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A healthcare provider can diagnose a urinary tract infection (UTI) through several methods:Medical History and Symptoms: The provider will take a detailed medical history and ask about symptoms such as frequent urination, burning sensation during urination, and lower abdominal pain.Urinalysis: A clean-catch urine sample is collected in a sterile container and tested for the presence of bacteria, white blood cells (leukocytes), nitrites, blood, and protein. The presence of leukocytes and...
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Interlaboratory Collaboration for Optimized Screening for Urinary Tract Infection.

Anne Russcher1, Elske Kusters2, Ron Wolterbeek3

  • 1Department of Medical Microbiology, Leiden University Medical Center, Leiden, The Netherlands.

Journal of Clinical Microbiology
|October 23, 2015
PubMed
Summary

Automated urinalysis using the IRIS Diagnostics iQ200 Elite (iQ200) analyzer can effectively screen urine cultures, significantly reducing laboratory workload. This method accurately predicts negative cultures, improving efficiency and providing faster results for clinicians.

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Area of Science:

  • Clinical Microbiology
  • Medical Diagnostics
  • Laboratory Automation

Background:

  • Most urine cultures yield negative results, leading to inefficient laboratory workflows and delayed clinical decisions.
  • Automated urinalysis offers a potential solution for rapid, large-scale screening of urine samples.
  • Accurate prediction of culture outcomes is crucial for optimizing laboratory resource allocation.

Purpose of the Study:

  • To develop and validate a screening algorithm using the IRIS Diagnostics iQ200 Elite (iQ200) analyzer to predict negative urine cultures.
  • To reduce the number of unnecessary bacterial cultures performed in a tertiary university hospital setting.
  • To compare the performance of the iQ200 screening method with Gram stain for assessing sample quality.

Main Methods:

  • 1,442 urine samples submitted for bacterial culture were screened using the iQ200 analyzer.
  • A predictive algorithm was developed based on iQ200 urinalysis parameters, including microorganism presence and leukocyte counts.
  • Logistic regression analysis was employed to optimize the algorithm's sensitivity and negative predictive value.

Main Results:

  • The absence of microorganisms detected by iQ200 screening showed a high sensitivity (90.5%) for predicting negative cultures.
  • An algorithm incorporating leukocyte counts achieved >95% sensitivity (95.2%) and a 97.3% negative predictive value.
  • The iQ200 screening algorithm anticipated a 44% reduction in culture workload.
  • Concordance between iQ200 screening and Gram stain for sample quality assessment was 72%.

Conclusions:

  • Screening urine samples with the iQ200 analyzer is a safe, cost-effective, and efficient method for reducing unnecessary bacterial cultures.
  • The iQ200-based algorithm provides faster results and improves laboratory efficiency.
  • The iQ200 analyzer offers a more objective assessment of sample quality compared to Gram stain.