Characterizing non-linear effects of hospitalisation duration on antimicrobial resistance in respiratory isolates: an analysis of a prospective nationwide surveillance system

Sommerstein, Rami; Atkinson, Andrew; Priore, Elia Francesco Lo; Kronenberg, Andreas; Marschall, Jonas; Burnens, André P.; Cherkaoui, Abdessalam; Dubuis, Olivier; Egli, Adrian; Gaia, Valéria; Leib, Stephen L.; Luyet, S.; Nordmann, Patrice; Perreten, Vincent; Piffaretti, Jean-Claude; Prod’hom, Guy; Schrenzel, Jacques; Widmer, Andreas F.; Zanetti, Giorgio; Zbinden, Reinhard

doi:10.1016/j.cmi.2017.05.018

Cited by 4 publications

(1 citation statement)

References 26 publications

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“…Previous research used GAMs in the context of antibiotic resistance. Two such papers dealt with the relationship between population-level antibiotic use and the emergence of resistance (24, 25), while another paper utilized GAMs, but not time-varying coefficient models, to model non-linear relationships between hospitalization duration and resistance (26).…”

Section: Introductionmentioning

confidence: 99%

Stable temporal relationships as a first step towards causal inference: an application to antibiotic resistance

Baraz

Chowers

Nevo

et al. 2022

Preprint

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Epidemiological studies often analyze data as static, essentially averaging observed associations across time. Overlooking time trends is especially problematic in settings subject to rapid changes. A prominent example for such a setting is antibiotic resistance, which has reached concerning levels, and poses a global healthcare challenge. Bacteria constantly evolve and hence antibiotic resistance is characterized by time-varying relationships with clinical and demographic covariates. In this paper, we speculate that covariates with a causal effect are expected to have stable relationships with resistance over calendar time. To this end, we applied time-varying coefficient models to a large clinical dataset from an Israeli hospital, and have shown their advantages in describing covariate-resistance relationships. We found both time-stable and time-varying covariate-resistance relationships. These results serve as initial evidence towards causal interpretation of these relationships, as one may expect time-stable rather than time-varying relationships to correspond with causal effects. We further conducted data-driven simulations, that have illustrated how results from time-varying coefficient models must be carefully interpreted with respect to causal claims. Potentially, identification of causal covariate-resistance relationships can lead to new medical interventions and healthcare policies, and improve the generalization of existing predictive models for antibiotic resistance.

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Section: Introductionmentioning

confidence: 99%

Stable temporal relationships as a first step towards causal inference: an application to antibiotic resistance

Baraz

Chowers

Nevo

et al. 2022

Preprint

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Extracellular products-mediated interspecific interaction between Pseudomonas aeruginosa and Escherichia coli

Yuan

Lin

et al. 2020

J Microbiol.

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Distribution of pathogens and antimicrobial resistance in bacteraemia according to hospitalization duration: a nationwide surveillance study in Switzerland

Buetti¹,

Marschall

Timsit

et al. 2021

Clinical Microbiology and Infection

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Objectives: Changing microorganism distributions and decreasing antibiotic susceptibility with increasing length of hospital stay have been demonstrated for the colonization or infection of selected organ systems. We wanted to describe microorganism distribution or antibiotic resistance in bacteraemia according to duration of the hospitalization using a large national epidemiological/microbiological database (ANRESIS) in Switzerland. Methods: We conducted a nationwide, observational study on bacteraemia using ANRESIS data from 1 January 2008 to 31 December 2017. We analysed data on bacteraemia from those Swiss hospitals that sent information on a regular basis during the entire study period. We described the pathogen distribution and specific trends of resistance during hospitalization for Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Serratia marcescens and Staphylococcus aureus. Results: We included 28 318 bacteraemia isolates from 90 Swiss hospitals. The most common aetiology was E. coli (33.4%, 9459), followed by S. aureus (16.7%, 4721), K. pneumoniae (7.1%, 2005), Enterococcus faecalis (5.2%, 1473), P. aeruginosa (4.3%, 1228), Streptococcus pneumoniae (4.3%, 1208) and Enterococcus faecium (3.9%, 1101). We observed 489 (1.73%) S. marcescens isolates. We observed an increasing trend for E. faecium (from 1.5% at day 0 to 13.7% at day 30; p < 0.001), K. pneumoniae (from 6.1% to 7.8%, p < 0.001) and P. aeruginosa (from 2.9% to 13.7%, p < 0.001) with increasing duration of hospitalization; and decreasing trends for E. coli (from 41.6% to 21.6%; p < 0.001) and S. aureus (p < 0.001). Ceftriaxone resistance among E. coli remained stable for the first 15 days of hospitalization and then increased. Ceftriaxone resistance among K. pneumoniae and S. marcescens and oxacillin resistance among S. aureus increased linearly during the hospitalization. Cefepime resistance among P. aeruginosa remained stable during the hospitalization. Discussion: We showed that hospitalization duration is associated with a species-and antibiotic classdependent pattern of antimicrobial resistance.

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Characterizing non-linear effects of hospitalisation duration on antimicrobial resistance in respiratory isolates: an analysis of a prospective nationwide surveillance system

Cited by 4 publications

References 26 publications

Stable temporal relationships as a first step towards causal inference: an application to antibiotic resistance

Stable temporal relationships as a first step towards causal inference: an application to antibiotic resistance

Extracellular products-mediated interspecific interaction between Pseudomonas aeruginosa and Escherichia coli

Distribution of pathogens and antimicrobial resistance in bacteraemia according to hospitalization duration: a nationwide surveillance study in Switzerland

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