
Semiquantitative, fully automated urine test strip analysis
Author(s) -
Oyaert Matthijs,
Delanghe Joris R.
Publication year - 2019
Publication title -
journal of clinical laboratory analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.536
H-Index - 50
eISSN - 1098-2825
pISSN - 0887-8013
DOI - 10.1002/jcla.22870
Subject(s) - urine , urinalysis , leukocyte esterase , dipstick , chromatography , albumin , hemoglobin , roche diagnostics , chemistry , medicine
Objectives Urinalysis is one of the most frequently ordered diagnostic laboratory tests. In order to reduce workload and costs, rapid screening tests such as urine test strip analyses are applied. The aim of this study was to evaluate the analytical performance of the UC‐3500 as well as the diagnostic performance in comparison with reference methods. Design and methods We measured within‐run and between‐run imprecision based on quantitative reflectance values. 347 prospectively included urine specimens were investigated for the presence of glucose, protein, albumin, leukocyte esterase, and hemoglobin peroxidase activity, and ordinal scale results were compared to an automated urine particle analyzer (UF‐5000, Sysmex, Kobe, Japan) and wet chemistry (Roche Cobas 8000, Mannheim, Germany). Results Within‐run and between‐run imprecision results based on reflectance data for both the 9 and 11 parameter test strips ranged from 0.07% to 1.36% for the low‐level control and from 0.37% to 6.13% for the high‐level control, depending on the parameter. Regarding diagnostic performance, the sensitivity/specificity for glucose, protein, albumin, leukocyte esterase, and hemoglobin peroxidase was 100/60%, 94.2/88.2%, 81.8/89.2%, 81.7/92.8%, and 85.1/88.6%, respectively; the negative predictive value was 100%, 83.3%, 89.1%, 94.6%, and 96.1%. The Spearman correlation coefficients of the UC‐3500 vs reference methods ranged from 0.915 to 0.967, depending on the parameter. Conclusion This fully automated urine test strip analyzer overall shows a satisfying performance and can reliably screen out negative urine samples in order to focus on further characterization of positive samples in the following steps of the workflow.