Literature DB >> 25855757

Screening urine samples for the absence of urinary tract infection using the sediMAX automated microscopy analyser.

Rosanne E Sterry-Blunt1, Karen S Randall1, Michael J Doughton1, Sani H Aliyu2, David A Enoch2.   

Abstract

Urinalysis culminates in a workload skew within the clinical microbiology laboratory. Routine processing involves screening via manual microscopy or biochemical dipstick measurement, followed by culture for each sample. Despite this, as many as 80% of specimens are reported as negative; thus, there is vast wastage of resources and time, as well as delayed turnaround time of results as numerous negative cultures fulfil their required incubation time. Automation provides the potential for streamlining sample screening by efficiently (>30% sample exclusion) and reliably [negative predictive value (NPV) ≥ 95%] ruling out those likely to be negative, whilst also reducing resource usage and hands-on time. The present study explored this idea by using the sediMAX automated microscopy urinalysis platform. We prospectively collected and processed 1411 non-selected samples directly after routine laboratory processing. The results from this study showed multiple optimum cut-off values for microscopy. However, although optimum cut-off values permitted rule-out of 40.1% of specimens, an associated 87.5% NPV was lower than the acceptable limit of 95%. Sensitivity and specificity of leukocytes and bacteria in determining urinary tract infection was assessed by receiver operator characteristic curves with area under the curve values found to be 0.697 [95% confidence interval (CI): 0.665-0.729] and 0.587 (95% CI: 0.551-0.623), respectively. We suggested that the sediMAX was not suitable for use as a rule-out screen prior to culture and further validation work must be carried out before routine use of the analyser.

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Year:  2015        PMID: 25855757     DOI: 10.1099/jmm.0.000064

Source DB:  PubMed          Journal:  J Med Microbiol        ISSN: 0022-2615            Impact factor:   2.472


  2 in total

1.  Direct Identification of Urinary Tract Pathogens from Urine Samples, Combining Urine Screening Methods and Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry.

Authors:  Melania Íñigo; Andreu Coello; Gema Fernández-Rivas; Belén Rivaya; Jessica Hidalgo; María Dolores Quesada; Vicente Ausina
Journal:  J Clin Microbiol       Date:  2016-01-27       Impact factor: 5.948

2.  Using artificial intelligence to reduce diagnostic workload without compromising detection of urinary tract infections.

Authors:  Ross J Burton; Mahableshwar Albur; Matthias Eberl; Simone M Cuff
Journal:  BMC Med Inform Decis Mak       Date:  2019-08-23       Impact factor: 2.796

  2 in total

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