Detection of Significant Bacteriuria. by Use of the iq200 Automated Urine Microscope

Transcription

1 JCM Accepts, published online ahead of print on 28 May 2014 J. Clin. Microbiol. doi: /jcm Copyright 2014, American Society for Microbiology. All Rights Reserved Detection of Significant Bacteriuria by Use of the iq200 Automated Urine Microscope Enno Stürenburg 1*#, Jan Kramer 1#, Gerhard Schön 2, Georg Cachovan 3, Ingo Sobottka 1. 1 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. * Corresponding author. Labor Dr. Staber & Kollegen, Hamburg, Germany. Tel.: Fax: e.stuerenburg@staber-kollegen.de # These authors contributed equally to this work. 27 1

2 Abstract 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 10 4 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 2

3 Introduction 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 [4]. 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 [5]. 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]. 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. 3

4 Material and Methods 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 [18]. 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 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 10 4 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. STATISTICAL ANALYSIS 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 [15]. 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] 4

5 Results SAMPLE CHARACTERISTICS AND CULTURE RESULTS 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 <10 4 CFU/ml) and 448 (46.6%) were positive, with bacterial counts of 10 4 CFU/ml. 465 (48.3%) specimens were from hospitalized patients (with a positivity rate of 41.8%) and 498 (51.7%) samples were from outpatients (positivity rate 51.4%). As judged from the species pattern, the most common microorganisms identified were Escherichia coli (n = 304), Enterococcus spp. (n = 177), gram positive bacilli not specified due to small amount of growth (n = 63), Klebsiella spp. (n = 40), gram negative rods not specified due to small amount of growth (n = 33), Proteus spp. (n = 31), coagulase-negative staphylococci (n = 30), Pseudomonas spp. (n = 28), Candida spp. (n = 27), group B streptococci (n = 19), Staphylococcus aureus (n =14), viridans streptococci (n = 5), Citrobacter spp. (n = 4), Serratia marcescens (n = 3). PERFORMANCE OF SINGLE INDICATORS To determine the potential of the single indicators, we first assessed the predictive abilities of the indicators if used alone. As presented in the box plots and stacked bar charts (Fig. 1a to 1e), there was a significant direct relationship between the single indicators measured on the iq200 system (leukocytes, bacteria, and all small particles ) as well as on the ichem Velocity reader (leukocyte esterase and nitrite) and the results of quantitative urine culture. As expected, for each parameter, when taken individually, the values measured by the iq200 instrument and the ichem Velocity were significantly higher in the groups of positive urines (containing 10 4 CFU/ml) than in the groups of negative urines (containing <10 4 CFU/ml) (Fig. 1a to 1e). When transformed and expressed in terms of specificity, however, the single indicators did not all exhibit a useful result except in the case of the all small particles. As presented in Tab. 2, the ASP count is the only individual indicator that developed acceptable specificity, yielding a high number of samples which do not need further culture. For instance, at 95% sensitivity, the ASP channel exhibited a specificity of 44.2% and a 25.8% rate of samples not needing further culture (Tab. 2). PERFORMANCE OF THE INDICATOR COMBINATIONS As with the Iris system multiple indicators are available, it is very interesting to evaluate if a combination of the indicators gets better results for making a diagnosis. Thus, we investigated whether an indicator combination (combination 1, leukocyte esterase + nitrite; combination 2, ASP + bacteria + leukocytes; or combination 3, leukocyte esterase + nitrite + ASP + bacteria + leukocytes) might improve the capabilities of identifying samples that can be excluded from urine culture. In order to assess the indicators combinations, a multiple logistic regression model was established. Using the predicted probabilities which were received from the logistic function, the data were translated into ROC curves and the key statistical figures were derived. As presented in Table 2, the estimated specificities and the percentages of cultures saved when the indicator combinations would have been used as screening tests were calculated as a function of sensitivity (in the range of 91-99%, which is typical for screening purposes). As one can see from the table, the results with the parameter combinations were convincing. For both techniques (automated microscopy and strip testing), there was a clear benefit in going from the single indicators to the combinations. However, 5

