- Email: [email protected]
Performance of the Sysmex UF-1000i urine analyser in the rapid diagnosis of urinary tract infections in hospitalized patients
J Infect Chemother xxx (2016) 1e6
Contents lists available at ScienceDirect
Journal of Infection and Chemotherapy journal homepage: http://www.elsevier.com/locate/jic
Original article
Performance of the Sysmex UF-1000i urine analyser in the rapid diagnosis of urinary tract infections in hospitalized patients Zhian Le, Fengying Li, Chunrong Fei, Aiqing Ye, Xinyou Xie, Jun Zhang* Clinical Laboratory, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310016, China
a r t i c l e i n f o
a b s t r a c t
Article history: Received 11 November 2015 Received in revised form 18 January 2016 Accepted 18 February 2016 Available online xxx
Background: Urinary tract infections (UTIs) are the second most frequently encountered nosocomial infectious diseases, and they greatly increase the cost of medical care and prolong the duration of hospital stays. Aim: We aimed to evaluate the performance of the Sysmex UF-1000i analyser for the rapid prediction of UTIs in hospitalized patients with or without indwelling catheters at a comprehensive teaching hospital that encounters complex disease types. Methods: Urine specimens (n ¼ 1016) were cultured and examined for WBC, RBC, bacteria (BACT) and yeast-like cell (YLC) count using the Sysmex UF-1000i. The results were compared with the urine culture results. Receiver operating characteristic curve analysis was applied to determine BACT and YLC cutoff values for bacterial and fungal UTIs independently. The diagnostic ability of the UF-1000i was also compared for patients with and without indwelling catheters. Findings: A cutoff value of 38.7/mL was acceptable for ruling out bacterial UTIs. In this setting, we achieved a sensitivity of 90%, a negative predictive value of 94.5%, a false negative rate of 2.85% (29 cases), and avoided culturing in 52% of the samples. The BACT count presented a larger area under the curve for patients with indwelling catheters than for those without (0.939 vs. 0.861); however, no significant difference in the diagnostic ability of the two curves was found. Conclusion: The Sysmex UF-1000i analyser could be a reliable method for excluding bacterial UTIs in hospitalized patients with or without urinary catheters and could help clinicians determine whether antibiotic therapy is necessary. © 2016, Japanese Society of Chemotherapy and The Japanese Association for Infectious Diseases. Published by Elsevier Ltd. All rights reserved.
Keywords: Urinary tract infections UF-1000i Nosocomial infections Pathogen diagnostics
1. Introduction Health care-associated infections are important issues at health facilities because they have a high incidence and are responsible for increased morbidity and mortality rates [1]. Urinary tract infections (UTIs) are the second most frequently encountered nosocomial infectious diseases, accounting for approximately 15% of the total health care-associated infections reported by acute care hospitals each year, and they greatly increase the cost of medical care and prolong the duration of hospital stays [2,3]. The gold standard for the diagnosis of UTIs is a quantitative urine culture [4]. However, the urine culture procedure is time consuming, and results cannot be returned to the clinic on the same day. When only UTI-like symptoms occur, some practitioners
* Corresponding author. Tel./fax: þ86 571 86006611. E-mail address: [email protected] (J. Zhang).
prescribe empirical antibiotics before receiving the culture results, which may lead to the antimicrobial resistance of urine tract bacteria [5,6]. Additionally, for ICU patients whose UTI-like symptoms are unclear, routine urine analysis may help clinicians diagnose UTIs early. Because UTIs are very common, a large number of urine cultures are ordered, which significantly increases the workload for the laboratory, even though the culture results for most specimens are negative [7]. Therefore, the development of a quick screening method for negative urine specimens will help clinicians rule out nosocomial UTIs in a much shorter time span and avoid unnecessary laboratory work and antibiotic prescriptions. By utilizing the advanced technology of laser-based fluorescent flow cytometry and a fully automated sample processing system, the Sysmex UF-1000i can differentiate between cell populations and enhance laboratory workflows. Several studies have reported the reliability of UF-1000i for the quick exclusion of UTIs among outpatients in community health services or other health care settings [8e11]. In our current research, the studied population is a
http://dx.doi.org/10.1016/j.jiac.2016.02.009 1341-321X/© 2016, Japanese Society of Chemotherapy and The Japanese Association for Infectious Diseases. Published by Elsevier Ltd. All rights reserved.
Please cite this article in press as: Le Z, et al., Performance of the Sysmex UF-1000i urine analyser in the rapid diagnosis of urinary tract infections in hospitalized patients, J Infect Chemother (2016), http://dx.doi.org/10.1016/j.jiac.2016.02.009
2
Z. Le et al. / J Infect Chemother xxx (2016) 1e6
large number of inpatients with or without indwelling catheters at a comprehensive teaching hospital that encounters complex disease types. The aim of this study was to systematically explore the performance of UF-1000i as a method for diagnosing bacterial or fungal UTIs. Urinary catheterization is the main cause of health care-associated UTI. The current study evaluated the diagnostic ability of the UF-1000i in groups of patients with and without indwelling catheters, which has not been previously reported. 2. Materials and methods 2.1. Patients From September 2010 to March 2011, 1016 urine samples with suspected urinary infections were analysed. All of the samples came from inpatients at Sir Run Run Shaw Hospital, which is a comprehensive university hospital with 1300 beds. Of the 1016 specimens, 543 specimens were from males, and 473 were from females; 486 were from catheterized patients, and 653 samples were from patients who were taking antibiotics within 72 h before sample collection. 2.2. Urine culture Morning midstream urine (15e20 mL) was collected from 530 patients without an indwelling catheter; urine specimen was also collected from 486 catheterized patients, obtained by aspirating from the sampling port of the catheter with a needle and syringe, after disinfecting the area where the needle puncture was made. A total of 1016 urine samples were collected using a disposable, sterile container and submitted to the clinical laboratory within 30 min. Well-mixed urine specimens were submitted to bacterial culture with a 1-mL calibrated loop onto Columbia blood agar plates and with a 10-mL calibrated loop onto selective eosin-methylene rieux, France). All of the plates were incubated blue plates (BioMe at 37 C for 18e24 h, and the numbers of colonies were counted and multiplied by 103 for the Columbia blood agar plates and 102 for the selective eosin-methylene blue plates to determine the number of organisms per millilitre. The culture was considered positive if bacterial counts reached 104 CFU/mL or yeast counts were more than 103 CFU/mL. The VITKE2-Compact automated system (Biorieux, France) was applied for bacterial identification. Samples Me showing the growth of 3 or more types of colonies without a dominant species were classified as mixed flora; these samples were considered culture positive but contaminated and were not subjected to the identification procedure. 