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

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35096889", "kind": "science-record", "revision": 5, "content_hash": "sha256:4ccf0ac640eef9e028c4a67adfe928f3096999eb6356b4a3f63ba46ab801cc09", "created_at": "2026-11-01T17:36:49Z", "updated_at": "2026-11-01T22:48:20Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Novel Plasma Biomarker-Based Model for Predicting Acute Kidney Injury After Cardiac Surgery: A Case Control Study.", "doi": "10.3389/fmed.2021.799516", "pmid": "35096889", "authors": ["Yichi Zhang", "Haige Zhao", "Qun Su", "Cuili Wang", "Hongjun Chen", "Lingling Shen", "Liang Ma", "Tingting Zhu", "Wenqing Chen", "Hong Jiang", "Jianghua Chen"], "journal": "Frontiers in medicine", "year": 2021, "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.\nMETHODS: 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.\nRESULTS: 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).\nCONCLUSION: Our research validated a promising plasma multi-biomarker model for predicting AKI after cardiac surgery.", "claims": [{"id": "claim-method", "statement": "WGCNA package was used to identify AKI-related modules and find hub genes in significant modules.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35096889", "locator": "sections[3] \"Differentially Expressed Gene (DEG) Analysis and WGCNA\" paragraph 2", "quote": "WGCNA package was used to identify AKI-related modules and find hub genes in significant modules."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE30718.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35096889", "locator": "sections[18] \"Data Availability Statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE30718."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A multi-biomarker model incorporating GDF15, IL1RL1, and uPAR predicted cardiac-surgery-associated AKI with a C-statistic of 0.84 and Brier score of 0.15, outperforming the clinical-factor-based model.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35096889", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "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)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A multi-biomarker model incorporating GDF15, IL1RL1, and uPAR predicted cardiac-surgery-associated AKI with a C-statistic of 0.84 and Brier score of 0.15, outperforming the clinical-factor-based model.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:35096889"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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