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. 2022 Nov 8;17(11):e0277340.
doi: 10.1371/journal.pone.0277340. eCollection 2022.

FlowUTI: An interactive web-application for optimizing the use of flow cytometry as a screening tool in urinary tract infections

Guillermo Martín-Gutiérrez  1   2   3 , Carlos Martín-Pérez  4 , Héctor Toledo  1 , Emilio Sánchez-Cantalejo  4 , José Antonio Lepe  1   2   3
Affiliations

FlowUTI: An interactive web-application for optimizing the use of flow cytometry as a screening tool in urinary tract infections

Guillermo Martín-Gutiérrez et al. PLoS One. .

Abstract

Due to the high prevalence of patients attending with urinary tract infection (UTI) symptoms, the use of flow-cytometry as a rapid screening tool to avoid unnecessary cultures is becoming a widely used system in clinical practice. However, the recommended cut-points applied in flow-cytometry systems differ substantially among authors, making it difficult to obtain reliable conclusions. Here, we present FlowUTI, a shiny web-application created to establish optimal cut-off values in flow-cytometry for different UTI markers, such as bacterial or leukocyte counts, in urine from patients with UTI symptoms. This application provides a user-friendly graphical interface to perform robust statistical analysis without a specific training. Two datasets are analyzed in this manuscript: one composed of 204 urine samples from neonates and infants (≤3 months old) attended in the emergency department with suspected UTI; and the second dataset including 1174 urines samples from an elderly population attended at the primary care level. The source code is available on GitHub (https://github.com/GuillermoMG-HUVR/Microbiology-applications/tree/FlowUTI/FlowUTI). The web application can be executed locally from the R console. Alternatively, it can be freely accessed at https://covidiario.shinyapps.io/flowuti/. FlowUTI provides an easy-to-use environment for evaluating the efficiency of the urinary screening process with flow-cytometry, reducing the computational burden associated with this kind of analysis.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. FlowUTI workflow.
Flow cytometry figure was adapted from www.cytognos.com, and csv figure from www.shareicon.net.
Fig 2
Fig 2. Screenshot of the FlowUTI diagnostic accuracy browser, showing the results obtained for bacterial counts corresponding to the neonate demo dataset.
See this image and copyright information in PMC

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Grants and funding

The author(s) received no specific funding for this work.