2.3. UF-1000i analysis All 1016 urine specimens were analysed for bacterial (BACT), WBC, RBC and YLC counts using a UF-1000i analyser (Sysmex, Kobe, Japan) immediately after the inoculation of the cultures, within 2 h after the collection of specimens. 2.4. Data analysis and statistical methods Statistical analysis was performed with SPSS 16. Urine specimen culture was used as the gold standard technique. The results of the UF-1000i BACT, WBC, RBC, and YLC counts were compared with the urine culture results using receiver operating characteristic (ROC) curve analysis. Different cut-off values were calculated to determine sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the true positive, false negative (FN), true negative and false positive rates. Binary logistic regression analysis was further performed to determine whether other
patient characteristics (e.g., sex, catheter indwelling status, and urine nitrite and protein level) could predict the diagnosis of UTI. The predictive probability was then analysed using the ROC curve to evaluate its diagnostic ability. Differences between the bacterial or fungal culture positive rates of patients with an indwelling catheter and patients without a catheter were compared using the chi-square test. The chi-square test was also used to analyse the difference between the positive nitrite rates in gram-positive and gram-negative bacteria. The UTI-predictive ability of the specimen bacterial and fungi counts grouped by patient catheter status was also analysed using ROC curves, which were then compared using Z tests. P < 0.05 was considered significant. 3. Results 3.1. Urine culture results Among 1016 urine cultures, 411 (40.4%) were positive and 605 were negative. Of the 411 positive samples, 14 were positive for both bacteria and yeast. Thus, a total of 282 (27.8%) specimens were positive for bacteria, and 143 (14.1%) were positive for yeast. Among the 282 bacteria positive cultures, 47 were mixed flora. Of the 235 bacteria-positive cultures, 224 had one species, and 11 had with two or more species. Of the isolated bacteria, 166 were gramnegative and 80 were gram-positive. The bacteria identified in the 282 positive cultures, in descending order of prevalence, were Escherichia coli (n ¼ 67), Klebsiella pneumoniae (n ¼ 40), Proteus mirabilis (n ¼ 28), Enterococcus faecalis (n ¼ 22), Enterococcus faecium (n ¼ 47), Pseudomonsa aeruginosa (n ¼ 13), Enterococcus gallinarum (n ¼ 5), Acinetobacter baumannii (n ¼ 8), Serratia marcescens (n ¼ 2), Staphylococcus (n ¼ 3), Chryseobacterium indologenes (n ¼ 1), Citrobacter freundii (n ¼ 1), Ureaplasma corynebacterium (n ¼ 1), Enterococcus casselif lavus (n ¼ 1), Streptococcus agalactiae (n ¼ 1), Sphingobacterium Spiritivovum (n ¼ 1), and 5 other gram-negative species. The yeasts identified in the 143 positive cultures, in descending order of prevalence, were Candida tropicalis (n ¼ 69), Candida albicans (n ¼ 39), Candida glabrata (n ¼ 13), Candida krusei (n ¼ 9), and 13 other non-C. albicans species. 3.2. UF-1000i performance in screening for UTIs Urine culture was used as the gold standard. The UF-1000i BACT, WBC and RBC count results were compared with the urine culture results using ROC curve analysis. The areas under the curve (AUC) for the BACT, WBC and RBC counts were 0.9 (0.88e0.93), 0.68 (0.64e0.72) and 0.48 (0.45e0.52), respectively (Fig. 1). Different cutoff values for the BACT count were examined, and their sensitivity, specificity, positive and negative predictive value, and false and true positive and negative rates were calculated (Table 1). The best cutoff point was obtained from the ROC curve analysis. The BACT count cutoff value with the highest sensitivity (98%) was 8.6/mL, against which the FN was 7; however, this value had to be excluded because of its low specificity (24%). For simply diagnosing UTIs, the cutoff value of 74.5/mL may be the best among those listed in Table 1; at that value, the sensitivity was 85.2%, the specificity was 80.2%, and the accuracy rate was 81.7%. Nevertheless, the NPV is the most important factor in a screening method, and specificity should be as high as possible. Thus, among the BACT count cutoff values, 38.7/mL was selected because it achieved the best NPV (90.0%) and the lowest FN (29). The quantitative results for YLCs and WBCs obtained with the UF-1000i were compared with results of urine cultures and ROC curves were used to assess these values' ability to predict UTIs caused by fungal yeasts. The AUCs for YLCs and WBCs were 0.836
Please cite this article in press as: Le Z, et al., Performance of the Sysmex UF-1000i urine analyser in the rapid diagnosis of urinary tract infections in hospitalized patients, J Infect Chemother (2016), http://dx.doi.org/10.1016/j.jiac.2016.02.009
Z. Le et al. / J Infect Chemother xxx (2016) 1e6
3
Fig. 1. ROC curve for diagnosing bacterial and fungal UTIs. ROC analysis of bacterial, WBC, RBC and YLC counts on the UF-1000i analyser compared with the quantitative urine culture results for 1016 inpatients to diagnose bacterial and fungal UTIs.
Table 1 Parameters depending on cutoff values for UF-1000i bacterial count in 1016 specimens for diagnosing bacterial UTIs. No. of bacteria (/mL)
Sensitivity (%) Specificity (%) Positive predictive value (%) Negative predictive value (%)
8.6
15.2
38.7
74.5
97.9 24.0 33.2 97.3
95.1 41.1 38.2 95.6
90.5 67.4 51.7 94.5
85.0 80.2 63.8 93.3
(95%CI: 0.791e0.882) and 0.739 (95%CI: 0.698e0.779), respectively (Fig. 1). The sensitivity and specificity of the fungal count for evaluating UTIs were analysed. The results showed that at a cutoff value of 15.2 fungi/mL, we obtained a sensitivity of 72.7%, a specificity of 90% and a false negative rate of 39 cases (Table 2). Binary logistic regressions were attempted, but no significant multivariate models were found. Once adjusted for bacterial count, no other predictors of UTI were significant, which suggested that there were no reliable markers other than bacterial count for significant UTIs. 3.3. UF-1000i performance in predicting UTI in patients with or without an indwelling catheter The patients were organized by catheter status into two groups: those with an indwelling catheter and those without one. The Table 2 Parameters depending on cut-off values for UF-1000i YLCs count in 1016 specimens for diagnosing fungal UTIs.
