Analysis of the Distribution of Urine Color and Its Relationship With Urine Dry Chemical Parameters Among College Students in Beijing, China - A Cross-Sectional Study

Assessing Concordance of Results: A Comparative Study of the Manual and Automated Urinalysis Methods

Introduction

An accurate urine analysis is a good indicator of the status of the renal and genitourinary system. However, limited studies have been done on comparing the diagnostic performance of the fully automated analyser and manual urinalysis especially in Ghana. This study evaluated the concordance of results of the fully automated urine analyser (Sysmex UN series) and the manual method urinalysis at the Komfo Anokye Teaching Hospital in Kumasi, Ghana. Methodology. Sixty-seven (67) freshly voided urine samples were analysed by the automated urine analyser Sysmex UN series and by manual examination at Komfo Anokye Teaching Hospital, Ghana. Kappa and Bland-Altman plot analyses were used to evaluate the degree of concordance and correlation of both methods, respectively.

Results

Substantial (κ = 0.711, p < 0.01), slight (κ = 0.193, p = 0.004), and slight (κ = 0.109, p < 0.001) agreements were found for urine colour, appearance, and pH, respectively, between the manual and automated methods. A strong and significant correlation (r = 0.593, p < 0.001) was found between both methods for specific gravity with a strong positive linear correlation observed for red blood cell count (r = 0.951, R2 = 0.904, p < 0.001), white blood cell count (r = 0.907, R2 = 0.822, p < 0.001), and epithelial cell count (r = 0.729, R2 = 0.532, p < 0.001). A perfect agreement of urine chemistry results in both methods was observed for nitrite 67 (100%) (κ = 1.000, p < 0.001) with a fair agreement for protein 46 (68.7%) (κ = 0.395, p < 0.001). A strong agreement was found in both methods for the presence of cast 65 (97.0%) (κ = 0.734, p < 0.001) with no concordance observed for the presence of crystals (κ = 0.115, p = 0.326) and yeast-like cells (YLC) (κ = 0.171, p = 0.116).

Conclusion

The automated and manual methods showed similar performances and good correlation, especially for physical and chemical examination. However, manual microscopy remains necessary to classify urine sediments, particularly for bacteria and yeast-like cells. Future research with larger samples could help validate automated urinalysis for wider clinical use and identify areas requiring improved automated detection capabilities.

Rapid discrimination of urine specific gravity using spectroscopy and a modified combination method based on SPA and spectral index

To achieve high-accuracy urine specific gravity discrimination and guide the design of four-waveband multispectral sensors. A modified combination strategy was attempted to be proposed based on the successive projections algorithm (SPA) and the spectral index (SI) in the present study. First, the SPA was used to select four spectral variables in the full spectra. Second, the four spectral variables were mathematically transformed by SI to obtain SI values. Then, SPA gradually fusions the SI values and establishes models to identify USG. The results showed that the SPA can screen out the four characteristic wavelengths related to the measured sample attributes. SIs can be used to improve the performance of constructed prediction models. The best model only involves four spectral variables and 1 SI value, with high accuracy (91.62%), sensitivity (0.9051), and specificity (0.9667). The results reveal that m-SPA-SI can effectively distinguish USG and provide design guidance for 4-wavelength multispectral sensors.

Validation of urine colour L*a*b* for assessing hydration amongst athletes

Objectives

Existing studies have confirmed that urine colour through a urine colour chart is one of the effective indicators for assessing hydration. In recent years, the L*a*b* colour space has been widely used in the objective quantitative analysis of colour. The L*, a* and b* values represent the luminance change from black to white, the chromaticity change from green to red and the chromaticity change from blue to yellow, respectively. This study aimed to examine the validity of the urine colour L*a*b* parameters for assessing the level of hydration amongst athletes.

Methods

The study included a total of 474 young elite athletes (251 males and 223 females, age: 24.59 ± 4.86 years). A total of 803 urine samples were collected from the subjects in various stages of hydration, including morning urine and spot urine sample during rehydration. L*a*b* parameters were measured by spectrophotometer. Hydration status was assessed via urine osmolality and urine specific gravity.

Results

Urine colour b* value has a high correlation with urine specific gravity and urine osmolality (r = 0.811, 0.741, both p < 0.01); L* value has a moderate correlation with urine specific gravity and urine osmolality (r = –0.508, –0.471, both p < 0.01); there was no significant correlation between a* value and urine specific gravity, urine osmolality (p > 0.05). Whether the diagnosis of hypohydration is based on Usg ≥ 1.020 or Uosm ≥ 700 mmol/kg: The AUC of b* values were all above 0.9 and the specificity and sensitivity of b* values were high (both greater than 80%). The AUC of both L* and a* values were less than 0.5. Whether the diagnosis of hyperhydration is based on Usg ≤ 1.010 or Uosm ≤ 500 mmol/kg: The AUC of b* values were all above 0.9 and the specificity and sensitivity of b* value were high (both greater than 90%). The AUC of both L* and a* values were less than 0.5.

Conclusion

These results suggested that the validity of urine colour b* value for assessing hydration amongst athletes was high, however, the validity of urine colour L* and a* values were low.