Abstract
Objectives
Careful consideration of the pre-analytical process for urine examination is essential to avoid errors and support accurate results and decision-making. Our objective was to assess the impact of various pre-analytical factors on urine test strip and quantitative chemistry results, including stability, tube type, fill volume, and centrifugation.
Methods
Residual random urine specimens were identified. Stability of 10 urine test strips and 13 quantitative chemistry parameters were assessed at eight time points (2, 4, 6, 8, 24, 48, 72, and 96 h) at room temperature (RT) and 2–8 °C (n=10–20 samples). The effect of additional pre-analytical variables was assessed, including using preservative tubes for urine chemistry as well as preservative tube underfilling and centrifugation on urine test strip results (n=10 samples).
Results
Seven of the ten urine tests strips evaluated met the minimal agreement criteria for stability (Cohen’s kappa >0.70) across all conditions. A Cohen’s kappa value of <0.70 was observed for pH (48 h), glucose (72 h), and protein (96 h) at RT. All 13 urine chemistry analytes remained stable at defined time points and conditions. Underfilling preservative tubes for urine test strips and centrifugation demonstrated no significant effect. The impact of using preservative tubes for urine chemistry was negligible with the exception of sodium and osmolality.
Conclusions
These findings highlight the pre-analytical factors that impact urine specimen evaluation and may be useful in informing clinical laboratory practices. Acceptable stability window for urine test strips should be considered in the context of the proportion of pathological samples evaluated.
Acknowledgments
We would like to acknowledge the staff at Toronto General Hospital for their help with sample procurement and instrument time.
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Research ethics: The University Health Network’s Quality Improvement Review Committee determined this study as exempt from further review by the Research Ethics Board.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: The author states no conflict of interest.
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Data availability: Not applicable.
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Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/cclm-2024-1233).
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