JCM Accepts, published online ahead of print on 28 May 2014 J. Clin. Microbiol. doi:10.1128/JCM.00112-14 Copyright © 2014, American Society for Microbiology. All Rights Reserved.
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Detection of Significant Bacteriuria
by Use of the iQ200 Automated Urine Microscope
6 7 8 Enno Stürenburg1*#, Jan Kramer1#, Gerhard Schön2, Georg Cachovan3, Ingo Sobottka1.
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LADR GmbH MVZ Dr. Kramer & Kollegen, Geesthacht, Germany. 2 Department of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Germany. 3 Department of Restorative and Preventive Dentistry, University Medical Center Hamburg-Eppendorf, Germany.
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* Corresponding author. Labor Dr. Staber & Kollegen, 20246 Hamburg, Germany. Tel.: ++ 49 40 600387614. Fax: ++ 49 40 600387622. E-mail: [email protected]
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These authors contributed equally to this work.
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In the microbiology laboratory, there is an augmented need for rapid screening methods for the detection of bacteria in urines since about two thirds of these samples will not yield any bacteria or insignificant growth when cultured. Thus, a reliable screening method can free up laboratory resources and can speed up the reporting of a negative urine result. In this study, we have evaluated the detection of leucocytes, bacteria, and a new sediment indicator, the ‘all small particles’ (ASP), by an automated instrument, the iQ200 urine analyser, to detect negative urine samples that can be excluded from being cultured. A coupled automated strip reader (iChem Velocity), enabling the detection of nitrite and leucocyte esterase, was tested in parallel. In total, 963 urine samples were processed through both, conventional urine culture and the iQ200 / iChem Velocity work station. Using the data, a multivariate regression model was established and for the indicators and their respective combinations (leucocytes + bacteria + ASP; leucocyte esterase + nitrite) the predicted percentage of specificity and the possible reduction in urine cultures were calculated. Among all options, diagnostic performance was best using the whole microscopic content of the sample (leucocytes + bacteria + ASP). By using a cut-off value of ≥ 104 CFU/ml for defining a positive culture, a given sensitivity of 95% resulted in a specificity of 61% and a reduction in urine cultures of 35%. By considering the indicators alone, specificity and the culture savings were both much less satisfactory. The regression model was also used to determine possible cut-off values for running the instrument in daily routine. By using a graphical representation of all combinations possible, we derived cut-off values for leukocytes, bacteria and the ASP count, which should enable the iQ200 microscope to screen out approximately one third of the urine samples, significantly reducing the workload in the microbiology laboratory.
Keywords: Urinary tract infection; bacteriuria screening; automated microscopy; iQ200
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Although traditional culture remains the ‘gold standard’ for the diagnostic assessment of patients suspected of having urinary tract infection (UTI), this methodology is costly, laborious and time consuming, and mostly large proportions of the urine samples sent to the laboratory turn out to be negative. Thus, stepwise strategies in urine analysis have been developed to detect the presence of infection as quickly and as reliably as possible, avoiding unnecessary culture testing, saving patient and laboratory expenses as well as maintaining efficiency within the microbiology department [1-3]. In recent years, the speed of making the diagnosis has gained a special economic attention too, since most European hospitals are subjected to reimbursement systems in which the duration of the patient’s hospital stay is strictly limited. Traditional screening methods, such as dipstick testing for nitrite and leukocytes esterase as well as microscopic sediment analysis for bacteria and white blood cells, are fast but lack sensitivity . Moreover, manually performed methods are laborious and vulnerable to observer variation and imprecision. Thus, in order to reach a more accurate analysis, there have been intensive attempts at exploring automated techniques for a more efficient UTI screening. The automated devices offer a high capacity of particle enumeration and can realize a great degree of labour and time savings when compared to manually performed urine sediments . Several instruments were put on the market with the aim freeing up resources by rejecting negative samples quickly and reliably [2,3]. Nevertheless, results from earlier studies, which were done mostly with the flow cytometers of the Sysmex UF series, thus far have been mixed. While some authors have reported a fairly sufficient performance compared to urine culture [5-10], others completely denied the feasibility of the automated devices as a screening tool, mostly due to an unacceptably large number of false negatives [11,12]. In addition, there is still an on-going debate of what are the best cut-off values to discriminate samples in the positive and negative groups [10,11,13,14].
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Recently, a new automated instrument, the ‘iQ200’ workstation (Iris Diagnostics), has been introduced. The main difference to the flow cytometers is that the urine content is analysed by assessment of digital images of the particles passing in the front of a microscope objective. The microscopic approach results in better performance in identifying the urine content, as there are more indicators on which the analysis can be based on (in the cytometers, there are only two measurement channels, one for bacteria, one for leukocyte detection). In this study, using 963 clinical samples which were sent to the LADR laboratory in Geesthacht (Germany), we compared the detection of bacteria, leukocytes and other urine constituents (‘all small particles’, ASP) of the iQ200 system (and a coupled strip reader, iChem Velocity) to the gold standard, urine culture. The results were analysed using different combinations of the indicators, which made it possible to find the best combination as well as to derive a suitable set of cut-off values.
