ArticleScientific reports2020
Urine Neutrophil Gelatinase-associated Lipocalin (NGAL) for Prediction of Persistent AKI and Major Adverse Kidney Events.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 43 citations in OpenAlex.
- Urinary NGAL and trehalase predict end-stage kidney disease in ANCA-associated vasculitis: a prospective cohort study.Renal failure · 2026Article
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- Transition from acute kidney injury to chronic kidney disease: molecular mechanisms and therapeutic interventions.Molecular biomedicine · 2026Review
- Renal Biomarkers of Injury as Early Predictors of COVID-19 Associated Acute Kidney Injury: A Prospective Observational Trial (BRICOAKI Study).Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2026Article
- Thrombomodulin: a potential biomarker for sepsis-associated acute kidney injury resulting from bacterial infections.Frontiers in cellular and infection microbiology · 2026Observational
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- Employing urinary biomarkers in predicting renal recovery three months after in-hospital acute kidney injury.Renal failure · 2025Article
- The use of urinary biomarkers as prognostic tools: predicting kidney outcomes in pediatric acute kidney injury.Pediatric nephrology (Berlin, Germany) · 2025Article
- Early Driving Pressure Is Associated with Major Adverse Kidney Events at 30 Days in ARDS Patients with SARS-CoV-2.Journal of clinical medicine · 2025Article
- Heterogeneity in the definition of major adverse kidney events: a scoping review.Intensive care medicine · 2024Article
- Trajectory pattern of serially measured acute kidney injury biomarkers in critically ill patients: a prospective observational study.Annals of intensive care · 2024Article
- The chemokine XCL1 functions as a pregnancy hormone to program offspring innate anxiety.Brain, behavior, and immunity · 2024Article
- Preoperative urine neutrophil gelatinase-associated lipocalin predicts mortality in colorectal cancer patients after laparoscopic surgery: A single-center study.Health science reports · 2023Article
- Role of platelet to albumin ratio for predicting persistent acute kidney injury in patients admitted to the intensive care unit.BMC anesthesiology · 2023Article
- Plasma Neutrophil Gelatinase-Associated Lipocalin Associates with New-Onset Chronic Kidney Disease in the General Population.Biomolecules · 2023Article
- Association of Wilms tumor-1 protein in urinary exosomes with kidney injury: a population-based cross-sectional study.Frontiers in medicine · 2023Article
- ISCCM Guidelines on Acute Kidney Injury and Renal Replacement Therapy.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2022Article
- Distinct Subtyping of Successful Weaning from Acute Kidney Injury Requiring Renal Replacement Therapy by Consensus Clustering in Critically Ill Patients.Biomedicines · 2022Article
- The Perspectives of Biomarkers in Predicting the Survival of the Renal Graft.Frontiers in pediatrics · 2022Review
- Kidney Cancer and Chronic Kidney Disease: Too Close for Comfort.Biomedicines · 2021Review
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Authors and funding
7 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We aimed to determine whether urinary neutrophil gelatinase-associated lipocalin (uNGAL) can accurately predict persistent AKI, major adverse kidney events at 30 days (MAKE30) and 365 days (MAKE365) in hospitalized AKI patients. This is a retrospective study of adult patients who were admitted at King Chulalongkorn Memorial Hospital. We performed multivariable logistic regression for persistent AKI, MAKE30, and MAKE365. We developed equations for predicting MAKE30 and MAKE365 and divided the dataset into derivation and validation cohorts. uNGAL performance and predictive models were assessed using the area under the receiver operating characteristic curve (AROC). Among 1,322 patients with AKI, 76.9%, 45.1%, and 61.7% had persistent AKI, MAKE30, and MAKE365. The AROC were 0.75 (95% confidence interval[CI] 0.70-0.80), 0.66 (95%CI 0.61-0.71), and 0.64 (95%CI 0.59-0.70) for prediction of persistent AKI, MAKE30, and MAKE365 by uNGAL. The AROC in the validation dataset combining uNGAL with clinical covariates were 0.74 (95%CI 0.69-0.79) and 0.72 (95%CI 0.67-0.77) for MAKE30 and MAKE365. We demonstrated an association between uNGAL and persistent AKI, MAKE30, and MAKE365. Prediction models combining uNGAL can modestly predict MAKE30 and MAKE365. Therefore, uNGAL is a useful tool for improving AKI risk stratification.
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