#535 Audit of the clinical implementation of urinary NGAL in the diagnostic work-up of acute kidney injury at an Irish Hospital
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».