Neutrophil gelatinase-associated lipocalin as predictor of acute kidney injury requiring renal replacement therapy: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: Patients with severe acute kidney injury (AKI) may require renal replacement therapy (RRT), such as hemodialysis and peritoneal dialysis. Neutrophil gelatinase-associated lipocalin (NGAL) is a sensitive indicator for early diagnosis and recognition of AKI; however, its predictive value of AKI-associated need for RRT needs further evaluation. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, relevant articles were systematically searched and selected from seven databases. The random effects model was applied to evaluate the predictive performance of NGAL for AKI requiring RRT. The Newcastle-Ottawa Scale (NOS) was used to assess the quality of each included study. Results: A total of 18 studies including 1,787 patients with AKI and having an average NOS score of 7.67 were included in the meta-analysis. For plasma/serum NGAL, the pooled sensitivity and specificity with corresponding 95% confidence interval (CI) were 0.75 (95% CI: 0.68-0.81) and 0.76 (95% CI: 0.70-0.81), respectively. The pooled positive likelihood ratio (PLR) was 2.9 (95% CI: 2.1-4.1), and the pooled negative likelihood ratio (NLR) was 0.34 (95% CI: 0.25-0.46). Subsequently, the pooled diagnostic odds ratio (DOR) was 9 (95% CI: 5-16) using a random effects model, and the area under the curve (AUC) of summary receiver operating characteristic to summarize predictive accuracy was 0.82 (95% CI: 0.79-0.85). For urine NGAL, the pooled sensitivity, specificity, PLR, NLR, DOR, and AUC values were 0.78 (95% CI: 0.61-0.90), 0.77 (95% CI: 0.65-0.85), 3.4 (95% CI: 2.4-4.8), 0.28 (95% CI: 0.15-0.52), 12 (95% CI: 6-24), and 0.84 (95% CI: 0.80-0.87), respectively. Conclusion: Plasma/serum and urine NGAL levels performed comparably well in predicting AKI requiring RRT. Our findings suggested that NGAL is an effective predictive biomarker for the AKI-associated need for RRT. Nevertheless, more pieces of high-quality evidence and future trials with larger sample sizes are needed for further improvement of patient outcomes. Systematic review registration: [https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022346595], identifier [CRD42022346595].
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.043 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".