Screening urine samples for the absence of urinary tract infection using the sediMAX automated microscopy analyser - 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 delaying turn-around time of results as numerous negative cultures fulfill 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 found 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 leucocytes and bacteria in determining urinary tract infection was assessed by receiver operator characteristic (ROC) curves with area under the curve (AUC) found to be 0.697 (95% confidence interval [CI], 0.665 to 0.729) and 0.587 (95% CI, 0.551 to 0.623) respectively. We suggest that the sediMAX is 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.

Written by:
Sterry-Blunt RE, Randall K, Doughton M, Aliyu S, Enoch D.   Are you the author?
Peterborough City Hospital; Stamford Hospitals NHSFT; Cambridge University Hospitals NHSFT.

Reference: J Med Microbiol. 2015 Apr 8. pii: jmm.0.000064.
doi: 10.1099/jmm.0.000064

 
PubMed Abstract
PMID: 25855757

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