3.4. UF-1000i performance in predicting UTI in patients taking antibiotics There were 653 (64.3%) patients taking antibiotics before sample collection in our study. The positive urine culture rates for both bacteria and yeast were compared between the two separated groups of patients taking antibiotics or not, and no significant differences were found for bacteria (P > 0.05), however, significant difference of positive culture rate for fungi was found between the two groups (P < 0.05). ROC curves were also used to analyse the predictive ability of BACT in the two groups. The AUC of BACT for diagnosing UTIs was 0.913 and 0.892 respectively. The AUC of YLC for diagnosing fungi UTIs was 0.825 and 0.818 in the two groups (Table 4). The Z test results showed no significant difference between the two AUCs. 3.5. Urine nitrite differences between gram-negative and grampositive bacteria
No. of YLCs (/mL)
Sensitivity (%) Specificity (%) Positive predictive value (%) Negative predictive value (%)
positive urine culture rates for bacteria and fungi were compared between the two groups, and significant differences were found (P < 0.05). Both the bacterial and the fungal positive culture rates were higher in the catheterized patients than in the noncatheterized patients (Table 3). The abilities of BACT and YLC counts to predict UTI in the two groups were analysed using separate ROC curves, and the AUCs were compared using the Z test. The AUC of BACT for diagnosing UTI was 0.936 in the patients with a urinary catheter and 0.861 in the patients without a catheter; the AUCs for the YLC count were 0.824 and 0.838 in the two groups, respectively (Fig. 2). The Z test results showed no significant difference between the 2 AUCs of the two groups.
15.2
67.1
324.3
2579.0
72.7 90.3 54.5 95.3
63.6 95 67.4 94.1
44.1 98 78.8 91.4
13 100 100 87.6
The chi-square test analysis showed that the nitrite-positive rate was significantly different between gram-positive and gramnegative bacteria (Table 5). Among the 79 nitrite-positive strains, 72 were gram-negative bacteria (91.1%). E. coli was the most
Please cite this article in press as: Le Z, et al., Performance of the Sysmex UF-1000i urine analyser in the rapid diagnosis of urinary tract infections in hospitalized patients, J Infect Chemother (2016), http://dx.doi.org/10.1016/j.jiac.2016.02.009
4
Z. Le et al. / J Infect Chemother xxx (2016) 1e6
Table 3 Comparison of catheterized and regular urine for urine culture positive rate of 1016 inpatients [n (%)]. Indwelling catheter
No Yes Total
Urine culture for bacteria Neg
Pos
406 (39.9) 328 (32.3) 734 (72.2)
124 (12.2) 158 (15.6) 282 (27.8)
c2
10.502
frequently isolated strain with positive nitrites, and K. pneumoniae was the second most common strain. 4. Discussions In this study, we aimed to evaluate the performance of UF-1000i in the rapid diagnosis of UTIs among inpatients with purpose of
P
Urine culture for yeast
P < 0.05
Neg
Pos
486 (47.9) 387 (38.1) 873 (86.0)
44 (4.3) 99 (9.7) 143 (14.0)
c2
P
30.532
P < 0.05
shortening the turn-around time and allowing early decisions regarding the need for antibiotic treatment, thus ultimately improving health care quality and reducing costs. Considering that the studied population comprised inpatients with complex disease types, and understanding that the reduced growth of microorganisms might have clinical relevance, we classified cultures with counts 104 CFU/ml as positive. The results showed that the
Fig. 2. ROC curve for diagnosing bacterial and fungal UTIs in patients with or without indwelling catheters. ROC curves of bacterial or YLC counts on the UF-1000i analyser compared with the quantitative urine culture results for 486 catheterized and 530 non-catheterized inpatients to diagnose bacterial and fungal UTIs.
Please cite this article in press as: Le Z, et al., Performance of the Sysmex UF-1000i urine analyser in the rapid diagnosis of urinary tract infections in hospitalized patients, J Infect Chemother (2016), http://dx.doi.org/10.1016/j.jiac.2016.02.009
Z. Le et al. / J Infect Chemother xxx (2016) 1e6
5
Table 4 Comparison of urine with and without antibiotic taking for urine culture positive rate of 1016 inpatients [n (%)]. Antibiotic treatment
No Yes Total
c2
Urine culture for bacteria Neg
Pos
251 (24.7) 483 (47.5) 734 (72.2)
112 (11) 170 (16.8) 282 (27.8)
2.70
positive rate of bacterial UTIs was 27.8%, which was similar to existing data [8,10]. ROC analysis showed that the AUC of BACT was 0.91, which represented a potential diagnostic value. To determine the best cutoff point for ruling out UTIs, we analysed a series of BACT cutoff values. We found that at a cutoff value of 38.7/mL, the sensitivity for UTIs was 90.5% and the NPV 94.5%, with 29 (2.9%) FN cases. The best cutoff value obtained in our study is slightly different from those obtained in other studies that used the same type of Sysmex instrument; those studies reported a higher NPV and fewer FNs. Rita De Rosa et al., whose subjects were primarily outpatients, found that a BACT count of 170/mL produced an NPV of 98.9%; Fabio Manoni et al., whose patients' origins were not described, reported an NPV of 98% by BACT count of 125/mL [9,10]. The difference between these studies and ours might be based on the complexity of the patients' disease types and on divergent study designs, as some studies used different urine culture cutoff values. In Marschal et al.'s study, which included a relatively small number of inpatients, the authors did not recommend the rapid diagnosis of UTIs in a complex population of hospitalized patients [12]. However, in current study, we demonstrated that using the cutoff value that we determined, 52% of specimens could be diagnosed as negative for UTIs using the UF-1000i and thereby bypassing urine culture, and only 2.9% of the results were false negatives. In some studies, WBC was considered an effective predictor of UTI, especially when combined with BACT [9,10,13]. In the present study, we attempted to construct a multivariate model for predicting UTIs; however, no significant predictors other than BACT were found. The inconsistency between the reliability of BACT and WBC in predicting UTIs might be related to the continuing growth of bacteria over time and the cellular collapse of WBCs over time. The reduced value of WBC counts for predicting UTIs was also reported by Boonen et al. [11]. In this study, 143 patients had fungal UTIs, the majority of which were caused by Candida species. Candida species were the seventh most common nosocomial pathogen reported by the National Nosocomial Infection Surveillance system of the US Centers for Disease Control and Prevention in the 1980s; they were the fourth most common pathogen when studies were limited to intensive care units, where most patients had a urinary catheter [14,15]. Our results demonstrated that patients with urinary catheters are more likely to develop bacterial or fungal UTIs compared with patients without catheters. To date, no studies have analysed the cutoff value of yeast cell counts determined using the UF-1000i for diagnosing fungal UTIs. In the current study, the YLC counts determined with the UF-1000i were not an adequate indicator for
