarkers combination to construct a model for predicting acute kidney injury after cardiac surgery and risk stratification. 
Methods: 
This was a case-control study. Weighted Gene Co-expression Network Analysis  was applied to Gene Expression Omnibus (GEO) dataset GSE30718 to seek for potential biomarkers associated with acute kidney injury. We measured biomarker level in venous blood samples of 67 patients with acute kidney injury after cardiac surgery and 59 control patients in two cohorts. Clinical data were collected. We developed a multi-biomarker model for predicting cardiac-surgery-associated acute kidney injury and compared it with traditional clinical-factor-based model. 
Results: 
From bioinformatics analysis and previous articles, we found 6 potential plasma biomarkers for prediction of acute kidney injury. Among them, 3 biomarkers including growth differentiation factor 15 (GDF15), soluble suppression of tumorigenicity 2 (ST2, IL1RL1) and soluble urokinase plasminogen activator receptor (uPAR) were found having prediction ability for acute kidney injury (AUC > 0.6) in patients undergoing cardiac surgery. They were then incorporated into a multi-biomarker model for predicting acute kidney injury (C-statistic: 0.84, Brier 0.15) which outperformed traditional clinical-factor-based model (C-statistic: 0.73, Brier 0.16). 
Conclusion: 
Our research validated a promising plasma multi-biomarker model for predicting acute kidney injury after cardiac surgery.