Stefano Bassetti1,2, Adrian Egli3,4,5, Christian Gehringer1,6,2, Axel Regeniter7, Katharina Rentsch8, Sarah Tschudin-Sutter9. 1. University Hospital Basel, Division of Internal Medicine, University of Basel, Basel, Switzerland. 2. University Hospital Basel, Department of Clinical Research, University of Basel, Basel, Switzerland. 3. University Hospital Basel, Division of Clinical Bacteriology and Mycology, University of Basel, Petersgraben 4, 4031, Basel, Switzerland. adrian.egli@usb.ch. 4. University Hospital Basel, Department of Clinical Research, University of Basel, Basel, Switzerland. adrian.egli@usb.ch. 5. Applied Microbiology Research, Department of Biomedicine, University of Basel, Basel, Switzerland. adrian.egli@usb.ch. 6. University Hospital Basel, Division of Clinical Bacteriology and Mycology, University of Basel, Petersgraben 4, 4031, Basel, Switzerland. 7. Current affiliation: Medica Medical Laboratories Dr. F. Käppeli, Wolfbachstrasse 17, Zurich, Switzerland. 8. University Hospital Basel, Division of Clinical Chemistry, University of Basel, Basel, Switzerland. 9. Infectious Diseases and Hospital Epidemiology, University Hospital Basel, University of Basel, Basel, Switzerland.
Abstract
BACKGROUND: Urinary tract infection (UTI) is diagnosed combining urinary symptoms with demonstration of urine culture growth above a given threshold. Our aim was to compare the diagnostic accuracy of Urine Flow Cytometry (UFC) with urine test strip in predicting bacterial growth and in identifying contaminated urine samples, and to derive an algorithm to identify relevant bacterial growth for clinical use. METHODS: Species identification and colony-forming unit (CFU/ml) quantification from bacterial cultures were matched to corresponding cellular (leucocytes/epithelial cells) and bacteria counts per μl. Results comprise samples analysed between 2013 and 2015 for which urine culture (reference standard) and UFC and urine test strip data (index tests, Sysmex UX-2000) were available. RESULTS: 47,572 urine samples of 26,256 patients were analysed. Bacteria counts used to predict bacterial growth of ≥105 CFU/ml showed an accuracy with an area under the receiver operating characteristic curve of > 93% compared to 82% using leukocyte counts. The relevant bacteriuria rule-out cut-off of 50 bacteria/μl reached a negative predictive value of 98, 91 and 89% and the rule-in cut-off of 250 bacteria/μl identified relevant bacteriuria with an overall positive predictive value of 67, 72 and 73% for microbiologically defined bacteriuria thresholds of 105, 104 or 103 CFU/ml, respectively. Measured epithelial cell counts by UFC could not identify contaminated urine. CONCLUSIONS: Prediction of a relevant bacterial growth by bacteria counts was most accurate and was a better predictor than leucocyte counts independently of the source of the urine and the medical specialty ordering the test (medical, surgical or others).
BACKGROUND: Urinary tract infection (UTI) is diagnosed combining urinary symptoms with demonstration of urine culture growth above a given threshold. Our aim was to compare the diagnostic accuracy of Urine Flow Cytometry (UFC) with urine test strip in predicting bacterial growth and in identifying contaminated urine samples, and to derive an algorithm to identify relevant bacterial growth for clinical use. METHODS: Species identification and colony-forming unit (CFU/ml) quantification from bacterial cultures were matched to corresponding cellular (leucocytes/epithelial cells) and bacteria counts per μl. Results comprise samples analysed between 2013 and 2015 for which urine culture (reference standard) and UFC and urine test strip data (index tests, Sysmex UX-2000) were available. RESULTS: 47,572 urine samples of 26,256 patients were analysed. Bacteria counts used to predict bacterial growth of ≥105 CFU/ml showed an accuracy with an area under the receiver operating characteristic curve of > 93% compared to 82% using leukocyte counts. The relevant bacteriuria rule-out cut-off of 50 bacteria/μl reached a negative predictive value of 98, 91 and 89% and the rule-in cut-off of 250 bacteria/μl identified relevant bacteriuria with an overall positive predictive value of 67, 72 and 73% for microbiologically defined bacteriuria thresholds of 105, 104 or 103 CFU/ml, respectively. Measured epithelial cell counts by UFC could not identify contaminated urine. CONCLUSIONS: Prediction of a relevant bacterial growth by bacteria counts was most accurate and was a better predictor than leucocyte counts independently of the source of the urine and the medical specialty ordering the test (medical, surgical or others).
Authors: J Michael Miller; Matthew J Binnicker; Sheldon Campbell; Karen C Carroll; Kimberle C Chapin; Peter H Gilligan; Mark D Gonzalez; Robert C Jerris; Sue C Kehl; Robin Patel; Bobbi S Pritt; Sandra S Richter; Barbara Robinson-Dunn; Joseph D Schwartzman; James W Snyder; Sam Telford; Elitza S Theel; Richard B Thomson; Melvin P Weinstein; Joseph D Yao Journal: Clin Infect Dis Date: 2018-08-31 Impact factor: 9.079
Authors: N Geerts; A R Jansz; K J M Boonen; R P W F Wijn; E L Koldewijn; A K Boer; V Scharnhorst Journal: Clin Chim Acta Date: 2015-06-26 Impact factor: 3.786