P
Urine culture for yeast
P > 0.05
Neg
Pos
348 (34.3) 525 (51.7) 873 (86.0)
15 (1.4) 128 (12.6) 143 (14.0)
c2
P
46.166
P > 0.05
the rapid screening of fungal UTIs. However, our results showed that when the YLC count is >15.2/mL, the odds that the patient has a fungal UTI could be as high as 90%, which could prompt health care practitioners to change the catheter without awaiting urine culture results. Urinary catheterization is the main cause of health careassociated urinary tract infection [16]. In this study, we demonstrated that both bacterial and fungal positive culture rates were higher in catheterized patients than in non-catheterized patients. Next, we assessed the ability of different BACT and YLC cutoff values to predict UTIs in the groups with and without urinary catheters. BACT did obtain a better AUC for patients with indwelling catheters than for those without catheters (0.939 vs. 0.861); however, the two curves showed no significant diagnostic ability. In the present study, no significant difference was found either on positive culture rate or on AUCs between the patients that were taking antibiotics or not. Thus, antibiotics treatment does not undermine the predictive ability of BACT to diagnose UTIs compared to urine culture. Additionally, according to Boonen et al.'s report, none of the false negative cases were patients being treated with antibiotics [11]. Nevertheless, they studied a relatively small number of patients, and further research is needed. In our study, we estimated the role of urine nitrite tests in predicting the class of organism causing a UTI and found that 91.1% of gram-negative bacteria yielded a positive urine nitrite test. Thus, gram-negative organisms should be highly suspected when urine nitrite is positive. This information could be helpful to clinicians deciding whether to prescribe antibiotic treatment for an ambulatory patient with a UTI. In conclusion, the BACT count determined with the UF-1000i analyser would be a reliable platform for ruling out bacterial UTIs in hospitalized patients with or without urinary catheters and could also facilitate clinicians determine whether antibiotic therapy is necessary. Conflict of interest We declare that we have no conflicts of interest. Acknowledgements This study was supported by a grant from Medical Science and Technology Planning Project of Zhejiang Province in China (No. 2014PYA011). References
Table 5 Urine nitrite differences between gram-negative and gram-positive bacteria. Nitrite
Pos Neg Total
c
P
27.396
P < 0.05
2
Strain Gram pos
Gram neg
7 66 73
72 90 162
[1] Klevens RM, Edwards JR, Richards Jr CL, Horan TC, Gaynes RP, Pollock DA, et al. Estimating health care-associated infections and deaths in U.S. hospitals, 2002. Public Health Rep 2007;122:160e6. [2] Prevention. CfDCa. Catheter-Associated Urinary Tract Infection (CAUTI) event. January 2014. 2014, www.cdc.gov/nhsn/PDFs/pscManual/7pscCAUTIcurrent. pdf. [3] Magill SS, Hellinger W, Cohen J, Kay R, Bailey C, Boland B, et al. Prevalence of healthcare-associated infections in acute care hospitals in Jacksonville, Florida. Infect Control Hosp Epidemiol 2012;33:283e91.
Please cite this article in press as: Le Z, et al., Performance of the Sysmex UF-1000i urine analyser in the rapid diagnosis of urinary tract infections in hospitalized patients, J Infect Chemother (2016), http://dx.doi.org/10.1016/j.jiac.2016.02.009
6
Z. Le et al. / J Infect Chemother xxx (2016) 1e6
[4] Glass JI, Lefkowitz EJ, Glass JS, Heiner CR, Chen EY, Cassell GH. The complete sequence of the mucosal pathogen Ureaplasma urealyticum. Nature 2000;407: 757e62. [5] Wagenlehner FM, Hoyme U, Kaase M, Funfstuck R, Naber KG, Schmiemann G. Uncomplicated urinary tract infections. Dtsch Arztebl Int 2011;108:415e23. [6] French GL. The continuing crisis in antibiotic resistance. Int J Antimicrob Agents 2010;36(Suppl. 3):S3e7. [7] Okada H, Sakai Y, Miyazaki S, Arakawa S, Hamaguchi Y, Kamidono S. Detection of significant bacteriuria by automated urinalysis using flow cytometry. J Clin Microbiol 2000;38:2870e2. [8] Kim SY, Kim YJ, Lee SM, Hwang SH, Kim HH, Son HC, et al. Evaluation of the Sysmex UF-100 urine cell analyzer as a screening test to reduce the need for urine cultures for community-acquired urinary tract infection. Am J Clin Pathol 2007;128:922e5. [9] Manoni F, Fornasiero L, Ercolin M, Tinello A, Ferrian M, Hoffer P, et al. Cutoff values for bacteria and leukocytes for urine flow cytometer Sysmex UF-1000i in urinary tract infections. Diagn Microbiol Infect Dis 2009;65:103e7. [10] De Rosa R, Grosso S, Bruschetta G, Avolio M, Stano P, Modolo ML, et al. Evaluation of the Sysmex UF1000i flow cytometer for ruling out bacterial urinary tract infection. Clin Chim Acta 2010;411:1137e42.
[11] Boonen KJ, Koldewijn EL, Arents NL, Raaymakers PA, Scharnhorst V. Urine flow cytometry as a primary screening method to exclude urinary tract infections. World J Urol 2013;31:547e51. [12] Marschal M, Wienke M, Hoering S, Autenrieth IB, Frick JS. Evaluation of 3 different rapid automated systems for diagnosis of urinary tract infections. Diagn Microbiol Infect Dis 2012;72:125e30. [13] 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 2013;46:810e3. [14] Jarvis WR, Martone WJ. Predominant pathogens in hospital infections. J Antimicrob Chemother 1992;29:19e24. [15] Jarvis WR, Edwards JR, Culver DH, Hughes JM, Horan T, Emori TG, et al. Nosocomial infection rates in adult and pediatric intensive care units in the United States. National Nosocomial Infections Surveillance System. Am J Med 1991;91:185Se91S. [16] Tambyah PA, Oon J. Catheter-associated urinary tract infection. Curr Opin Infect Dis 2012;25:365e70.
