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Prediction of Uropathogens by Flow Cytometry and Dip-stick Test Results of Urine Through Multivariable Logistic Regression Analysis


Autoři: Akihiro Nakamura aff001;  Aya Kohno aff002;  Nobuyoshi Noguchi aff001;  Kenji Kawa aff002;  Yuki Ohno aff002;  Masaru Komatsu aff001;  Hachiro Yamanishi aff001
Působiště autorů: Department of Clinical Laboratory Science, Faculty of Health Care, Tenri Health Care University, Tenri, Japan aff001;  Department of Clinical Bacteriology, Clinical Laboratory Medicine, Tenri Hospital, Tenri, Japan aff002
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0227257

Souhrn

Purpose

Multidrug-resistant Enterobacteriaceae in urinary tract infection (UTI) has spread worldwide; one cause is overuse of broad-spectrum antimicrobial agents such as fluoroquinolone antibacterials. To improve antimicrobial agent administration, this study aimed to calculate a probability prediction formula to predict the organism strain causing UTI in real time from dip-stick testing and flow cytometry.

Methodology

We examined 372 outpatient spot urine samples with observed pyuria and bacteriuria using dip-stick testing and flow cytometry. We performed multiple logistic-regression analysis on the basis of 11 measurement items and BACT scattergram analysis with age and sex as explanatory variables and each strain as the response variable and calculated a probability prediction formula.

Results

The best prediction formula for discrimination of the bacilli group and cocci or polymicrobial group was a model with 5 explanatory variables that included percentage of scattergram dots in an angular area of 0–25° (P<0.001), sex (P<0.001), nitrite (P = 0.002), and ketones (P = 0.133). For a predicted cut-off value of Y = 0.395, sensitivity was 0.867 and specificity was 0.775 (cross-validation group: sensitivity = 0.840, specificity = 0.760). The best prediction formula for P. mirabilis and other bacilli was a model with percentage of scattergram dots in an angular area of 0–20° (P<0.001) and nitrite (P = 0.090). For a predicted cut-off value of Y = 0.064, sensitivity was 0.889 and specificity was 0.788 (cross-validation group: sensitivity = 1.000, specificity = 0.766).

Conclusion

Simultaneous use of the calculated probability prediction formula with urinalysis results facilitates real-time prediction of organisms causing UTI, thus providing helpful information for empiric therapy.

Klíčová slova:

Antimicrobials – Enterobacteriaceae – Flow cytometry – Ketones – Nitrites – Proteus mirabilis – Urinary tract infections – Urine


Zdroje

1. Johnson JR. Virulence factors in Escherichia coli urinary tract infection. Clin Microbiol Rev 1991; 4: 80–128. doi: 10.1128/cmr.4.1.80 1672263

2. Paterson DL, Bonomo RA. Extended-spectrum beta-lactamases: a clinical update. Clin Microbiol Rev 2005; 18: 657–86. doi: 10.1128/CMR.18.4.657-686.2005 16223952

3. Queenan AM, Bush K. Carbapenemases: the versatile beta-lactamases. Clin Microbiol Rev 2007; 20: 440–58. doi: 10.1128/CMR.00001-07 17630334

4. Ohno Y, Nakamura A, Hashimoto E, Matsutani H, Abe N, Fukuda S, et al. Molecular epidemiology of carbapenemase-producing Enterobacteriaceae in a primary care hospital in Japan, 2010–2013. J Infect Chemother 2017; 23: 224–9. doi: 10.1016/j.jiac.2016.12.013 28161293

5. Monsen T, Rydén P. Flow cytometry analysis using sysmex UF-1000i classifies uropathogens based on bacterial, leukocyte, and erythrocyte counts in urine specimens among patients with urinary tract infections. J Clin Microbiol 2015; 53: 539–45. doi: 10.1128/JCM.01974-14 25472486

6. Sun SJ, Zuo LL, Liu PP, Wang XM, He ML, Wu SY. The diagnostic performance of urine flow cytometer UF1000i for urinary tract infections. Clin Lab 2018; 64: 1395–401. doi: 10.7754/Clin.Lab.2018.180210 30274017

7. Muratani T, Kobayashi T, Minamoto Y, Ikuno Y, Migita S. The possibility of the bacterial class estimate using urine from patients with the urinary tract infection by the fully automated urine particle analyzer UF-1000i. Sysmex J Int 2013; 23: 1–9.

