ArticleJournal of the American Heart Association2021
Plasma Metabolites-Based Prediction in Cardiac Surgery-Associated Acute Kidney Injury.
Article in Journal of the American Heart Association, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- Machine learning for the prediction of acute kidney injury post cardiac surgery: a systematic review and meta-analysis.BMC medical informatics and decision making · 2026Pooled it
- Explainable machine learning using urinary metabolomics to predict pediatric sepsis-associated acute kidney injury: a two-center prospective observational study.Renal failure · 2026Observational
- Differential transcriptomic and metabolomic landscape between human hearts donated after cardiac vs brain death.The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation · 2026Article
- Untargeted urinary metabolomics reveals metabolic alterations and severity biomarkers in cardiac surgery-associated acute kidney injury.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- Integrated lipidomics and proteomics analysis in the cardiac surgery-associated acute kidney injury.Renal failure · 2025Article
- Machine learning model for the prediction of postoperative acute kidney injury (AKI) in neonates undergoing digestive surgery.Pediatric surgery international · 2025Article
- N -Acetyl-Tryptophan in Acute Kidney Injury after Cardiac Surgery.Journal of the American Society of Nephrology : JASN · 2025Article
- Serum metabolic profiling of patients with diabetic kidney disease based on gas chromatography-mass spectrometry.Frontiers in molecular biosciences · 2025Article
- A Predictive Model for Acute Kidney Injury Based on Leukocyte-Related Indicators in Hepatocellular Carcinoma Patients Admitted to the Intensive Care Unit.Mediators of inflammation · 2025Article
- An Integrated Approach Based on Clinical Data Combined with Metabolites and Biomarkers for the Assessment of Post-Operative Complications after Cardiac Surgery.Journal of clinical medicine · 2024Article
- Renal Arterial and Venous Doppler in Cardiorenal Syndrome: Pathophysiological and Clinical Insights.Biomedicines · 2024Review
- Article
- Huangqi-Danshen decoction protects against cisplatin-induced acute kidney injury in mice.Frontiers in pharmacology · 2023Article
- Article
- Machine Learning Algorithm to Predict Obstructive Coronary Artery Disease: Insights from the CorLipid Trial.Metabolites · 2022Article
- Metabolomic profiling demonstrates evidence for kidney and urine metabolic dysregulation in a piglet model of cardiac surgery-induced acute kidney injury.American journal of physiology. Renal physiology · 2022Article
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background Cardiac surgery-associated acute kidney injury (CSA-AKI) is a common postoperative complication following cardiac surgery. Currently, there are no reliable methods for the early prediction of CSA-AKI in hospitalized patients. This study developed and evaluated the diagnostic use of metabolomics-based biomarkers in patients with CSA-AKI. Methods and Results A total of 214 individuals (122 patients with acute kidney injury [AKI], 92 patients without AKI as controls) were enrolled in this study. Plasma samples were analyzed by liquid chromatography tandem mass spectrometry using untargeted and targeted metabolomic approaches. Time-dependent effects of selected metabolites were investigated in an AKI swine model. Multiple machine learning algorithms were used to identify plasma metabolites positively associated with CSA-AKI. Metabolomic analyses from plasma samples taken within 24 hours following cardiac surgery were useful for distinguishing patients with AKI from controls without AKI. Gluconic acid, fumaric acid, and pseudouridine were significantly upregulated in patients with AKI. A random forest model constructed with selected clinical parameters and metabolites exhibited excellent discriminative ability (area under curve, 0.939; 95% CI, 0.879-0.998). In the AKI swine model, plasma levels of the 3 discriminating metabolites increased in a time-dependent manner (
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