Please cite this article in press as: Le Z, et al., Performance of the Sysmex UF-1000i urine analyser in the rapid diagnosis of urinary tract infections in hospitalized patients, J Infect Chemother (2016), http://dx.doi.org/10.1016/j.jiac.2016.02.009
Contents lists available at ScienceDirect
Journal of Infection and Chemotherapy journal homepage: http://www.elsevier.com/locate/jic
Original article
Performance of the Sysmex UF-1000i urine analyser in the rapid diagnosis of urinary tract infections in hospitalized patients Zhian Le, Fengying Li, Chunrong Fei, Aiqing Ye, Xinyou Xie, Jun Zhang* Clinical Laboratory, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310016, China
a r t i c l e i n f o
a b s t r a c t
Article history: Received 11 November 2015 Received in revised form 18 January 2016 Accepted 18 February 2016 Available online xxx
Background: Urinary tract infections (UTIs) are the second most frequently encountered nosocomial infectious diseases, and they greatly increase the cost of medical care and prolong the duration of hospital stays. Aim: We aimed to evaluate the performance of the Sysmex UF-1000i analyser for the rapid prediction of UTIs in hospitalized patients with or without indwelling catheters at a comprehensive teaching hospital that encounters complex disease types. Methods: Urine specimens (n ¼ 1016) were cultured and examined for WBC, RBC, bacteria (BACT) and yeast-like cell (YLC) count using the Sysmex UF-1000i. The results were compared with the urine culture results. Receiver operating characteristic curve analysis was applied to determine BACT and YLC cutoff values for bacterial and fungal UTIs independently. The diagnostic ability of the UF-1000i was also compared for patients with and without indwelling catheters. Findings: A cutoff value of 38.7/mL was acceptable for ruling out bacterial UTIs. In this setting, we achieved a sensitivity of 90%, a negative predictive value of 94.5%, a false negative rate of 2.85% (29 cases), and avoided culturing in 52% of the samples. The BACT count presented a larger area under the curve for patients with indwelling catheters than for those without (0.939 vs. 0.861); however, no significant difference in the diagnostic ability of the two curves was found. Conclusion: The Sysmex UF-1000i analyser could be a reliable method for excluding bacterial UTIs in hospitalized patients with or without urinary catheters and could help clinicians determine whether antibiotic therapy is necessary. © 2016, Japanese Society of Chemotherapy and The Japanese Association for Infectious Diseases. Published by Elsevier Ltd. All rights reserved.
Keywords: Urinary tract infections UF-1000i Nosocomial infections Pathogen diagnostics
1. Introduction Health care-associated infections are important issues at health facilities because they have a high incidence and are responsible for increased morbidity and mortality rates [1]. Urinary tract infections (UTIs) are the second most frequently encountered nosocomial infectious diseases, accounting for approximately 15% of the total health care-associated infections reported by acute care hospitals each year, and they greatly increase the cost of medical care and prolong the duration of hospital stays [2,3]. The gold standard for the diagnosis of UTIs is a quantitative urine culture [4]. However, the urine culture procedure is time consuming, and results cannot be returned to the clinic on the same day. When only UTI-like symptoms occur, some practitioners
* Corresponding author. Tel./fax: þ86 571 86006611. E-mail address: [email protected] (J. Zhang).
prescribe empirical antibiotics before receiving the culture results, which may lead to the antimicrobial resistance of urine tract bacteria [5,6]. Additionally, for ICU patients whose UTI-like symptoms are unclear, routine urine analysis may help clinicians diagnose UTIs early. Because UTIs are very common, a large number of urine cultures are ordered, which significantly increases the workload for the laboratory, even though the culture results for most specimens are negative [7]. Therefore, the development of a quick screening method for negative urine specimens will help clinicians rule out nosocomial UTIs in a much shorter time span and avoid unnecessary laboratory work and antibiotic prescriptions. By utilizing the advanced technology of laser-based fluorescent flow cytometry and a fully automated sample processing system, the Sysmex UF-1000i can differentiate between cell populations and enhance laboratory workflows. Several studies have reported the reliability of UF-1000i for the quick exclusion of UTIs among outpatients in community health services or other health care settings [8e11]. In our current research, the studied population is a
http://dx.doi.org/10.1016/j.jiac.2016.02.009 1341-321X/© 2016, Japanese Society of Chemotherapy and The Japanese Association for Infectious Diseases. Published by Elsevier Ltd. All rights reserved.
Please cite this article in press as: Le Z, et al., Performance of the Sysmex UF-1000i urine analyser in the rapid diagnosis of urinary tract infections in hospitalized patients, J Infect Chemother (2016), http://dx.doi.org/10.1016/j.jiac.2016.02.009
2
Z. Le et al. / J Infect Chemother xxx (2016) 1e6
large number of inpatients with or without indwelling catheters at a comprehensive teaching hospital that encounters complex disease types. The aim of this study was to systematically explore the performance of UF-1000i as a method for diagnosing bacterial or fungal UTIs. Urinary catheterization is the main cause of health care-associated UTI. The current study evaluated the diagnostic ability of the UF-1000i in groups of patients with and without indwelling catheters, which has not been previously reported. 2. Materials and methods 2.1. Patients From September 2010 to March 2011, 1016 urine samples with suspected urinary infections were analysed. All of the samples came from inpatients at Sir Run Run Shaw Hospital, which is a comprehensive university hospital with 1300 beds. Of the 1016 specimens, 543 specimens were from males, and 473 were from females; 486 were from catheterized patients, and 653 samples were from patients who were taking antibiotics within 72 h before sample collection. 2.2. Urine culture Morning midstream urine (15e20 mL) was collected from 530 patients without an indwelling catheter; urine specimen was also collected from 486 catheterized patients, obtained by aspirating from the sampling port of the catheter with a needle and syringe, after disinfecting the area where the needle puncture was made. A total of 1016 urine samples were collected using a disposable, sterile container and submitted to the clinical laboratory within 30 min. Well-mixed urine specimens were submitted to bacterial culture with a 1-mL calibrated loop onto Columbia blood agar plates and with a 10-mL calibrated loop onto selective eosin-methylene rieux, France). All of the plates were incubated blue plates (BioMe at 37 C for 18e24 h, and the numbers of colonies were counted and multiplied by 103 for the Columbia blood agar plates and 102 for the selective eosin-methylene blue plates to determine the number of organisms per millilitre. The culture was considered positive if bacterial counts reached 104 CFU/mL or yeast counts were more than 103 CFU/mL. The VITKE2-Compact automated system (Biorieux, France) was applied for bacterial identification. Samples Me showing the growth of 3 or more types of colonies without a dominant species were classified as mixed flora; these samples were considered culture positive but contaminated and were not subjected to the identification procedure. 