Evidence of within-facility patient–patient Clostridiodes difficile infection spread across diverse settings

Abstract

Previous studies have suggested that a hospital patient's risk of developing healthcare facilityonset (HCFO) Clostridioides difficile infections (CDIs) increases with the number of concurrent spatially proximate patients with CDI, termed CDI pressure. However, these studies were performed either in a single institution or in a single state with a very coarse measure of concurrence. We conducted a retrospective case-control study involving over 17.5 million inpatient visits across 700 hospitals in eight U.S. states. We built a weighted, directed network connecting overlapping inpatient visits to measure facility-level CDI pressure. We then matched HCFO-CDIs with non-CDI controls on facility, comorbidities, and demographics and performed a conditional logistic regression to determine the odds of developing HCFO-CDI given the number of coincident patient visits with CDI. On average, cases' visits coincided with 9.2 CDI cases, which for an individual with an average length of stay corresponded to an estimated 17.7% (95% CI: 12.9% -22.7%) increase in the odds of acquiring HCFO-CDI compared to an inpatient visit without concurrent CDI cases or fully isolated from both direct and indirect risks from concurrent CDI cases. These results suggest that, either directly or indirectly, hospital patients with CDI lead to CDIs in non-infected patients with temporally overlapping visits.

Clustering Disease of Clostridioides Difficile Infection: Implication for the Management in Internal Medicine

“…Furthermore, emerging data confirm that during the recent pandemic, CDI often complicated the clinical course of patients with COVID-19, mainly among those who had more comorbidities and previous experiences of exposure in healthcare settings [81]. In addition to these causes, an important role is played by the diffusion of new hypervirulent strains and an increase in community-acquired CDI has recently been reported among individuals free of the major risk factors hitherto recognized [82][83][84]. Once the size of the problem has been analyzed, the adaptation of the health structures, the education of the personnel, and the containment of the infection and, therefore, of the consumption of antibiotics are the measures to be implemented to limit the spread of CD and, therefore, of its infection at both the nosocomial and community level [85,86].…”

Classification of Tweets Related to Natural Disasters Using Machine Learning Algorithms

“…Is an ML model used for the binary classification of data. It is used to predict one of two possible categorical outcomes (e.g., yes/no, true/false) based on a set of predictors [34]. The output of RL is a probability that maps to a binary prediction (0 or 1).…”