Dataset Information

Early Economic Assessment of Faecal Microbiota Transplantation for Patients with Urinary Tract Infections Caused by Multidrug-Resistant Organisms.

ABSTRACT:

Introduction

The use of faecal microbiota transplantation (FMT) to eradicate intestinal carriage of multidrug-resistant organisms (MDRO) has been described in case reports and small case series. Although few in numbers, these patients suffer from recurrent infections that may exacerbate both the patients' comorbidities and their healths. In the current study, we hypothesized that FMT for MDRO-related urinary tract infections (UTIs) reduces hospitalisations and associated costs.

Methods

In a cohort of patients referred for FMT from 2015 to 2020, we selected all patients who had consecutively been referred for eradication of MRDO carriage with UTIs. An early economic assessment was performed to calculate hospital-related costs. The overall study cohort was registered at ClinicalTrials, study identifier NCT03712722.

Results

We consecutively included five patients with UTIs caused by MDROs. Four of the patients were renal transplant recipients. Patients were followed for median 126 days (range 60-320), where the follow-up duration for each patient was aligned with the number of days from the first UTI to FMT. The median number of UTIs per patient dropped from 4 to 0. Investigating hospital costs, hospital admission days dropped by 87% and monthly hospital costs by 79%.

Conclusions

FMT was effective in reducing the occurrence of UTIs and mediated a marked reduction in hospital costs. We suggest that this strategy is cost-effective.

Trial registration

ClinicalTrials, study identifier NCT03712722.

SUBMITTER: Baek OD

PROVIDER: S-EPMC10229483 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

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Publications

Early Economic Assessment of Faecal Microbiota Transplantation for Patients with Urinary Tract Infections Caused by Multidrug-Resistant Organisms.

Baek Olivia Dybro OD Hjermitslev Camilla K CK Dyreborg Line L Baunwall Simon M D SMD Høyer Katrine L KL Rågård Nina N Hammeken Lianna H LH Povlsen Johan V JV Ehlers Lars H LH Hvas Christian Lodberg CL

Infectious diseases and therapy 20230416 5

<h4>Introduction</h4>The use of faecal microbiota transplantation (FMT) to eradicate intestinal carriage of multidrug-resistant organisms (MDRO) has been described in case reports and small case series. Although few in numbers, these patients suffer from recurrent infections that may exacerbate both the patients' comorbidities and their healths. In the current study, we hypothesized that FMT for MDRO-related urinary tract infections (UTIs) reduces hospitalisations and associated costs.<h4>Method ...[more]

PMID: 37062804

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Project description:BACKGROUND:The scope of this work is to build a Machine Learning model able to predict patients risk to contract a multidrug resistant urinary tract infection (MDR UTI) after hospitalization. To achieve this goal, we used different popular Machine Learning tools. Moreover, we integrated an easy-to-use cloud platform, called DSaaS (Data Science as a Service), well suited for hospital structures, where healthcare operators might not have specific competences in using programming languages but still, they do need to analyze data as a continuous process. Moreover, DSaaS allows the validation of data analysis models based on supervised Machine Learning regression and classification algorithms. RESULTS:We used DSaaS on a real antibiotic stewardship dataset to make predictions about antibiotic resistance in the Clinical Pathology Operative Unit of the Principe di Piemonte Hospital in Senigallia, Marche, Italy. Data related to a total of 1486 hospitalized patients with nosocomial urinary tract infection (UTI). Sex, age, age class, ward and time period, were used to predict the onset of a MDR UTI. Machine Learning methods such as Catboost, Support Vector Machine and Neural Networks were utilized to build predictive models. Among the performance evaluators, already implemented in DSaaS, we used accuracy (ACC), area under receiver operating characteristic curve (AUC-ROC), area under Precision-Recall curve (AUC-PRC), F1 score, sensitivity (SEN), specificity and Matthews correlation coefficient (MCC). Catboost exhibited the best predictive results (MCC 0.909; SEN 0.904; F1 score 0.809; AUC-PRC 0.853, AUC-ROC 0.739; ACC 0.717) with the highest value in every metric. CONCLUSIONS:the predictive model built with DSaaS may serve as a useful support tool for physicians treating hospitalized patients with a high risk to acquire MDR UTIs. We obtained these results using only five easy and fast predictors accessible for each patient hospitalization. In future, DSaaS will be enriched with more features like unsupervised Machine Learning techniques, streaming data analysis, distributed calculation and big data storage and management to allow researchers to perform a complete data analysis pipeline. The DSaaS prototype is available as a demo at the following address: https://dsaas-demo.shinyapps.io/Server/.

Project description:Escherichia coli is the leading cause of urinary tract infections (UTIs) in children and adults. The gastrointestinal tract is the primary reservoir of uropathogenic E. coli, which can be acquired from a variety of environmental exposures, including retail meat. In the current study, we used a novel statistical-genomic approach to estimate the proportion of pediatric UTIs caused by foodborne zoonotic E. coli strains. E. coli urine isolates were collected from DC residents aged 2 months to 17 years from the Children's National Medical Center Laboratory, 2013-2014. During the same period, E. coli isolates were collected from retail poultry products purchased from 15 sites throughout DC. A total of 52 urine and 56 poultry isolates underwent whole-genome sequencing, core genome phylogenetic analysis, and host-origin prediction by a Bayesian latent class model that incorporated data on the presence of mobile genetic elements (MGEs) among E. coli isolates from multiple vertebrate hosts. A total of 56 multilocus sequence types were identified among the isolates. Five sequence types-ST10, ST38, ST69, ST117, and ST131-were observed among both urine and poultry isolates. Using the Bayesian latent class model, we estimated that 19% (10/52) of the clinical E. coli isolates in our population were foodborne zoonotic strains. These data suggest that a substantial portion of pediatric UTIs in the Washington DC region may be caused by E. coli strains originating in food animals and likely transmitted via contaminated poultry meat.IMPORTANCEEscherichia coli UTIs are a heavy public health burden and can have long-term negative health consequences for pediatric patients. E. coli has an extremely broad host range, including humans, chickens, turkeys, pigs, and cattle. E. coli derived from food animals is a frequent contaminant of retail meat products, but little is known about the risk these strains pose to pediatric populations. Quantifying the proportion of pediatric UTIs caused by food-animal-derived E. coli, characterizing the highest-risk strains, and identifying their primary reservoir species could inform novel intervention strategies to reduce UTI burden in this vulnerable population. Our results suggest that retail poultry meat may be an important vehicle for pediatric exposure to zoonotic E. coli strains capable of causing UTIs. Vaccinating poultry against the highest-risk strains could potentially reduce poultry colonization, poultry meat contamination, and downstream pediatric infections.

Dataset Information

Early Economic Assessment of Faecal Microbiota Transplantation for Patients with Urinary Tract Infections Caused by Multidrug-Resistant Organisms.

Introduction

Methods

Results

Conclusions

Trial registration

Publications

Early Economic Assessment of Faecal Microbiota Transplantation for Patients with Urinary Tract Infections Caused by Multidrug-Resistant Organisms.

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