A New Automated System for Urine Analysis: a Simple, Cost-effective and Reliable Method for Distinguishing Between Glomerular and Nonglomerular Sources of Haematuria

Objective: To determine the ability, reliability and accuracy of a new automated system of urine analysis in differentiating glomerular from nonglomerular bleeding in the initial investigation of haematuria, and compare its efficacy with conventional phase-contrast microscopy (PCM).

Patients And Methods: One hundred and six urine samples from patients in whom the final diagnosis was available were analysed using electrical flow impedance to detect, enumerate and size red blood cells in a conductive fluid (the cellfacts analyser, Microbial Systems Ltd, Coventry, UK). All the samples were also tested using a dipstick method and PCM was carried out for comparison on 45 of the 106 urine specimens. The results of cellfacts analysis were correlated with the final diagnoses to assess sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of this method; the costs were also analysed.

Results: Sixty-nine urine samples tested positive for blood on dipstick urine analysis and all these were confirmed to have red blood cells on cellfacts analysis. The remaining 37 samples were negative for blood on dipstick testing and cellfacts analysis, although seven patients had been referred with previously detected microscopic haematuria, none of whom were found to have any detectable pathology in the urinary tract on clinical examination and investigations. The remaining 30 patients were diagnosed to have urological or nephrological conditions with no haematuria. In the positive group, 20 (29%) patients were from the glomerular group, with a mean (range) red blood cell size of 4.25 (4-5.1) micrometer, and 49 (71%) from the nonglomerular group, with red blood cells of 5.47 (4.67-5.70) micrometer. These ranges overlapped at 4.67-5.1 micrometer at the decision threshold of 4.75 micrometer, the distribution of dysmorphic and eumorphic red blood cells for the glomerular group was 18 (90%) and two (10%), respectively, and for the nonglomerular group was 2 (4%) and 47 (96%), respectively. The sensitivity, specificity, PPV and NPV were 90%, 96%, 90% and 96%, respectively. Consumable and labour costs were very low.

Conclusions: Cellfacts analysis is a simple, rapid, objective and cost-effective method for differentiating glomerular from nonglomerular urinary red blood cells, especially when few such cells are present.