2.3. UF-1000i analysis All 1016 urine specimens were analysed for bacterial (BACT), WBC, RBC and YLC counts using a UF-1000i analyser (Sysmex, Kobe, Japan) immediately after the inoculation of the cultures, within 2 h after the collection of specimens. 2.4. Data analysis and statistical methods Statistical analysis was performed with SPSS 16. Urine specimen culture was used as the gold standard technique. The results of the UF-1000i BACT, WBC, RBC, and YLC counts were compared with the urine culture results using receiver operating characteristic (ROC) curve analysis. Different cut-off values were calculated to determine sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the true positive, false negative (FN), true negative and false positive rates. Binary logistic regression analysis was further performed to determine whether other
patient characteristics (e.g., sex, catheter indwelling status, and urine nitrite and protein level) could predict the diagnosis of UTI. The predictive probability was then analysed using the ROC curve to evaluate its diagnostic ability. Differences between the bacterial or fungal culture positive rates of patients with an indwelling catheter and patients without a catheter were compared using the chi-square test. The chi-square test was also used to analyse the difference between the positive nitrite rates in gram-positive and gram-negative bacteria. The UTI-predictive ability of the specimen bacterial and fungi counts grouped by patient catheter status was also analysed using ROC curves, which were then compared using Z tests. P < 0.05 was considered significant. 3. Results 3.1. Urine culture results Among 1016 urine cultures, 411 (40.4%) were positive and 605 were negative. Of the 411 positive samples, 14 were positive for both bacteria and yeast. Thus, a total of 282 (27.8%) specimens were positive for bacteria, and 143 (14.1%) were positive for yeast. Among the 282 bacteria positive cultures, 47 were mixed flora. Of the 235 bacteria-positive cultures, 224 had one species, and 11 had with two or more species. Of the isolated bacteria, 166 were gramnegative and 80 were gram-positive. The bacteria identified in the 282 positive cultures, in descending order of prevalence, were Escherichia coli (n ¼ 67), Klebsiella pneumoniae (n ¼ 40), Proteus mirabilis (n ¼ 28), Enterococcus faecalis (n ¼ 22), Enterococcus faecium (n ¼ 47), Pseudomonsa aeruginosa (n ¼ 13), Enterococcus gallinarum (n ¼ 5), Acinetobacter baumannii (n ¼ 8), Serratia marcescens (n ¼ 2), Staphylococcus (n ¼ 3), Chryseobacterium indologenes (n ¼ 1), Citrobacter freundii (n ¼ 1), Ureaplasma corynebacterium (n ¼ 1), Enterococcus casselif lavus (n ¼ 1), Streptococcus agalactiae (n ¼ 1), Sphingobacterium Spiritivovum (n ¼ 1), and 5 other gram-negative species. The yeasts identified in the 143 positive cultures, in descending order of prevalence, were Candida tropicalis (n ¼ 69), Candida albicans (n ¼ 39), Candida glabrata (n ¼ 13), Candida krusei (n ¼ 9), and 13 other non-C. albicans species. 3.2. UF-1000i performance in screening for UTIs Urine culture was used as the gold standard. The UF-1000i BACT, WBC and RBC count results were compared with the urine culture results using ROC curve analysis. The areas under the curve (AUC) for the BACT, WBC and RBC counts were 0.9 (0.88e0.93), 0.68 (0.64e0.72) and 0.48 (0.45e0.52), respectively (Fig. 1). Different cutoff values for the BACT count were examined, and their sensitivity, specificity, positive and negative predictive value, and false and true positive and negative rates were calculated (Table 1). The best cutoff point was obtained from the ROC curve analysis. The BACT count cutoff value with the highest sensitivity (98%) was 8.6/mL, against which the FN was 7; however, this value had to be excluded because of its low specificity (24%). For simply diagnosing UTIs, the cutoff value of 74.5/mL may be the best among those listed in Table 1; at that value, the sensitivity was 85.2%, the specificity was 80.2%, and the accuracy rate was 81.7%. Nevertheless, the NPV is the most important factor in a screening method, and specificity should be as high as possible. Thus, among the BACT count cutoff values, 38.7/mL was selected because it achieved the best NPV (90.0%) and the lowest FN (29). The quantitative results for YLCs and WBCs obtained with the UF-1000i were compared with results of urine cultures and ROC curves were used to assess these values' ability to predict UTIs caused by fungal yeasts. The AUCs for YLCs and WBCs were 0.836
Please cite this article in press as: Le Z, et al., Performance of the Sysmex UF-1000i urine analyser in the rapid diagnosis of urinary tract infections in hospitalized patients, J Infect Chemother (2016), http://dx.doi.org/10.1016/j.jiac.2016.02.009
Z. Le et al. / J Infect Chemother xxx (2016) 1e6
3
Fig. 1. ROC curve for diagnosing bacterial and fungal UTIs. ROC analysis of bacterial, WBC, RBC and YLC counts on the UF-1000i analyser compared with the quantitative urine culture results for 1016 inpatients to diagnose bacterial and fungal UTIs.
Table 1 Parameters depending on cutoff values for UF-1000i bacterial count in 1016 specimens for diagnosing bacterial UTIs. No. of bacteria (/mL)
Sensitivity (%) Specificity (%) Positive predictive value (%) Negative predictive value (%)
8.6
15.2
38.7
74.5
97.9 24.0 33.2 97.3
95.1 41.1 38.2 95.6
90.5 67.4 51.7 94.5
85.0 80.2 63.8 93.3
(95%CI: 0.791e0.882) and 0.739 (95%CI: 0.698e0.779), respectively (Fig. 1). The sensitivity and specificity of the fungal count for evaluating UTIs were analysed. The results showed that at a cutoff value of 15.2 fungi/mL, we obtained a sensitivity of 72.7%, a specificity of 90% and a false negative rate of 39 cases (Table 2). Binary logistic regressions were attempted, but no significant multivariate models were found. Once adjusted for bacterial count, no other predictors of UTI were significant, which suggested that there were no reliable markers other than bacterial count for significant UTIs. 3.3. UF-1000i performance in predicting UTI in patients with or without an indwelling catheter The patients were organized by catheter status into two groups: those with an indwelling catheter and those without one. The Table 2 Parameters depending on cut-off values for UF-1000i YLCs count in 1016 specimens for diagnosing fungal UTIs.
