Novel biomarkers for predicting successful liberation of renal replacement therapy for acute kidney injury: a systematic review
Bibliographic record
Abstract
Renal 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. The 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 2 statistics. Sixteen 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 2 = 39.8%). When excluding a study focused on long-term outcomes, the result showed a better predictive ability with low heterogeneity (AUC 0.801, I 2 = 0%). Plasma proenkephalin A (PENK) and serum NGAL also showed potential, but quantitative synthesis was limited by study number and heterogeneity. The cut-off value also varied widely, complicating clinical translation. In addition, multivariable models combining novel biomarkers with clinical indicators have also demonstrated promising predictive potential. However, due to the limited number of studies and inconsistent conclusions, further exploration is needed. uNGAL 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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.019 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".