SynthesisCritical care (London, England)2025
Novel biomarkers for predicting successful liberation of renal replacement therapy for acute kidney injury: a systematic review.
Synthesis in Critical care (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- GATM alleviates sepsis-induced acute kidney injury via PDK4-mediated glycolytic reprogramming in renal tubular epithelial cells.Cellular and molecular life sciences : CMLS · 2026Article
- Article
- Predicting Continuous Kidney Support Therapy (CKST) time: the role of urine NGAL in guiding CKST duration and discontinuation.Pediatric research · 2026Article
- Proenkephalin A for assessing kidney integrity and guiding KRT liberation decisions in critically ill patients.Annals of intensive care · 2026Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
introductionRenal replacement therapy (RRT) is commonly used in critically ill patients with acute kidney injury (AKI). However, optimal timing of RRT liberation remains controversy. This meta-analysis evaluates novel biomarkers to predict successful RRT liberation in critically ill AKI patients.
methodsThe systematic review reported following PRISMA guidelines, PubMed, Embase, and Scopus were searched up to May 2, 2025, and were screened using predefined criteria. Methodological quality was assessed using the Newcastle-Ottawa scale. Pooled ROC-AUCs with 95% CIs were calculated; heterogeneity was evaluated using I
resultsSixteen studies (3020 patients) involving 23 biomarkers were included. Urinary neutrophil gelatinase-associated lipocalin (uNGAL) demonstrated fair predictive performance with 4 studies (AUC 0.766, I
conclusionuNGAL moderately predicts short-term RRT liberation, while other biomarkers (e.g., PENK) require further validation. Standardizing definitions of successful liberation and integrating dynamic biomarker changed with clinical indicators (e.g., urine output) may enhance predictive accuracy. Further large-scale, prospective, and multicenter studies are needed to validate these findings.
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