6 when comparing the combinations, our calculations showed that the microscopy indicators (ASP + bacteria + leukocytes) allow the prediction of positivity in culture more specifically and with greater chance to reduce the number of unnecessary cultures than using strip testing (leukocyte esterase + nitrite). At sensitivity levels of 95% (99%), we obtained 61% (21.1%) specificity and 34.8% (11.7%) of the samples that do not require further culture in the case of iq200 microscopy, and reached only 35.7% (7.1%) specificity and 21.3% (4.1%) of the samples not requiring further culture with ichem Velocity strip chemistries. Thus, with regard to reducing the number of samples that are urine culture candidates, the iq200 microscopy was clearly superior than using the ichem Velocity strip chemistries. Interestingly, the results of the whole work station iq200 + ichem Velocity (leukocyte esterase + nitrite + ASP + bacteria + leukocytes) were almost identical to that obtained for the better technique alone (microscopy), and thus in this dataset it does not appear that using chemical measurement in addition to microscopy adds substantially to the systems capability to diagnose urinary tract infection. FINDING OPTIMAL CUT-OFF VALUES FOR THE SEDIMENT INDICATORS WHEN USED IN COMBINATION In order to derive cut-offs that give well balanced results in terms of sensitivity/reject rate, we used the data from the logistic regression model of the iq200 microscopy and transformed them in a graphical representation. In order to reduce the complexity, the impact of bacteria has been displayed as scenario a) bacteria present (Appendix file 1a) and scenario b) bacteria absent (Appendix file 1b). This procedure generated two grid patterns of value pairs that were defined by the ASP values (on the y-axis) and the leukocyte counts (on the x-axis). In the figures, each point on the grid represents a double value consisting of a) the predicted sensitivity (numerator) and b) the predicted percentage of samples that do not require further culture (denominator). Finally, using the grid patterns, we identified a suitable set of cut-off value combinations which enable both a sensitivity of greater than 95% and a percentage of samples not needing culture of at least % (Table 3; Appendix file 1 a and 1b). 6

7 Discussion The main objective of using an automated device for the screening of urine samples is to reduce the number of specimens cultured. This in turn allows financial and labor savings, and helps to speed up the reporting of a negative urine sample. The iq200 is a new automated urine analyser which uses a combination of three indicators (leukocytes, bacteria and all small particles ) to diagnose urinary tract infection. Hence there is an additional indicator, the all small particles, on which the diagnosis can be based on, the advanced technique holds promise of better results for reducing the workload within the laboratory than the previous generation of automated urine microscopes. However, since there is still uncertainty of what are the best instrument settings when the parameters are used together [18], we were particularly interested which indicator combinations would give the best diagnostic performances and how much gain could be realized by adjustment. In the beginning, we looked at the manufacturer recommended settings (i.e., bacteria 5++; leukocytes 25; ASP 3000) (Alice Airaud, personal communication), which showed excellent sensitivity 98.9% but did not yield an acceptable culture savings rate (only 21.5%). Thus, we opted to assess various combinations of leukocytes, bacteria and ASP counts to achieve a better performance. The statistical treatment of the data comprised a two-step procedure, first establishing a multivariate regression model and then running a thresholds optimization procedure. This approach enabled us to find those threshold combinations for the indicators that yielded both, a sensitivity of at least 95% and a urine culture savings rate of at least %. Remarkably, this savings rate would not have been possible without the ASP count. This new indicator mainly reflects the presence of Gram-positive bacteria that cannot be well tracked in the 'bacteria' channel. While other studies see no extra value for the all small particles and thus consider this piece of information as dispensable [18], we can clearly claim that this indicator contributes a significant amount to the detection performance of the automated urine microscope. As judged from the performance data of the individual parameters, the ASP count is even the best single indicator among the sediment constituents (Tab. 2). As a main finding of our calculations, it became clear that the suitability of an automated urine device is subject to circumstances, which are beyond the analytical process itself. A key influence is exerted by the threshold value applied to define a positive urine culture. As has already been pointed out approximately % of the urines can be excluded from being cultured with the iq200 microscope. A reduction of one third is of a similar extent as the saving rates that other working groups have recently published. However, this work was done almost exclusively with the flow cytometers of the Sysmex UF Series [6,7,9,10,18,19], and in many of these studies, which were mostly conducted in hospital surroundings [9,12,18-20], the prevalence of UTIs was in the range of 13-30% [8-11,13,18,19]. Our trial gives strong evidence that the automated urine microscopy not only works in the setting of a hospital laboratory, but also is a valuable tool in the non-hospital laboratory, delivering fast screening results to the general practitioners. Furthermore, in our study the prevalence of positive urines, by using a cut-off value of 10 4 CFU/ml, was an above average 46.6%. The possibility for culture savings was thus lower. Given this obstacle the exclusion rate predicted from our calculations ( %) seems fairly good as this corresponds to approximately two thirds (57-67%) of all the negative samples (53.5%) that can be excluded from culture. 7

