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Record W4415425537 · doi:10.1093/ndt/gfaf116.0981

#535 Audit of the clinical implementation of urinary NGAL in the diagnostic work-up of acute kidney injury at an Irish Hospital

2025· article· en· W4415425537 on OpenAlexaff
Michael Brad Strader, Ellen Saghie, Bernadine Louis, Candice Fraser, S. Imran, Vladimir Petkov Stoyanov, Tariq Abdullah, Patrick J. Twomey, Jean Côté, Patrick Murray

Bibliographic record

VenueNephrology Dialysis Transplantation · 2025
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsAcute kidney injuryUrinary systemCreatininePopulationAuditNephrologyRetrospective cohort study

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.027
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.019
GPT teacher head0.377
Teacher spread0.358 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Published2025
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