Interlaboratory Collaboration for Optimized Screening for Urinary Tract Infection.
As the majority of urine samples submitted for culture yields a negative result, rapid screening that accurately predicts culture outcome benefits clinicians by reducing the time to result and improves the efficiency of the microbiological laboratory. Automated urinalysis using the IRIS Diagnostics iQ200 Elite (iQ200) analyzer permits just such a fast and large-scale screening. We aimed to predict and thus to reduce negative cultures with a screening algorithm based on iQ200 urinalysis in a tertiary university hospital. In parallel, we evaluated the performance of the iQ200 screen compared to that of Gram stain for sample quality. We screened 1,442 samples submitted for bacterial culture using the iQ200 analyzer; of these samples, 357 (24.8%) had a positive culture result. We identified the absence of microorganisms in the iQ200 screen as the strongest solitary predictor for a negative culture, with a sensitivity of 90.5% (323/357). The algorithm was further improved by performing logistic regression on leukocyte counts, which gave a cutoff of 65 leukocytes/μl to obtain the desired sensitivity of >95% (95.2%; 95% confidence interval [CI], 92.5 to 97.0), a negative predictive value of 97.3% (95% CI, 95.7 to 98.3), and an anticipated culture workload reduction of 44% (95% CI, 41 to 46). Concordance between sample quality based on Gram stain and iQ200 screening was only 72%, which was probably a result of interobserver effect in evaluation of the Gram stain. In conclusion, in our setting, screening by iQ200 proved to be a safe and cost-effective means to provide faster culture results, and it has the added benefit of a more objective evaluation of sample quality.
Department of Medical Microbiology, Leiden University Medical Center, Leiden, The Netherlands.
Anne
A
Russcher
Department of Clinical Chemistry and Laboratory Medicine, Leiden University Medical Center, Leiden, The Netherlands.
Elske
E
Kusters
Department of Biostatistics, Leiden University Medical Center, Leiden, The Netherlands.
Ron
R
Wolterbeek
Department of Medical Microbiology, Leiden University Medical Center, Leiden, The Netherlands.
Ed J
EJ
Kuijper
Department of Clinical Chemistry and Laboratory Medicine, Leiden University Medical Center, Leiden, The Netherlands.
Christa M
CM
Cobbaert
Department of Medical Microbiology, Leiden University Medical Center, Leiden, The Netherlands m.t.van_der_beek@lumc.nl.
Martha T
MT
van der Beek
D000328:Adult; D000368:Aged; D000465:Algorithms; D057205:Automation, Laboratory / Q000379:methods; D003299:Cooperative Behavior; D005260:Female; D006785:Hospitals, University; D006801:Humans; D008297:Male; D008403:Mass Screening / Q000379:methods*; D008875:Middle Aged; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity; D062606:Tertiary Care Centers; D016482:Urinalysis / Q000379:methods; D014552:Urinary Tract Infections / Q000175:diagnosis*