Dataset Information

A Porcine Model for Urinary Tract Infection.

ABSTRACT: Urinary tract infection (UTI) is the most common bacterial infectious disease with a high frequency of recurrence and the leading cause of septicemia. In vivo experimentation has contributed significantly to the present-day knowledge on UTI pathogenesis. This research has traditionally been based on murine models of UTI. Occasional conflicting results between UTI in mice and humans and increasing skepticism toward small rodent models in general warrant the need of novel large-animal infection models that better resemble the anatomy and physiology of humans, and thus better mimic the course of infection in humans. Here, we report, to our knowledge, the first large-animal model of cystitis. The model is based on pigs, and the protocol supports the establishment of persistent, non-ascending infection in this animal and is established without invasive surgical procedures, pain, and discomfort for the animal. The course of infection is monitored by cystoscopy, microscopy of bladder biopsies, and biochemical analysis of urine and blood samples. At termination, harvested whole bladders from infected pigs are analyzed for microbiological colonization using microscopy, histology, and viable bacterial counts. The model is a useful tool in future studies of UTI pathogenesis and opens up novel possibilities to bridge the current knowledge obtained from small-animal UTI models to UTI pathogenesis in humans.

SUBMITTER: Nielsen TK

PROVIDER: S-EPMC6882375 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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A Porcine Model for Urinary Tract Infection.

Nielsen Thomas Kastberg TK Petersen Nicky Anúel NA Stærk Kristian K Grønnemose Rasmus Birkholm RB Palarasah Yaseelan Y Nielsen Lene Feldskov LF Kolmos Hans Jørn HJ Andersen Thomas Emil TE Lund Lars L

Frontiers in microbiology 20191121

Urinary tract infection (UTI) is the most common bacterial infectious disease with a high frequency of recurrence and the leading cause of septicemia. <i>In vivo</i> experimentation has contributed significantly to the present-day knowledge on UTI pathogenesis. This research has traditionally been based on murine models of UTI. Occasional conflicting results between UTI in mice and humans and increasing skepticism toward small rodent models in general warrant the need of novel large-animal infec ...[more]

PMID: 31824442

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Project description:Background: Urinary tract infection (UTI) is one of the common causes of sepsis. However, nomograms predicting the sepsis risk in UTI patients have not been comprehensively researched. The goal of this study was to establish and validate a nomogram to predict the probability of sepsis in UTI patients. Methods: Patients diagnosed with UTI were extracted from the Medical Information Mart for Intensive Care III database. These patients were randomly divided into training and validation cohorts. Independent prognostic factors for UTI patients were determined using forward stepwise logistic regression. A nomogram containing these factors was established to predict the sepsis incidence in UTI patients. The validity of our nomogram model was determined using multiple indicators, including the area under the receiver operating characteristic curve (AUC), correction curve, Hosmer-Lemeshow test, integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision-curve analysis (DCA). Results: This study included 6,551 UTI patients. Stepwise regression analysis revealed that the independent risk factors for sepsis in UTI patients were congestive heart failure, diabetes, liver disease, fluid electrolyte disorders, APSIII, neutrophils, lymphocytes, red blood cell distribution width, urinary protein, urinary blood, and microorganisms. The nomogram was then constructed and validated. The AUC, NRI, IDI and DCA of the nomogram all showed better performance than traditional APSIII score. The calibration curve and Hosmer-Lemeshow test results indicate that the nomogram was well-calibrated. Improved NRI and IDI values indicate that our nomogram scoring system is superior to other commonly used ICU scoring systems. The DCA curve indicates that the DCA map of the nomogram has good clinical application ability. Conclusion: This study identified the independent risk factors of sepsis in UTI patients and used them to construct a prediction model. The present findings may provide clinical reference information for preventing sepsis in UTI patients.

Dataset Information

A Porcine Model for Urinary Tract Infection.

Publications

A Porcine Model for Urinary Tract Infection.

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