ArticleBMC nephrology2026
A longitudinal prospective study investigating serial measurements of plasma neutrophil gelatinase-associated lipocalins (NGAL) for the prediction of chronic kidney disease and acute kidney injury in emergency department patients.
Article in BMC nephrology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Beyond eGFR and Albuminuria: Biological Pathways and Multiomics in Cardiovascular-Kidney-Metabolic Disease.Kidney international reports · 2026Review
- Association between urinary neutrophil gelatinase-associated lipocalin and severe preeclampsia among pregnant women in Kinshasa: a multicenter-based cross-sectional study.BMC pregnancy and childbirth · 2026Article
- Diagnosis-specific associations of plasma NGAL in drug-naïve schizophrenia and major depressive disorder: an exploratory cross-sectional study.BMC psychiatry · 2026Article
- Unpacking KDIGO Guidelines: Prioritizing and Applying Exposures and Susceptibilities for AKI in Clinical Practice.Journal of clinical medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background and hypothesisAcute kidney injury (AKI) and chronic kidney disease (CKD) are associated with high morbidity. Current diagnostic markers, e.g., creatinine (Cr) and estimated glomerular filtration rate (eGFR), lack precision in early kidney disease detection. Neutrophil gelatinase-associated lipocalin (NGAL) has been proposed as a more sensitive biomarker for kidney injury. This study examined the effectiveness of plasma NGAL (pNGAL) as biomarker for CKD and AKI prediction in an emergency department (ED) setting and for predicting the presence of CKD and AKI in future admissions (readmission). The performance was compared to that of C-reactive protein (CRP).
methodsA prospective longitudinal study was conducted at North Zealand University Hospital in Denmark, including 925 patients admitted to the ED. pNGAL and CRP were measured at admission, with follow-up pNGAL measurements for hospitalized patients. CKD and AKI were defined according to KDIGO guidelines. Receiver operating characteristic (ROC) analyses assessed the sensitivity, specificity, and predictive value of pNGAL and CRP, for AKI and CKD, at both admission and readmission. RESULTS AND
conclusionpNGAL is useful for ruling out AKI and CKD, especially at admission, but its low PPV limit its diagnostic accuracy, particularly at readmission. It is more effective as a screening tool rather than a standalone diagnostic marker. Higher predictive performance was observed for pNGAL compared to CRP. For serial pNGAL measurements, CKD patients exhibited higher initial levels at admission, but these differences diminished by day four, with considerable variability observed thereafter.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.