#535 Audit of the clinical implementation of urinary NGAL in the diagnostic work-up of acute kidney injury at an Irish Hospital
Bibliographic record
Abstract
Abstract Background and Aims Differentiating functional acute kidney injury (AKI) from structural tubular injury AKI remains challenging with existing clinical tools. urinary NGAL (uNGAL) has shown promise in distinguishing functional AKI, such as “Pre-Renal” AKI, from tubular injury (“Intra-Renal”) AKI. We evaluated the implementation of uNGAL in the clinical nephrology consult service over 3-year period in a heterogenous medical AKI population at a single center in Ireland. Method A retrospective audit investigating the clinical utility of uNGAL in an adult population at an Irish hospital was conducted from 2020–2023. Standard clinical data, such as clinical history, examination findings, radiology reports, serial serum creatinine and urea levels, proteinuria, FENa, and infection status around the time of AKI and uNGAL request were recorded. Blinded adjudication of the standard clinical data was performed between two expert Nephrologists. Responses were limited to: “Pre-Renal”, “Intra-Renal”, “Post-Renal”, “No-AKI”, or “Not Enough Information”. uNGAL accuracy in differentiating Intrinsic AKI from Functional AKI (Pre-renal & Post-renal) was assessed, and box plots visualized uNGAL and FENa levels across groups. Results A total of 320 uNGAL tests were performed between 2020 and 2023, which 292 were adjudicated to their AKI case. Adjudicated intra-renal AKI patients (n = 120) demonstrated significantly higher raw uNGAL levels (median: 1052 ng/ml [IQR: 302.4–1314.4]) compared to functional (median: 228.2 ng/ml [IQR: 69.7–895.1; P = 2.00 × 10-9) (Table 1). Similarly, cr-corrected uNGAL levels were significantly higher (P = 8.20 × 10⁻¹¹) in the intrinsic group (median: 1288.7 ng/mg [IQR: 438.2–2317.2]) compared to the functional group (median: 323.7 ng/mg [IQR: 86.9–1083.7]). These differences remained significant after adjusting for UTI status (Raw uNGAL: P = 1.34 × 10⁻¹²).; cr-corrected uNGAL: P = 5.66 × 10⁻¹²). The diagnostic accuracy of raw uNGAL at the manufacturer-recommended threshold of 150 ng/ml for adjudicated intrinsic AKI was moderate, with an AUC of 0.71 (95% CI: 0.64–0.77). At a lower threshold of 125 ng/ml, a similar diagnostic performance was observed. Cr-corrected uNGAL demonstrated slightly better performance, with an AUC of 0.73 (95% CI: 0.67–0.79) at a threshold of 150 ng/mg. FENa, in comparison, showed moderate diagnostic accuracy at a threshold of 2% (AUC: 0.67 [95% CI: 0.67–0.75]), with higher specificity (0.73) but lower sensitivity (0.47). After controlling for UTI status, the diagnostic accuracy of both raw and cr-corrected uNGAL improved. The AUC for raw uNGAL increased to 0.77 (95% CI: 0.71–0.84), while cr-corrected uNGAL improved to 0.76 (95% CI: 0.70–0.83). Sensitivity remained high for both raw uNGAL (0.87 to 0.86) and cr-corrected uNGAL (0.92 to 0.90), with NPV remaining stable (0.82 to 0.82 for raw uNGAL; 0.87 to 0.85 for cr-corrected uNGAL). Conclusion The use of uNGAL, raw or cr-corrected, improved the accuracy of differential diagnosis of AKI in clinical practice by differentiating intrinsic AKI from functional. Specificity was lower at the recommended manufacturer (150 ng/ml) and pediatric (125 ng/ml), but the sensitivity and NPV was high therefore supporting the use to rule-out an intrinsic injury clinically. The presence of UTI does not consistently result in an increase in uNGAL levels.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".