Clinical Implementation of NGAL Testing to Improve Diagnostic Assessment of AKI Episodes in a Canadian Center
Bibliographic record
Abstract
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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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".