Material and Methods
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During our study, 963 samples of urine specimens were obtained. The urine specimens were routinely submitted to the microbiology division at the LADR laboratory, Geesthacht, Germany, for bacteriological culture (from March 2011 to May 2011). 788 samples were collected from patients with midstream technique (clean-catch urines), 175 samples were derived from indwelling urinary catheters. Urine was poured into sterile non-preservative tubes (Sarstedt Urine-Monovette, 10 ml) and sent to the microbiology laboratory within 3-6 h after collection, at maximum. Transport was performed in a cold shipping box maintaining a constant 2-8°C environment. After arrival in the laboratory, each sample was divided into two aliquots. From the first aliquot, automated urine analysis was performed using the iChem Velocity instrument (for analyzing the strip chemistries) and the iQ200 digital imaging system (for particle analysis). The indicators measured by iChem Velocity were nitrite and leukocyte esterase, and the iQ200 module assessed were white leukocytes, bacteria and the ‘all small particles’ (ASPs). The ASP application counts all the background particles of less than 3 µm and mainly reflects the presence of “small bacteria”, e.g. gram positive cocci . From the second aliquot, the urine culture was performed by plating out 10 µl of the sample on Columbia blood agar containing colistine-nalidixic acid and on chromogenic agar, the last one for the growth assessment of gram negatives by color changes (Urin 3G-Agar II biplates, heipha Dr. Müller GmbH, Eppelheim, Germany). The plates were incubated under aerobic conditions at 36°C for 16-18 h. For the purpose of this study, we considered a positive culture as one that contained one or two potential uropathogens at a concentration of ≥ 104 CFU/ml as suggested by several authors [7,18,19]. Potential uropathogens were defined as Enterobacteriaceae, Enterococcus species, Streptococcus agalactiae, Pseudomonas species, Candida species, Staphylococcus aureus and non-aureus species. Specimens that yielded growth of ≥ 3 isolates (with no predominating organism) or samples that grew nonpathogens (e.g., Lactobacillus spp., Corynebacterium spp. or Neisseria spp.) were considered contaminated with commensal flora.
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An excel spreadsheet was populated with data, including patient details, presence and quantitation of the chemistries (nitrite and leukocyte esterase) on the strip reader, presence and quantitation of the microscopic indicators (leukocytes, bacteria, and ‘all small particles’), urine culture results, and the types of microorganisms isolated, including yeasts. Bivariate association between leukocytes, nitrite, leukocyte esterase, ASP and bacteria and colony counts were evaluated by estimating Pearson's correlation coefficients with 95% confidence limits and p-values. Statistical analysis of the data was done using the R package . Using the gold standard definition, sensitivity, specificity and the rate of urine samples that can be excluded from culture were calculated for the microscopic indicators alone and their respective combinations. Assessment of the combinations was done using a multiple logistic regression model, followed by an analysis of the receiver operating characteristic (ROC) curves of the predicted probabilities from the combinations [15-17]
SAMPLE CHARACTERISTICS AND CULTURE RESULTS
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Table 1 shows the culture results and the sample characteristics. In total, 515 (53.5%) samples were negative on culture (no growth or a bacterial count of 15 > 1,700 > 20 > 1,400 > 50 > 1,200 > 100 > 1,000 > 150
Scenario b) in the absence of bacteria ‘All small particles’ [pcls/µl] Leukocytes [cells/µl] > 8,000 > 10 > 6,500 > 30 > 5,000 > 75 > 4,000 > 150 > 3,000 > 600 > 2,500 > 1,000 > 2,000 > 2,500
Fig. 1abc Stacked bar charts, leukocyte esterase vs. colony growth (A), nitrite vs. colony growth (B) and bacteria vs. colony growth (C).
Fig. 1de Box plots, leukocytes vs. colony growth (D) and ‚all small particles‘(ASP) vs. colony growth (E).
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Appendix files 1a and 1b Two-dimensional grid patterns representing sensitivities (numerator) and percentage of samples that do not require further culture (denominator) for different combinations of ASP values (y-axis; logarithmic scale) and leukocyte counts (x-axis; logarithmic scale) in the presence of bacteria (appendix file 1a) and in the absence of bacteria (appendix file 1b).
Fig. 2 Comparison, sensitivity (x-axis), specificity (y-axis, left scale) and reject rate of urine samples (yaxis, right scale) at different gold standard definitions, using model 7 (iQ200 automated microscopy).
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