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

Abstract

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. 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). 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.