ArticleKidney diseases (Basel, Switzerland)2021
Incorporation of Urinary Neutrophil Gelatinase-Associated Lipocalin and Computed Tomography Quantification to Predict Acute Kidney Injury and In-Hospital Death in COVID-19 Patients.
Article in Kidney diseases (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it, 36 citations in OpenAlex.
- Global geographic and socioeconomic disparities in COVID-associated acute kidney injury: a systematic review and meta-analysis.Journal of global health · 2025Pooled it
- Characterization of Risk Prediction Models for Acute Kidney Injury: A Systematic Review and Meta-analysis.JAMA network open · 2023Pooled it
- The Relationship Between Kidney Biomarkers, Inflammation, Severity, and Mortality Due to COVID-19-A Two-Timepoint Study.International journal of molecular sciences · 2025Article
- Neutrophil gelatinase-associated lipocalin as a predictive biomarker of acute kidney injury in COVID-19 infection: A systematic review and meta-analysis.Journal of family medicine and primary care · 2025Article
- Article
- Incorporation of Chest Computed Tomography Quantification to Predict Outcomes for Patients on Hemodialysis with COVID-19.Kidney diseases (Basel, Switzerland) · 2024Article
- Plasma neutrophil gelatinase-associated lipocalin independently predicts dialysis need and mortality in critical COVID-19.Scientific reports · 2024Article
- Serum Cystatin C within 24 hours after admission: a potential predictor for acute kidney injury in Chinese patients with community acquired pneumonia.Renal failure · 2023Article
- SARS-CoV-2 infection induces expression and secretion of lipocalin-2 and regulates iron in a human lung cancer xenograft model.BMB reports · 2023Article
- Acute kidney injury in critical COVID-19 patients: usefulness of urinary biomarkers and kidney proximal tubulopathy.Renal failure · 2023Article
- The role of kidney injury biomarkers in COVID-19.Renal failure · 2022Review
- A Survey on Medical Explainable AI (XAI): Recent Progress, Explainability Approach, Human Interaction and Scoring System.Sensors (Basel, Switzerland) · 2022Review
- Urinary tubular biomarkers as predictors of death in critically ill patients with COVID-19.Biomarkers in medicine · 2022Article
- Predictive Values of Procalcitonin and Presepsin for Acute Kidney Injury and 30-Day Hospital Mortality in Patients with COVID-19.Medicina (Kaunas, Lithuania) · 2022Article
- Role of Urinary Kidney Stress Biomarkers for Early Recognition of Subclinical Acute Kidney Injury in Critically Ill COVID-19 Patients.Biomolecules · 2022Article
- Renal and Inflammation Markers-Renalase, Cystatin C, and NGAL Levels in Asymptomatic and Symptomatic SARS-CoV-2 Infection in a One-Month Follow-Up Study.Diagnostics (Basel, Switzerland) · 2022Article
- Advances in the study of subclinical AKI biomarkers.Frontiers in physiology · 2022Review
- Prevalence and Outcomes Associated with Hyperuricemia in Hospitalized Patients with COVID-19.American journal of nephrology · 2022Article
- Current Concepts of Pediatric Acute Kidney Injury-Are We Ready to Translate Them into Everyday Practice?Journal of clinical medicine · 2021Review
- Pathophysiology and Clinical Manifestations of COVID-19-Related Acute Kidney Injury-The Current State of Knowledge and Future Perspectives.International journal of molecular sciences · 2021Review
Corrections and comments
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Authors and funding
13 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe prevalence of acute kidney injury (AKI) in COVID-19 patients is high, with poor prognosis. Early identification of COVID-19 patients who are at risk for AKI and may develop critical illness and death is of great importance.
objectiveThe aim of this study was to develop and validate a prognostic model of AKI and in-hospital death in patients with COVID-19, incorporating the new tubular injury biomarker urinary neutrophil gelatinase-associated lipocalin (u-NGAL) and artificial intelligence (AI)-based chest computed tomography (CT) analysis.
methodsA single-center cohort of patients with COVID-19 from Wuhan Leishenshan Hospital were included in this study. Demographic characteristics, laboratory findings, and AI-assisted chest CT imaging variables identified on hospital admission were screened using least absolute shrinkage and selection operator (LASSO) and logistic regression to develop a model for predicting the AKI risk. The accuracy of the AKI prediction model was measured using the concordance index (C-index), and the internal validity of the model was assessed by bootstrap resampling. A multivariate Cox regression model and Kaplan-Meier curves were analyzed for survival analysis in COVID-19 patients.
resultsOne hundred seventy-four patients were included. The median (±SD) age of the patients was 63.59 ± 13.79 years, and 83 (47.7%) were men.u-NGAL, serum creatinine, serum uric acid, and CT ground-glass opacity (GGO) volume were independent predictors of AKI, and all were selected in the nomogram. The prediction model was validated by internal bootstrapping resampling, showing results similar to those obtained from the original samples (i.e., 0.958; 95% CI 0.9097-0.9864). The C-index for predicting AKI was 0.955 (95% CI 0.916-0.995). Multivariate Cox proportional hazards regression confirmed that a high u-NGAL level, an increased GGO volume, and lymphopenia are strong predictors of a poor prognosis and a high risk of in-hospital death.
conclusionsThis model provides a useful individualized risk estimate of AKI in patients with COVID-19. Measurement of u-NGAL and AI-based chest CT quantification are worthy of application and may help clinicians to identify patients with a poor prognosis in COVID-19 at an early stage.
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