8 Facing the ongoing discussion of what is suitable in diagnosing urinary tract infection, it is very interesting to see what happens when one changes the thresholds for defining a positive urine culture. In a recent publication with very low thresholds the saving effect was greatly reduced to 20%, of which 14% were false negative results, as compared to 52% (28%) by using a cut-off value of 10 5 ( 10 4 ) CFU/ml [12]. If we had changed the settings in similar fashion like the authors did, we also would observe deterioration of the culture savings to 15% for a decrease in the threshold to 10 3 CFU/ml (Fig. 2). Vice versa, we would observe an improvement to 40% culture savings rate for a threshold of 10 5 CFU/ml (Fig. 2). However, 10 5 CFU/ml is much too high for certain high risk populations (e.g., pregnant women screenings, children, patients with urological disorders, request for repeat culture), in which 10 3 CFU/ml or even lower colony counts could represent significant bacterial infection. Thus automated testing that is tested against higher microbial counts (i.e., 10 4 CFU/ml or 10 5 CFU/ml) must not be applied at all. For the application in a non-hospital laboratory, however, we think that 10 4 CFU/ml is adequate. At least in Germany, most doctors in ambulatory medicine sent 70-80% of their samples from patients, who do not have a fully expressed UTI risk profile, but rather the bladder or the kidneys are only two of several other sources of infection which should be excluded. If specimens that are not amenable to automated testing are strictly excluded, automated testing can reliably be done on the 10 4 CFU/ml level. By using the optimized cut-offs derived from the present study, utilization of the iq200 microscope would decrease the number of bacterial cultures performed by %. Extrapolating these results to approximately 28,000 requests for urine culture/year that our laboratory processes, translates into 7,500-8,750 negative urine cultures that would be avoided. Necessarily, this success is shadowed by the fact that one has to expect a small number of false negatives/per year. We think that this is acceptable if the knowledge about the limitation is carefully communicated to the physicians so that they can react accordingly if needed (e.g. request a repeat culture). Rather, if most general practitioners act upon the results of automated urine testing, which are available almost immediately after the sample has reached the laboratory, the advantages of the iq200 analyser are highlighted as it cannot only reduce the number of negative cultures but also can help to avoid the use of antibiotics. Finally, we feel confident that the statistical approach in this study holds promise of better results than using the default settings. As many studies in the past worked with the cut-off recommendations of the manufacturer, there could have been some underachieving in terms of the culture savings rate that can probably be improved by a thresholds optimization procedure. 8

9 Acknowledgements We are particularly grateful to Monique Vöpel, Hannah Westermann and Jennifer Schenon for providing technical assistance in the evaluation of the iq200 system; and we also thank the technical staff of the LADR microbiology department in Geesthacht, Germany. Competing interests The authors declare that they have no competing interests. Downloaded from on August 23, 2018 by guest 9

10 References 1. European Confederation of Laboratory Medicine (ECLM) European Urinalysis Guidelines. Scand. J. Clin. Lab. Invest. 60: Delanghe J New screening diagnostic techniques in urinalysis. Acta Clin. Belg. 62: Thompson R, Gammie A, Lewis D, Smith R, Edwards C NHS Purchasing and Supply Agency. Evidence review. Automated urine screening systems. jaa&url=http%3a%2f%2fwww.akeah.gov.tr%2fdownload.php%3fd%3d0%26f%3d _CEP10030-Automated-Urine-Screening-Evidence-Review.pdf&ei=g6ScUt-WG- 3cygPeuYBo&usg=AFQjCNG3z12ho4fiFKZ-OiO8Io1ag1aISw&bvm=bv ,d.bGQ 4. Van Nostrand JD, Junkins AD, Bartholdi RK Poor predictive ability of urinanalysis ad microscopic examination to detect urinary tract infection. Am. J. Clin. Pathol. 113: Wang J, Zhang Y, Xu D, Shao W, Lu Y Evaluation of the Sysmex UF-1000i for the diagnosis of urinary tract infection. Am. J. Clin. Pathol. 133: Grosso S, Bruschetta G, De Rosa R, Avolio M, Camporese A Improving the efficiency and efficacy of pre-analytical and analytical work-flow of urine cultures with urinary flow cytometry. New Microbiol. 31: Pieretti B, Brunati P, Pini B, Colzani C, Congedo, P, Rocci M, Terramocci R Diagnosis of bacteriuria and leukocyturia by automated flow cytometry compared with urine culture. J. Clin. Microbiol. 48: Kadkhoda K, Manickam K, Degagne P, Sokolowski P, Pang P, Kontzie N, Alfa M UF- 1000i flow cytometry is an effective screening method for urine specimens. Diagn. Microbiol. Infect. Dis. 69: doi: /j.diagmicrobio Giesen CD, Greeno AM, Thompson KA, Patel R, Jenkins SM, Lieske JC Performance of flow cytometry to screen urine for bacteria and white blood cells prior to urine culture. Clin. Biochem. 46: doi: /j.clinbiochem Epub 2013 Mar De Rosa R, Grosso S, Bruschetta G, Avolio M, Stano P, Modolo ML, Camporese A Evaluation of the Sysmex UF1000i flow cytometer for ruling out bacterial urinary tract infection. Clin. Chim. Acta. 411: doi: /j.cca Epub 2010 Mar Brilha S, Proença H, Cristino JM, Hänscheid T Use of flow cytometry (Sysmex UF-100) to screen for positive urine cultures: in search for the ideal cut-off. Clin. Chem. Lab. Med. 48: Broeren MA, Bahçeci S, Vader HL, Arents NL Screening for urinary tract infection with the Sysmex UF-1000i urine flow cytometer. J. Clin. Microbiol. 49: Jolkkonen S, Paattiniemi EL, Kärpänoja P, Sarkkinen H Screening of urine samples by flow cytometry reduces the need for culture. J. Clin. Microbiol. 48: Manoni F, Fornasiero L, Ercolin M, Tinello A, Ferrian M, Hoffer P, Valverde S, Gessoni G Cutoff values for bacteria and leukocytes for urine flow cytometer Sysmex UF-1000i in urinary tract infections. Diagn. Microbiol. Infect. Dis. 65: R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing,

