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

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

doi:10.3389/fmed.2021.799516

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

Front Med (Lausanne). 2022 Jan 14:8:799516. doi: 10.3389/fmed.2021.799516. eCollection 2021.

Authors

Yichi Zhang^{1

2

3

4}, Haige Zhao⁵, Qun Su⁶, Cuili Wang^{1

2

3

4}, Hongjun Chen^{1

2

3

4}, Lingling Shen^{1

2

3

4}, Liang Ma⁵, Tingting Zhu^{1

2

3

4}, Wenqing Chen^{1

2

3

4}, Hong Jiang^{1

2

3

4}, Jianghua Chen^{1

2

3

4}

Affiliations

¹ Kidney Disease Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
² Key Laboratory of Nephropathy, Hangzhou, China.
³ Institute of Nephropathy, Zhejiang University, Hangzhou, China.
⁴ Zhejiang Clinical Research Center of Kidney and Urinary System Disease, Hangzhou, China.
⁵ Department of Cardiothoracic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
⁶ Department of Intensive Care Unit, The First Affiliated Hospital, Zhejiang University, Hangzhou, China.

Abstract

Introduction: Acute kidney injury (AKI) after cardiac surgery is independently associated with a prolonged hospital stay, increased cost of care, and increased post-operative mortality. Delayed elevation of serum creatinine (SCr) levels requires novel biomarkers to provide a prediction of AKI after cardiac surgery. Our objective was to find a novel blood biomarkers combination to construct a model for predicting AKI after cardiac surgery and risk stratification.

Methods: This was a case-control study. Weighted Gene Co-expression Network Analysis (WGCNA) was applied to Gene Expression Omnibus (GEO) dataset GSE30718 to seek potential biomarkers associated with AKI. We measured biomarker levels in venous blood samples of 67 patients with AKI 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 AKI and compared it with a traditional clinical-factor-based model.

Results: From bioinformatics analysis and previous articles, we found 6 potential plasma biomarkers for the prediction of AKI. Among them, 3 biomarkers, such as growth differentiation factor 15 (GDF15), soluble suppression of tumorigenicity 2 (ST2, IL1RL1), and soluble urokinase plasminogen activator receptor (uPAR) were found to have prediction ability for AKI (area under the curve [AUC] > 0.6) in patients undergoing cardiac surgery. They were then incorporated into a multi-biomarker model for predicting AKI (C-statistic: 0.84, Brier 0.15) which outperformed the traditional clinical-factor-based model (C-statistic: 0.73, Brier 0.16).

Conclusion: Our research validated a promising plasma multi-biomarker model for predicting AKI after cardiac surgery.

Keywords: CSA-AKI; GDF-15; IL1RL1; WGCNA; acute kidney injury; biomarkers; predictive model; uPAR.