Evidence Hub

Evaluation of diagnostic accuracy of urine neutrophil gelatinase-associated lipocalin in patients with symptoms of urinary tract infections: a meta-analysis

Yin Zhang, Chen Chen, M. Mitsnefes, Bin Huang, Prasad Devarajan

Frontiers in Pediatrics, 2024

8 citations

Abstract

Introduction Early and accurate diagnosis of urinary tract infection (UTI) can prevent serious sequelae including chronic kidney disease. Multiple individual studies have identified urine neutrophil gelatinase-associated lipocalin (uNGAL) as a promising biomarker for early diagnosis of UTI. We sought to understand the distribution and diagnostic accuracy of uNGAL values in patients presenting with UTI symptoms. Methods Our systematic literature reviews in PubMed, Embase, and Cochrane Reviews up to March 2024, identified 25 studies reporting mean/median, standard deviation/quartiles, and detection limits of uNGAL in symptomatic patients with and without culture-confirmed UTI. Seventeen studies were in children. Meta-analyses were performed using the quantile estimation (QE) method estimating the distributions of uNGAL, which were then compared between the UTI and non-UTI groups for identifying the best cut-off points maximizing the Youden index. Sensitivity analyses were performed on all 25 studies including adult patients. Results We found that uNGAL levels were significantly higher in samples with confirmed UTI compared to those without. In pediatric studies, median and 95% confidence interval (CI) of uNGAL values were 22.41 (95% CI of 9.94, 50.54) ng/mL in non-UTI group vs. 118.85 (95% CI of 43.07, 327.97) ng/mL in UTI group. We estimated the cut-off point of 48.43 ng/mL with highest sensitivity (96%) and specificity (97%) in children. Sensitivity analysis including both…

How strong is the evidence?

Evidence Hub reads this as a meta-analysis, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.

How the five levels of evidence work. The level is assigned automatically from the abstract and can be wrong, so check the paper.

Read the paper

Related studies

Go deeper in Evidence Hub

Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.

Open this study Sign up free