Clinical Implementation and Initial Experience of Neutrophil Gelatinase-Associated Lipocalin Testing for the Diagnostic and Prognostic Assessment of Acute Kidney Injury Events in Hospitalized Patients
Bibliographic record
Abstract
INTRODUCTION: The use of novel kidney injury biomarkers has been shown to improve diagnostic assessment and prognostic prediction in various populations with acute kidney injury (AKI), but their use in a standard clinical practice have been rarely reported. METHODS: We reported the clinical implementation of neutrophil gelatinase-associated lipocalin (NGAL) measurement for routine AKI diagnostic workup of patients receiving nephrology consultation in a tertiary academic centre. Specific focus was made on the diagnostic performance to discriminate functional ("pre-renal") from intra-renal AKI and to predict AKI progression. RESULTS: Forty-five urine NGAL (uNGAL) and 25 plasma NGAL (pNGAL) samples in the first 50 consecutive patients were analysed. KDIGO Stage 1, 2, 3 AKI, and renal replacement therapy occurred in 10%, 40%, 50%, and 24% of cases, respectively. The uNGAL was lower in patients with transient AKI (<48 h) and no sign of urinary tract infections (37 [25-167] ng/mL) than sustained or progressive AKI (298 [74-1,308] ng/mL) (p = 0.016), while pNGAL did not discriminate transient (264 [100-373] ng/mL) from persistent AKI (415 [220-816] ng/mL) (p = 0.137). The median uNGAL level was 63 (35-1,123) ng/mL for functional/pre-renal AKI and 451 (177-1,315) ng/mL for intra-renal AKI (p = 0.043), while the pNGAL was 264 (114-468) and 415 (230-816) ng/mL (p = 0.235), respectively. CONCLUSION: NGAL, as part of the routine workup, is useful for diagnostic and prognostic assessment of new-onset AKI in clinical practice. Interpretation of an increased NGAL level should be clinically evaluated in its clinical context, particularly considering concomitant infection (urinary or systemic). Clinical adoption of emerging AKI biomarkers as diagnostic tests in clinical practice should be further encouraged.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".