ArticlePloS one2020
Prediction of Uropathogens by Flow Cytometry and Dip-stick Test Results of Urine Through Multivariable Logistic Regression Analysis.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed, 5 citations in OpenAlex.
- Current and Future Flow Cytometry Applications Contributing to Antimicrobial Resistance Control.Microorganisms · 2023Review
- Paper and thread as media for the frugal detection of urinary tract infections (UTIs).Analytical and bioanalytical chemistry · 2022Review
- Evaluation of the detection ability of uropathogen morphology and vaginal contamination by the Atellica UAS800 automated urine microscopy analyzer and its effectiveness.Journal of clinical laboratory analysis · 2021Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeMultidrug-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.
resultsThe 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).
conclusionSimultaneous 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.
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Registered trials
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