FlowUTI: An interactive web-application for optimizing the use of flow cytometry as a screening tool in urinary tract infections.
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0277340 |
| _version_ | 1797862010624409600 |
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| author | Guillermo Martín-Gutiérrez Carlos Martín-Pérez Héctor Toledo Emilio Sánchez-Cantalejo José Antonio Lepe |
| author_facet | Guillermo Martín-Gutiérrez Carlos Martín-Pérez Héctor Toledo Emilio Sánchez-Cantalejo José Antonio Lepe |
| author_sort | Guillermo Martín-Gutiérrez |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-09T22:13:30Z |
| format | Article |
| id | doaj.art-04ac19b877ef4047832a946002584c45 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-09T22:13:30Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-04ac19b877ef4047832a946002584c452023-03-23T05:32:09ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-011711e027734010.1371/journal.pone.0277340FlowUTI: An interactive web-application for optimizing the use of flow cytometry as a screening tool in urinary tract infections.Guillermo Martín-GutiérrezCarlos Martín-PérezHéctor ToledoEmilio Sánchez-CantalejoJosé Antonio LepeDue 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.https://doi.org/10.1371/journal.pone.0277340 |
| spellingShingle | Guillermo Martín-Gutiérrez Carlos Martín-Pérez Héctor Toledo Emilio Sánchez-Cantalejo José Antonio Lepe FlowUTI: An interactive web-application for optimizing the use of flow cytometry as a screening tool in urinary tract infections. PLoS ONE |
| title | FlowUTI: An interactive web-application for optimizing the use of flow cytometry as a screening tool in urinary tract infections. |
| title_full | FlowUTI: An interactive web-application for optimizing the use of flow cytometry as a screening tool in urinary tract infections. |
| title_fullStr | FlowUTI: An interactive web-application for optimizing the use of flow cytometry as a screening tool in urinary tract infections. |
| title_full_unstemmed | FlowUTI: An interactive web-application for optimizing the use of flow cytometry as a screening tool in urinary tract infections. |
| title_short | FlowUTI: An interactive web-application for optimizing the use of flow cytometry as a screening tool in urinary tract infections. |
| title_sort | flowuti an interactive web application for optimizing the use of flow cytometry as a screening tool in urinary tract infections |
| url | https://doi.org/10.1371/journal.pone.0277340 |
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