3.4. UF-1000i performance in predicting UTI in patients taking antibiotics There were 653 (64.3%) patients taking antibiotics before sample collection in our study. The positive urine culture rates for both bacteria and yeast were compared between the two separated groups of patients taking antibiotics or not, and no significant differences were found for bacteria (P > 0.05), however, significant difference of positive culture rate for fungi was found between the two groups (P < 0.05). ROC curves were also used to analyse the predictive ability of BACT in the two groups. The AUC of BACT for diagnosing UTIs was 0.913 and 0.892 respectively. The AUC of YLC for diagnosing fungi UTIs was 0.825 and 0.818 in the two groups (Table 4). The Z test results showed no significant difference between the two AUCs. 3.5. Urine nitrite differences between gram-negative and grampositive bacteria
No. of YLCs (/mL)
Sensitivity (%) Specificity (%) Positive predictive value (%) Negative predictive value (%)
positive urine culture rates for bacteria and fungi were compared between the two groups, and significant differences were found (P < 0.05). Both the bacterial and the fungal positive culture rates were higher in the catheterized patients than in the noncatheterized patients (Table 3). The abilities of BACT and YLC counts to predict UTI in the two groups were analysed using separate ROC curves, and the AUCs were compared using the Z test. The AUC of BACT for diagnosing UTI was 0.936 in the patients with a urinary catheter and 0.861 in the patients without a catheter; the AUCs for the YLC count were 0.824 and 0.838 in the two groups, respectively (Fig. 2). The Z test results showed no significant difference between the 2 AUCs of the two groups.
15.2
67.1
324.3
2579.0
72.7 90.3 54.5 95.3
63.6 95 67.4 94.1
44.1 98 78.8 91.4
13 100 100 87.6
The chi-square test analysis showed that the nitrite-positive rate was significantly different between gram-positive and gramnegative bacteria (Table 5). Among the 79 nitrite-positive strains, 72 were gram-negative bacteria (91.1%). E. coli was the most
Please cite this article in press as: Le Z, et al., Performance of the Sysmex UF-1000i urine analyser in the rapid diagnosis of urinary tract infections in hospitalized patients, J Infect Chemother (2016), http://dx.doi.org/10.1016/j.jiac.2016.02.009
4
Z. Le et al. / J Infect Chemother xxx (2016) 1e6
Table 3 Comparison of catheterized and regular urine for urine culture positive rate of 1016 inpatients [n (%)]. Indwelling catheter
No Yes Total
Urine culture for bacteria Neg
Pos
406 (39.9) 328 (32.3) 734 (72.2)
124 (12.2) 158 (15.6) 282 (27.8)
c2
10.502
frequently isolated strain with positive nitrites, and K. pneumoniae was the second most common strain. 4. Discussions In this study, we aimed to evaluate the performance of UF-1000i in the rapid diagnosis of UTIs among inpatients with purpose of
P
Urine culture for yeast
P < 0.05
Neg
Pos
486 (47.9) 387 (38.1) 873 (86.0)
44 (4.3) 99 (9.7) 143 (14.0)
c2
P
30.532
P < 0.05
shortening the turn-around time and allowing early decisions regarding the need for antibiotic treatment, thus ultimately improving health care quality and reducing costs. Considering that the studied population comprised inpatients with complex disease types, and understanding that the reduced growth of microorganisms might have clinical relevance, we classified cultures with counts 104 CFU/ml as positive. The results showed that the
Fig. 2. ROC curve for diagnosing bacterial and fungal UTIs in patients with or without indwelling catheters. ROC curves of bacterial or YLC counts on the UF-1000i analyser compared with the quantitative urine culture results for 486 catheterized and 530 non-catheterized inpatients to diagnose bacterial and fungal UTIs.
Please cite this article in press as: Le Z, et al., Performance of the Sysmex UF-1000i urine analyser in the rapid diagnosis of urinary tract infections in hospitalized patients, J Infect Chemother (2016), http://dx.doi.org/10.1016/j.jiac.2016.02.009
Z. Le et al. / J Infect Chemother xxx (2016) 1e6
5
Table 4 Comparison of urine with and without antibiotic taking for urine culture positive rate of 1016 inpatients [n (%)]. Antibiotic treatment
No Yes Total
c2
Urine culture for bacteria Neg
Pos
251 (24.7) 483 (47.5) 734 (72.2)
112 (11) 170 (16.8) 282 (27.8)
2.70
positive rate of bacterial UTIs was 27.8%, which was similar to existing data [8,10]. ROC analysis showed that the AUC of BACT was 0.91, which represented a potential diagnostic value. To determine the best cutoff point for ruling out UTIs, we analysed a series of BACT cutoff values. We found that at a cutoff value of 38.7/mL, the sensitivity for UTIs was 90.5% and the NPV 94.5%, with 29 (2.9%) FN cases. The best cutoff value obtained in our study is slightly different from those obtained in other studies that used the same type of Sysmex instrument; those studies reported a higher NPV and fewer FNs. Rita De Rosa et al., whose subjects were primarily outpatients, found that a BACT count of 170/mL produced an NPV of 98.9%; Fabio Manoni et al., whose patients' origins were not described, reported an NPV of 98% by BACT count of 125/mL [9,10]. The difference between these studies and ours might be based on the complexity of the patients' disease types and on divergent study designs, as some studies used different urine culture cutoff values. In Marschal et al.'s study, which included a relatively small number of inpatients, the authors did not recommend the rapid diagnosis of UTIs in a complex population of hospitalized patients [12]. However, in current study, we demonstrated that using the cutoff value that we determined, 52% of specimens could be diagnosed as negative for UTIs using the UF-1000i and thereby bypassing urine culture, and only 2.9% of the results were false negatives. In some studies, WBC was considered an effective predictor of UTI, especially when combined with BACT [9,10,13]. In the present study, we attempted to construct a multivariate model for predicting UTIs; however, no significant predictors other than BACT were found. The inconsistency between the reliability of BACT and WBC in predicting UTIs might be related to the continuing growth of bacteria over time and the cellular collapse of WBCs over time. The reduced value of WBC counts for predicting UTIs was also reported by Boonen et al. [11]. In this study, 143 patients had fungal UTIs, the majority of which were caused by Candida species. Candida species were the seventh most common nosocomial pathogen reported by the National Nosocomial Infection Surveillance system of the US Centers for Disease Control and Prevention in the 1980s; they were the fourth most common pathogen when studies were limited to intensive care units, where most patients had a urinary catheter [14,15]. Our results demonstrated that patients with urinary catheters are more likely to develop bacterial or fungal UTIs compared with patients without catheters. To date, no studies have analysed the cutoff value of yeast cell counts determined using the UF-1000i for diagnosing fungal UTIs. In the current study, the YLC counts determined with the UF-1000i were not an adequate indicator for