11 Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, Müller M proc: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 12: Seshan, Venkatraman E. clinfun: Clinical Trial Design and Data Analysis Functions, Parta M, Hudson BY, Le TP, Ittmann M, Musher DM, Stager C IRIS iq200 workstation as a screen for performing urine culture. Diagn. Microbiol. Infect. Dis. 75:5-8. doi: /j.diagmicrobio Epub 2012 Oct Martinez MH, Bottini PV, Levy CE, Garlipp CR UriSed as a screening tool for presumptive diagnosis of urinary tract infection. Clin. Chim. Acta. 425C: doi: /j.cca [Epub ahead of print] 20. Van der Zwet WC, Hessels J, Canbolat F, Deckers MM Evaluation of the Sysmex UF- 1000i urine flow cytometer in the diagnostic work-up of suspected urinary tract infection in a Dutch general hospital. Clin. Chem. Lab. Med. 48: Downloaded from on August 23, 2018 by guest 11

12 323 Tables 12

13 Table 1. Culture results and sample characteristics Samples (n = 963) Specimens growth behavior No growth, negative culture 315 (32.7%) 10 2 CFU/ml non significant growth, negative culture 133 (13.8%) 10 3 CFU/ml non significant growth, negative culture 67 (6.9%) 10 4 CFU/ml significant growth, positive culture 25 (2.6%) 10 5 CFU/ml significant growth, positive culture 71 (7.4%) 10 6 CFU/ml significant growth, positive culture 352 (36.6%) Patients Male 307 (31.9%) Female 656 (68.1%) Hospitalized % rate positive cultures 465 (48.3%) 41.8% Outpatients % rate positive cultures 498 (51.7%) 51.4% Species pattern All urines Escherichia coli 304 (31.6%) Enterococcus spp. 177 (18.4%) Gram positive bacilli, not specified 63 (6.5%) Klebsiella spp. 40 (4.1%) Gram negative rods, not specified 33 (3.4%) Proteus spp. 31 (3.2%) Coagulase-negative staphylococci 30 (3.1%) Pseudomonas spp. 28 (2.9%) Candida spp. 27 (2.8%) Group B streptococci 19 (2.0%) Staphylococcus aureus 14 (1.5%) Viridans streptococci 5 (0.5%) Citrobacter spp. 4 (0.4%) Serratia marcescens 3 (0.3%) 13

14 Table 2. Indicators and combinations: specificities and percentages of samples not needing culture, predicted to a given sensitivity Parameter % SEN URINE CHEMISTRY (ICHEM VELOCITY) Leukocyte esterase % SPE % NNC Nitrite % SPE % NNC Nitrite + LE % SPE % NNC URINE MICROSCOPY (IQ200) All small particles % SPE % NNC Bacteria % SPE % NNC Leukocytes % SPE % NNC ASP + bacteria + % SPE leukocytes % NNC URINE CHEMISTRY + URINE MICROSCOPY iq200 + ichem % SPE Velocity % NNC ASP, all small particles ; LE, leukocyte esterase; % SEN, per cent sensitivity; % SPE, per cent specificity; % NNC, percentage of sample that do not need further culture. Downloaded from on August 23, 2018 by guest 14

15 Table 3. Optimized cut-off values for the iq200 automated urine microscope Scenario a) in the presence of bacteria Scenario b) in the absence of bacteria All small particles [pcls/µl] Leukocytes [cells/µl] All small particles [pcls/µl] Leukocytes [cells/µl] > 2,000 > 15 > 8,000 > 10 > 1,700 > 20 > 6,500 > 30 > 1,400 > 50 > 5,000 > 75 > 1,200 > 100 > 4,000 > 150 > 1,000 > 150 > 3,000 > 600 > 2,500 > 1,000 > 2,000 > 2,500 Pcls, particles. Downloaded from on August 23, 2018 by guest 15

16 Figure legends 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). 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). Downloaded from on August 23, 2018 by guest 16

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