FlowUTI: An interactive web-application for optimizing the use of flow cytometry as a screening tool in urinary tract infections.
PLoS One
; 17(11): e0277340, 2022.
Artículo
en Inglés
| MEDLINE | ID: covidwho-2109331
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.
Texto completo:
Disponible
Colección:
Bases de datos internacionales
Base de datos:
MEDLINE
Asunto principal:
Infecciones Urinarias
Tipo de estudio:
Estudio experimental
/
Estudio observacional
/
Estudio pronóstico
Límite:
Anciano
/
Humanos
/
Lactante
/
Recién Nacido
Idioma:
Inglés
Revista:
PLoS One
Asunto de la revista:
Ciencia
/
Medicina
Año:
2022
Tipo del documento:
Artículo
País de afiliación:
Journal.pone.0277340
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