8. De Rosa R, Grosso S, Lorenzi G, Bruschetta G, Camporese A. Evaluation of the new Sysmex UF-5000 fluorescence flow cytometry analyser for ruling out bacterial urinary tract infection and for prediction of Gram negative bacteria in urine cultures. Clin Chim Acta 2018; 484: 171–8. doi: 10.1016/j.cca.2018.05.047 29803898

9. Nakamura A, Komatsu M, Kondo A, Ohno Y, Kohno H, Nakamura F, et al. Rapid detection of B2-ST131 clonal group of extended-spectrum β-lactamase-producing Escherichia coli by matrix-assisted laser desorption ionization-time-of-flight mass spectrometry: discovery of a peculiar amino acid substitution in B2-ST131 clonal group. Diagn Microbiol Infect Dis 2015; 83: 237–44. doi: 10.1016/j.diagmicrobio.2015.06.024 26316404

10. Ruopp MD, Perkins NJ, Whitcomb BW, Schisterman EF. Youden Index and optimal cut-point estimated from observations affected by a lower limit of detection. Biom J 2008; 50: 419–30. doi: 10.1002/bimj.200710415 18435502

11. Akaike H. A new look at the statistical model identification. IEEE Transactions on Automatic Control 1974; 19: 716–23.

12. Vrieze SI. Model selection and psychological theory: a discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Psychol Methods 2012; 17: 228–43. doi: 10.1037/a0027127 22309957

13. Kim SY, Park Y, Kim H, Kim J, Koo SH, Kwon GC. Rapid screening of urinary tract infection and discrimination of gram-positive and gram-negative bacteria by automated flow cytometric analysis using Sysmex UF-5000. J Clin Microbiol 2018; 56: pii: e02004–17. doi: 10.1128/JCM.02004-17 29769277

14. Wang XH, Zhang G, Fan YY, Yang X, Sui WJ, Lu XX. Direct identification of bacteria causing urinary tract infections by combining matrix-assisted laser desorption ionization-time of flight mass spectrometry with UF-1000i urine flow cytometry. J Microbiol Methods 2013; 92: 231–5. doi: 10.1016/j.mimet.2012.12.016 23305925

15. Schechner V, Kotlovsky T, Kazma M, Mishali H, Schwartz D, Navon-Venezia S, et al. Asymptomatic rectal carriage of blaKPC producing carbapenem-resistant Enterobacteriaceae: who is prone to become clinically infected? Clin Microbiol Infect 2013; 19: 451–6. doi: 10.1111/j.1469-0691.2012.03888.x 22563800

16. Ahn JY, Song JE, Kim MH, Choi H, Kim JK, Ann HW, et al. Risk factors for the acquisition of carbapenem-resistant Escherichia coli at a tertiary care center in South Korea: a matched case-control study. Am J Infect Control 2014; 42: 621–5. doi: 10.1016/j.ajic.2014.02.024 24837112

17. Sanford Guide for Infectious Disease Treatment 2019 [digital content]. Henry FC. USA.

18. Matsumoto T, Hamasuna R, Ishikawa K, Takahashi S, Yasuda M, Hayami H, et al. Nationwide survey of antibacterial activity against clinical isolates from urinary tract infections in Japan (2008). Int J Antimicrob Agents 2011; 37: 210–8. doi: 10.1016/j.ijantimicag.2010.10.032 21242062

19. Hayami H, Takahashi S, Ishikawa K, Yasuda M, Yamamoto S, Uehara S, et al. Nationwide surveillance of bacterial pathogens from patients with acute uncomplicated cystitis conducted by the Japanese surveillance committee during 2009 and 2010: antimicrobial susceptibility of Escherichia coli and Staphylococcus saprophyticus. J Infect Chemother 2013; 19: 393–403. doi: 10.1007/s10156-013-0606-9 23640203

20. Sinha B, Gonzalez R. Hyperammonemia in a boy with obstructive ureterocele and proteus infection. J Urol 1984; 131: 330–1. doi: 10.1016/s0022-5347(17)50366-3 6699967


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