Incorporation of Urinary Neutrophil Gelatinase-Associated Lipocalin and Computed Tomography Quantification to Predict Acute Kidney Injury and In-Hospital Death in COVID-19 Patients

Abstract

Background: The 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. Objective: The 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. Methods: A single-center cohort of patients with COVID-19from 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. Results: One 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. Conclusions: This 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.

Elevated Neutrophil Gelatinase-Associated Lipocalin Is Associated With the Severity of Kidney Injury and Poor Prognosis of Patients With COVID-19

“…Our study has a number of limitations. Similar to prior studies 38,39 , we were limited by the use of SCr as the gold standard for AKI. Notably many COVID-19 patients did not have prior health records, making it difficult to establish their baseline SCr values and to detect AKI and calculate its stage.…”

Current Concepts of Pediatric Acute Kidney Injury—Are We Ready to Translate Them into Everyday Practice?

“…It could predict 30-day and 3-month mortality in children with AKI [ 17 ], and its concentration could distinguish between patients at risk of developing AKI as a consequence of nephrotoxicity [ 18 ]. Most recently, uNGAL has shown its usefulness, together with CT-scan, in the assessment of AKI in patients suffering from SARS-CoV 2 infection [ 19 ].…”

Urine Test Predicts Kidney Injury and Death in COVID-19

“…All included cases were diagnosed by positive SARS-CoV-2 PCR test based on nasopharyngeal sampling. The biobank collected residual blood and urine samples after clinical testing from every COVID-19 patient diagnosed at CUIMC, providing us with the largest cohort of COVID-19 patients for urinary biomarker analysis to date 17,18 .…”