P
Urine culture for yeast
P > 0.05
Neg
Pos
348 (34.3) 525 (51.7) 873 (86.0)
15 (1.4) 128 (12.6) 143 (14.0)
c2
P
46.166
P > 0.05
the rapid screening of fungal UTIs. However, our results showed that when the YLC count is >15.2/mL, the odds that the patient has a fungal UTI could be as high as 90%, which could prompt health care practitioners to change the catheter without awaiting urine culture results. Urinary catheterization is the main cause of health careassociated urinary tract infection [16]. In this study, we demonstrated that both bacterial and fungal positive culture rates were higher in catheterized patients than in non-catheterized patients. Next, we assessed the ability of different BACT and YLC cutoff values to predict UTIs in the groups with and without urinary catheters. BACT did obtain a better AUC for patients with indwelling catheters than for those without catheters (0.939 vs. 0.861); however, the two curves showed no significant diagnostic ability. In the present study, no significant difference was found either on positive culture rate or on AUCs between the patients that were taking antibiotics or not. Thus, antibiotics treatment does not undermine the predictive ability of BACT to diagnose UTIs compared to urine culture. Additionally, according to Boonen et al.'s report, none of the false negative cases were patients being treated with antibiotics [11]. Nevertheless, they studied a relatively small number of patients, and further research is needed. In our study, we estimated the role of urine nitrite tests in predicting the class of organism causing a UTI and found that 91.1% of gram-negative bacteria yielded a positive urine nitrite test. Thus, gram-negative organisms should be highly suspected when urine nitrite is positive. This information could be helpful to clinicians deciding whether to prescribe antibiotic treatment for an ambulatory patient with a UTI. In conclusion, the BACT count determined with the UF-1000i analyser would be a reliable platform for ruling out bacterial UTIs in hospitalized patients with or without urinary catheters and could also facilitate clinicians determine whether antibiotic therapy is necessary. Conflict of interest We declare that we have no conflicts of interest. Acknowledgements This study was supported by a grant from Medical Science and Technology Planning Project of Zhejiang Province in China (No. 2014PYA011). References
Table 5 Urine nitrite differences between gram-negative and gram-positive bacteria. Nitrite
Pos Neg Total
c
P
27.396
P < 0.05
2
Strain Gram pos
Gram neg
7 66 73
72 90 162
[1] Klevens RM, Edwards JR, Richards Jr CL, Horan TC, Gaynes RP, Pollock DA, et al. Estimating health care-associated infections and deaths in U.S. hospitals, 2002. Public Health Rep 2007;122:160e6. [2] Prevention. CfDCa. Catheter-Associated Urinary Tract Infection (CAUTI) event. January 2014. 2014, www.cdc.gov/nhsn/PDFs/pscManual/7pscCAUTIcurrent. pdf. [3] Magill SS, Hellinger W, Cohen J, Kay R, Bailey C, Boland B, et al. Prevalence of healthcare-associated infections in acute care hospitals in Jacksonville, Florida. Infect Control Hosp Epidemiol 2012;33:283e91.
Please cite this article in press as: Le Z, et al., Performance of the Sysmex UF-1000i urine analyser in the rapid diagnosis of urinary tract infections in hospitalized patients, J Infect Chemother (2016), http://dx.doi.org/10.1016/j.jiac.2016.02.009
6
Z. Le et al. / J Infect Chemother xxx (2016) 1e6
[4] Glass JI, Lefkowitz EJ, Glass JS, Heiner CR, Chen EY, Cassell GH. The complete sequence of the mucosal pathogen Ureaplasma urealyticum. Nature 2000;407: 757e62. [5] Wagenlehner FM, Hoyme U, Kaase M, Funfstuck R, Naber KG, Schmiemann G. Uncomplicated urinary tract infections. Dtsch Arztebl Int 2011;108:415e23. [6] French GL. The continuing crisis in antibiotic resistance. Int J Antimicrob Agents 2010;36(Suppl. 3):S3e7. [7] Okada H, Sakai Y, Miyazaki S, Arakawa S, Hamaguchi Y, Kamidono S. Detection of significant bacteriuria by automated urinalysis using flow cytometry. J Clin Microbiol 2000;38:2870e2. [8] Kim SY, Kim YJ, Lee SM, Hwang SH, Kim HH, Son HC, et al. Evaluation of the Sysmex UF-100 urine cell analyzer as a screening test to reduce the need for urine cultures for community-acquired urinary tract infection. Am J Clin Pathol 2007;128:922e5. [9] Manoni F, Fornasiero L, Ercolin M, Tinello A, Ferrian M, Hoffer P, et al. Cutoff values for bacteria and leukocytes for urine flow cytometer Sysmex UF-1000i in urinary tract infections. Diagn Microbiol Infect Dis 2009;65:103e7. [10] De Rosa R, Grosso S, Bruschetta G, Avolio M, Stano P, Modolo ML, et al. Evaluation of the Sysmex UF1000i flow cytometer for ruling out bacterial urinary tract infection. Clin Chim Acta 2010;411:1137e42.
[11] Boonen KJ, Koldewijn EL, Arents NL, Raaymakers PA, Scharnhorst V. Urine flow cytometry as a primary screening method to exclude urinary tract infections. World J Urol 2013;31:547e51. [12] Marschal M, Wienke M, Hoering S, Autenrieth IB, Frick JS. Evaluation of 3 different rapid automated systems for diagnosis of urinary tract infections. Diagn Microbiol Infect Dis 2012;72:125e30. [13] 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 2013;46:810e3. [14] Jarvis WR, Martone WJ. Predominant pathogens in hospital infections. J Antimicrob Chemother 1992;29:19e24. [15] Jarvis WR, Edwards JR, Culver DH, Hughes JM, Horan T, Emori TG, et al. Nosocomial infection rates in adult and pediatric intensive care units in the United States. National Nosocomial Infections Surveillance System. Am J Med 1991;91:185Se91S. [16] Tambyah PA, Oon J. Catheter-associated urinary tract infection. Curr Opin Infect Dis 2012;25:365e70.
Please cite this article in press as: Le Z, et al., Performance of the Sysmex UF-1000i urine analyser in the rapid diagnosis of urinary tract infections in hospitalized patients, J Infect Chemother (2016), http://dx.doi.org/10.1016/j.jiac.2016.02.009