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Background <p>Efficient urinary tract infection (UTI) diagnostics require reliable methods to rapidly exclude negative samples while accurately identifying positive cases. Automated urine flow cytometry (UFC) offers a promising approach to streamline laboratory workflows by reducing unnecessary cultures and providing early bacterial classification. This study evaluated the performance of a UFC-based screening method compared to conventional urine culture.</p> Methods <p>4005 urine samples were analysed using UFC (Sysmex UF-5000) and compared to culture results. Receiver operating characteristic (ROC) curve analyses assessed method agreement across different patient subpopulations. Predictive values were calculated for the total population and the ten different subpopulations at different cut-offs. Carryover and cross-contamination were also examined.</p> Results <p>Based on our data, we propose an algorithm using bacterial count from UFC to guide urine culture decisions. A cut-off of &lt; 30 BACT cells/µl to support exclusion of negative samples (AUC 0.921) could potentially reduce culture rates by ~ 32%. Due to reduced diagnostic performance in subgroup analyses, pregnant women and children were excluded from the rule-out strategy. UFC also demonstrated high sensitivity and specificity in identifying samples likely to be culture-positive, and a cut-off of &gt; 4000 BACT cells/µl was suggested to indicate clinically relevant bacteriuria (AUC 0.917). Pregnant women were excluded from this rule-in approach due to limited discriminatory performance in this subgroup. Additionally, the BACT-Info flag “Gram Neg?” predicted Gram-negative bacteria in 61% of culture-positive samples, with 96% concordance, offering potential early indication of Gram-negative infections.</p> Conclusions <p>UFC shows potential as a useful screening tool for reducing unnecessary cultures by helping to exclude negative samples and highlighting those likely to be positive. The “Gram Neg?” flag facilitated early differentiation of Gram-negative infections, aiding timely targeted antibiotic therapy. Thus, implementing UFC-based screening in routine diagnostics could reduce laboratory workload while maintaining diagnostic accuracy and patient safety.</p>

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Flow cytometry for screening and prioritisation of urine samples: a retrospective comparison with culture

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月30日

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  • Vicky Sender,
  • John Kerr White,
  • Ludvig Bolinder,
  • Karin Amilon,
  • Mirja Hägg,
  • Karin Haij Bhattarai,
  • Niklas K. Björkström,
  • Baharak Saeedi

摘要

Background

Efficient urinary tract infection (UTI) diagnostics require reliable methods to rapidly exclude negative samples while accurately identifying positive cases. Automated urine flow cytometry (UFC) offers a promising approach to streamline laboratory workflows by reducing unnecessary cultures and providing early bacterial classification. This study evaluated the performance of a UFC-based screening method compared to conventional urine culture.

Methods

4005 urine samples were analysed using UFC (Sysmex UF-5000) and compared to culture results. Receiver operating characteristic (ROC) curve analyses assessed method agreement across different patient subpopulations. Predictive values were calculated for the total population and the ten different subpopulations at different cut-offs. Carryover and cross-contamination were also examined.

Results

Based on our data, we propose an algorithm using bacterial count from UFC to guide urine culture decisions. A cut-off of < 30 BACT cells/µl to support exclusion of negative samples (AUC 0.921) could potentially reduce culture rates by ~ 32%. Due to reduced diagnostic performance in subgroup analyses, pregnant women and children were excluded from the rule-out strategy. UFC also demonstrated high sensitivity and specificity in identifying samples likely to be culture-positive, and a cut-off of > 4000 BACT cells/µl was suggested to indicate clinically relevant bacteriuria (AUC 0.917). Pregnant women were excluded from this rule-in approach due to limited discriminatory performance in this subgroup. Additionally, the BACT-Info flag “Gram Neg?” predicted Gram-negative bacteria in 61% of culture-positive samples, with 96% concordance, offering potential early indication of Gram-negative infections.

Conclusions

UFC shows potential as a useful screening tool for reducing unnecessary cultures by helping to exclude negative samples and highlighting those likely to be positive. The “Gram Neg?” flag facilitated early differentiation of Gram-negative infections, aiding timely targeted antibiotic therapy. Thus, implementing UFC-based screening in routine diagnostics could reduce laboratory workload while maintaining diagnostic accuracy and patient safety.