Neutrophil Gelatinase-Associated Lipocalin Measured on Clinical Laboratory Platforms for the Prediction of Acute Kidney Injury and the Associated Need for Dialysis Therapy: A Systematic Review and Meta-analysis
Rationale & Objective: The usefulness of measures of neutrophil gelatinase-associated lipocalin (NGAL) in urine or plasma obtained on clinical laboratory platforms for predicting acute kidney injury (AKI) and AKI requiring dialysis (AKI-D) has not been fully evaluated. We sought to quantitatively summarize published data to evaluate the value of urinary and plasma NGAL for kidney risk prediction. Study Design: Literature-based meta-analysis and individual-study-data meta-analysis of diagnostic studies following PRISMA-IPD guidelines. Setting & Study Populations: Studies of adults investigating AKI, severe AKI, and AKI-D in the setting of cardiac surgery, intensive care, or emergency department care using either urinary or plasma NGAL measured on clinical laboratory platforms. Selection Criteria for Studies: PubMed, Web of Science, Cochrane Library, Scopus, and congress abstracts ever published through February 2020 reporting diagnostic test studies of NGAL measured on clinical laboratory platforms to predict AKI. Data Extraction: Individual-study-data meta-analysis was accomplished by giving authors data specifications tailored to their studies and requesting standardized patient-level data analysis. Analytical Approach: Individual-study-data meta-analysis used a bivariate time-to-event model for interval-censored data from which discriminative ability (AUC) was characterized. NGAL cutoff concentrations at 95% sensitivity, 95% specificity, and optimal sensitivity and specificity were also estimated. Models incorporated as confounders the clinical setting and use versus nonuse of urine output as a criterion for AKI. A literature-based meta-analysis was also performed for all published studies including those for which the authors were unable to provide individual-study data analyses. Results: We included 52 observational studies involving 13,040 patients. We analyzed 30 data sets for the individual-study-data meta-analysis. For AKI, severe AKI, and AKI-D, numbers of events were 837, 304, and 103 for analyses of urinary NGAL, respectively; these values were 705, 271, and 178 for analyses of plasma NGAL. Discriminative performance was similar in both meta-analyses. Individual-study-data meta-analysis AUCs for urinary NGAL were 0.75 (95% CI, 0.73-0.76) and 0.80 (95% CI, 0.79-0.81) for severe AKI and AKI-D, respectively; for plasma NGAL, the corresponding AUCs were 0.80 (95% CI, 0.79-0.81) and 0.86 (95% CI, 0.84-0.86). Cutoff concentrations at 95% specificity for urinary NGAL were >580 ng/mL with 27% sensitivity for severe AKI and >589 ng/mL with 24% sensitivity for AKI-D. Corresponding cutoffs for plasma NGAL were >364 ng/mL with 44% sensitivity and >546 ng/mL with 26% sensitivity, respectively. Limitations: Practice variability in initiation of dialysis. Imperfect harmonization of data across studies. Conclusions: Urinary and plasma NGAL concentrations may identify patients at high risk for AKI in clinical research and practice. The cutoff concentrations reported in this study require prospective evaluation.
|Keywords||Acute kidney injury (AKI), AKI biomarker, AKI prediction, AKI requiring dialysis (AKI-D), cut-off value, diagnostic accuracy, meta-analysis, neutrophil gelatinase-associated lipocalin (NGAL), plasma NGAL, renal replacement therapy (RRT), renal risk assessment, urine NGAL|
|Persistent URL||dx.doi.org/10.1053/j.ajkd.2020.05.015, hdl.handle.net/1765/130355|
|Journal||American Journal of Kidney Diseases|
Albert, C. (Christian), Zapf, A. (Antonia), Haase, M. (Michael), Röver, C. (Christian), Pickering, J.W. (John W.), Albert, A. (Annemarie), … Haase-Fielitz, A. (Anja). (2020). Neutrophil Gelatinase-Associated Lipocalin Measured on Clinical Laboratory Platforms for the Prediction of Acute Kidney Injury and the Associated Need for Dialysis Therapy: A Systematic Review and Meta-analysis. American Journal of Kidney Diseases. doi:10.1053/j.ajkd.2020.05.015