Clinical Implementation of NGAL Testing to Improve Diagnostic Assessment of AKI Episodes in a Canadian Center
Notice bibliographique
Résumé
Background: The differential diagnosis of acute kidney injury (AKI) episodes is often challenging. Novel AKI biomarkers have shown their utility to improve prognostic prediction and diagnostic assessment in various research populations but their implementation in standard clinical practice is still rarely reported. Objective: To report the differential diagnostic ability and associated clinical utility of the neutrophil gelatinase-associated lipocalin (NGAL) testing in a real-life setting of a heterogeneous AKI population. Design: This is a retrospective cohort study combined with a clinical audit using questionnaires distributed to consultant nephrologists following NGAL results. Setting: The first 250 consecutive patients with a confirmed AKI where an NGAL test (plasma NGAL [pNGAL] or urine NGAL [uNGAL]) was ordered from a large academic center in Montreal, Canada from January 2021 to August 2021. Patients: Patients were classified into 3 groups based on the final AKI etiology category (functional, intrarenal, and postrenal) following definitive adjudication by 2 independent nephrologists. Methods: The ability of plasma NGAL (pNGAL), urine NGAL (uNGAL), and uNGAL-to-creatinine ratio (uNGAL/Cr) to discriminate intrarenal from functional AKI etiologies was compared to standard urine chemistry (FENa) and proteinuria. A logistic regression was used to evaluate the association between intrarenal AKI and increased biomarker levels. The overall clinical utility and appreciation of the NGAL test was evaluated using a questionnaire completed prospectively by the consultant nephrologist at the time of receiving the NGAL result. The NGAL results were prospectively available to clinicians with a median time of 2.9 (1.3-7.4) hours from the initial order. Results: A total of 214 uNGAL and 44 pNGAL were ordered from 100 functional, 139 intrarenal and 11 postrenal AKI episodes after final adjudication. The discriminative ability of FENa (AUC 0.68 [95% CI: 0.61-0.75]) was lower than uNGAL (AUC 0.80 [95% CI: 0.73-0.86]) and uNGAL/Cr (AUC 0.83 [95% CI: 0.77-0.88]) but better than pNGAL (AUC 0.66 [95% CI: 0.48-0.85]). According to consultant nephrologists, the NGAL testing has led to a change in clinical management in 42% of cases. Limitations: Data reported came from a single center and NGAL was reserved for more complex cases, which limits generalizability. No biopsy has been performed for most AKI cases as the final adjudication was based on a retrospective review of the hospitalization episode. Conclusions: Neutrophil gelatinase-associated lipocalin testing can be successfully integrated as part of the diagnostic workup for AKI in clinical practice. The integration of tubular damage biomarkers to functional biomarkers can further improve the differential diagnostic assessment. However, the impact of such biomarkers on AKI management and associated outcomes still needs further validation.
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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 | 0,002 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».