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Diagnostics Biochemistry and molecular biology, Clinical care

Improving Flow in Urinalysis

Author C.N. Srinivas.

Urinalysis – a routine test in the clinical laboratory and one whose importance often goes underappreciated. Urine testing can help diagnose and monitor kidney disorders, urological disorders, systemic disorders, inborn errors of metabolism, and much more. The information this simple lab test provides is vital to patients’ clinical management – and, sometimes, even to their survival.

We commonly examine urine for its physical and chemical characteristics – for instance, color, pH, specific gravity, and volume – as well as conducting microscopic examinations in case of infection. These tests can be performed manually or automatically (see Table 1).

Manual urinalysis: 10-15 mL urine, strips must be protected against humidity, visual methods have subjectivity, requires training to intermediate level, microscopy has numerous requirements, reports require transcription, and turnaround time is approximately 20 minutes. Automated urinalysis: 5 mL urine, fully loaded machine can protect against humidity, reflectance spectrometry is objective and standardized, requires training to basic level, microscopy requirements are reduced, machines can transfer reports to LIS automatically, and turnaround time is under seven minutes.
Table 1. Comparing manual and automated urinalysis.

Urinalysis automation has improved test standardization and shortened turnaround times due to integrated information management, with auto-verification rules set by each individual laboratory.

Automate and integrate

Integrated analyzers can test urine samples for pH, specific gravity, and chemistry using reflectance spectrometry; some can also test the protein/creatinine and albumin/creatinine ratios, osmolality, and conductivity of the sample. They are also capable of microscopy (by flow cytometry with auto-particle recognition complemented by digital imaging) to detect cells, pathogens, and more.

Flow cytometry

This method differentially stains the characteristics of elements (size, shape, and nuclei) using fluorescent dyes, then measures the light scattering and fluorescence emission resulting from laser beam stimulation. The results are displayed on scatter plots for analysis.

Flow cytometry allows clear analysis of the distribution of roughly classified elements in non-centrifuged urine. Their levels (counts/μL) can be measured quickly, but the technique’s scope for detailed classification of elements other than red and white blood cells is limited. Further microscopic examination is essential for detailed classification.

Auto-particle recognition

This technology uses digital imaging and auto-particle recognition to classify and quantify urine particles in non-centrifuged urine based on size and shape. The analyzer classifies images into categories including red blood cells, white blood cells, white blood cell clumps, hyaline casts, unclassified casts, epithelial cells, non-squamous epithelial cells, bacteria, yeast, crystals, mucus, sperm, and amorphous substances. Digital images of particles (approximately 500 frames per sample) are collected and can be viewed and reclassified by the operator, potentially without the need for manual microscopy. However, many labs use flag-off criteria and view specimens under microscopy for confirmation.

Digital imaging

Digital imaging is a natural extension of automation – whether flow cytometry or auto-particle recognition. The analyzer uses a digital microscope to take high-power field images, just as they would with a manual microscope. The system automatically analyzes the particles in these images using a neural network-based algorithm and a database with reference images of the 11 individual sediment particle types. The operator can simply evaluate or validate the results directly on the analyzer’s user interface.

If test strips (chemistry, specific gravity, or pH) show a positive result and microscopy is negative, the user must check for false positives and artifacts. If test strips show a negative result, but microscopy is positive, the user must check for red or white blood cells on the strips.

Review criteria based on clinical experience include:

  • Counts over equipment capacity (exceeding linearity)
  • Presence of particles above laboratory-established values
  • Unusual or changed histogram pattern
  • Conductivity disorders with dilute urine
  • Crystals, cells, or casts (flag off)
  • Fungi or yeast (flag off)
  • Parasites suspected on digital image

A lean solution

Adopting automation brings in Lean practices that can improve processing and productivity (see Figure 1 for our experiences at MIOT).

A flowchart depicting workflows before and after automating urinalysis. The "before" flowchart has seven steps; the "after" flowchart has only five.
Figure 1. Workflows at MIOT a) before and b) after automating urinalysis.

Automation offers benefits in multiple areas: laboratory operations and processes; patient safety; and administration.

Laboratory operations and process excellence

  • increased precision, accuracy, and consistency
  • reduced turnaround times
  • standardization
  • reduced issues with storage, tracking, and retrieval
  • 24/7 machine availability with simultaneous reduction in staffing needs
  • more time available for documentation, case study, and training
  • ease of handling
  • fewer patient and physician complaints
  • better stock inventory
  • good IQC and EQAS results (increasing confidence)

Patient safety

  • reduced potential for transcription errors
  • reduced potential for errors caused by fatigue or lack of concentration
  • reduced potential for sample mix-ups and labeling errors
  • closed collection system (improved traceability)
  • shorter patient stays


  • lower staff requirements for equivalent or higher workload
  • rapid return on investment due to reduced staff costs

It seems clear that urinalysis automation offers opportunities to improve accuracy, reduce costs, and standardize testing. It also allows data archiving, which is useful for delta checks and medicolegal purposes. That’s not to say automation is without disadvantages; however, if the user is aware of its limitations – for instance, difficulty with high color or viscosity and failure to detect parasites and suspicious cells – and carefully reviews any flags the analyzer presents, these can be overcome. Combined with the Lean processes it enables, automated urinalysis can take laboratories to the next level in service to their patients. 

A flowchart depicting the evolution of urinalysis from manual chemical analysis through to fully automated urine analyzers.
The evolution of urinalysis.

Sample Case: BK Virus Nephropathy

A 51-year-old male presents for routine follow-up urinalysis after renal transplant with no symptoms.

A screenshot of the patient's urinalysis, remarkable for LEU 3+, P/C 1+, A/C 1+, and X'TAL 59.1/microliter.
RTEC greater than 5 cells/μL requires review.
Microscopic images of decoy cells present in urine.
Decoy cells with optical clear nuclei.
A histologic image of a renal biopsy showing decoy cells.
Renal biopsy shows decoy cells in the renal tubular cells.

The patient was diagnosed with BK virus nephropathy. BK virus is in the polyomavirus group of non-enveloped DNA viruses – a group that also includes JC virus and Merkel cell polyomavirus. These viruses are common in renal allograft patients; the preferred test is urine viral load analysis.

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About the Author
C.N. Srinivas

Director of Laboratory Medicine and Head of Transplantation Immunology and Molecular Diagnostics, MIOT International, Chennai, India.

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