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The incidence of infections caused by multidrug-resistant E. coli strains has risen in the past years. Antibiotic resistance in E. coli is often mediated by acquisition and maintenance of plasmids. The study of E. coli plasmid epidemiology and genomics often requires long-read sequencing information, but recently a number of tools that allow plasmid prediction from short-read data have been developed.Here, we reviewed 25 available plasmid prediction tools and categorized them into binary plasmid/chromosome classification tools and plasmid reconstruction tools. We benchmarked six tools that aim to reliably reconstruct distinct plasmids, with a special focus on plasmids carrying antibiotic resistance genes (ARGs) such as extended-spectrum beta-lactamase genes. They use either assembly graph information (plasmidSPAdes, gplas), reference databases (MOB-Suite, FishingForPlasmids) or both (HyAsP and SCAPP) to produce plasmid predictions. The benchmark data set consisted of 240 E. coli strains, harboring 631 plasmids, which were representative for the diversity of E. coli in public databases. Notably, these strains were not used for training any of the tools.We found that two thirds (n=425, 66.3.%) of all plasmids were correctly reconstructed by at least one of the six tools, with a range of 92 (14.58%) to 317 (50.23%) correctly predicted plasmids. However, the majority of plasmids that carried antibiotic resistance genes (n=85, 57.8%) could not be completely recovered as distinct plasmids by any of the tools. MOB-suite was the only tool that was able to correctly reconstruct the majority of plasmids (n=317, 50.23%), and performed best at reconstructing large plasmids (n=166, 46.37%) and ARG-plasmids (n=41, 27.9%), but predictions frequently contained chromosome contamination (40%). In contrast, plasmidSPAdes reconstructed the highest fraction of plasmids smaller than 18 kbp (n=168, 61.54%). Large ARG-plasmids, however, were recovered with small precision values (median=0.47, IQR=0.61), indicating that plasmidSPAdes frequently merged sequences derived from distinct replicons. Additionally, only 63% of all plasmid-borne ARGs were correctly predicted by plasmidSPAdes.The remaining four tools (FishingForPlasmids, HyAsP, SCAPP and gplas) were able to correctly reconstruct a combined total of 18 plasmids that were missed by MOB-suite and plasmidSPAdes.Available bioinformatic tools can provide valuable insight into E. coli plasmids, but also have important limitations. This work will serve as a guideline for selecting the most appropriate plasmid reconstruction tool for studies focusing on E. coli plasmids in the absence of long-read sequencing data.

The incidence of infections caused by multidrug-resistant E. coli strains has risen in the past years. Antibiotic resistance in E. coli is often mediated by acquisition and maintenance of plasmids. The study of E. coli plasmid epidemiology and genomics often requires long-read sequencing information, but recently a number of tools that allow plasmid prediction from short-read data have been developed. Here, we reviewed 25 available plasmid prediction tools and categorized them into binary plasmid/chromosome classification tools and plasmid reconstruction tools. We benchmarked six tools (MOB-suite, plasmidSPAdes, gplas, FishingForPlasmids, HyAsP and SCAPP) that aim to reliably reconstruct distinct plasmids, with a special focus on plasmids carrying antibiotic resistance genes (ARGs) such as extended-spectrum beta-lactamase genes. We found that two thirds (n = 425, 66.3%) of all plasmids were correctly reconstructed by at least one of the six tools, with a range of 92 (14.58%) to 317 (50.23%) correctly predicted plasmids. However, the majority of plasmids that carried antibiotic resistance genes (n = 85, 57.8%) could not be completely recovered as distinct plasmids by any of the tools. MOB-suite was the only tool that was able to correctly reconstruct the majority of plasmids (n = 317, 50.23%), and performed best at reconstructing large plasmids (n = 166, 46.37%) and ARG-plasmids (n = 41, 27.9%), but predictions frequently contained chromosome contamination (40%). In contrast, plasmidSPAdes reconstructed the highest fraction of plasmids smaller than 18 kbp (n = 168, 61.54%). Large ARG-plasmids, however, were frequently merged with sequences derived from distinct replicons. Available bioinformatic tools can provide valuable insight into E. coli plasmids, but also have important limitations. This work will serve as a guideline for selecting the most appropriate plasmid reconstruction tool for studies focusing on E. coli plasmids in the absence of long-read sequencing data.

ObjectiveTo determine the cost-effectiveness of selective digestive decontamination (SDD) as compared to selective oropharyngeal decontamination (SOD) in intensive care units (ICUs) with low levels of antimicrobial resistance.DesignPost-hoc analysis of a previously performed individual patient data meta-analysis of two cluster-randomised cross-over trials.Setting24 ICUs in the Netherlands.Participants12 952 ICU patients who were treated with ≥1 dose of SDD (n=6720) or SOD (n=6232).InterventionsSDD versus SOD.Primary and secondary outcome measuresThe incremental cost-effectiveness ratio (ICER; ie, costs to prevent one in-hospital death) was calculated by comparing differences in direct healthcare costs and in-hospital mortality of patients treated with SDD versus SOD. A willingness-to-pay curve was plotted to reflect the probability of cost-effectiveness of SDD for a range of different values of maximum costs per prevented in-hospital death.ResultsThe ICER resulting from the fixed-effect meta-analysis, adjusted for clustering and differences in baseline characteristics, showed that SDD significantly reduced in-hospital mortality (adjusted absolute risk reduction 0.0195, 95% CI 0.0050 to 0.0338) with no difference in costs (adjusted cost difference €62 in favour of SDD, 95% CI –€1079 to €935). Thus, SDD yielded significantly lower in-hospital mortality and comparable costs as compared with SOD. At a willingness-to-pay value of €33 633 per one prevented in-hospital death, SDD had a probability of 90.0% to be cost-effective as compared with SOD.ConclusionIn Dutch ICUs, SDD has a very high probability of cost-effectiveness as compared to SOD. These data support the implementation of SDD in settings with low levels of antimicrobial resistance.

ABSTRACT Antibiotic resistance is a global and increasing problem that is not counterbalanced by the development of new therapeutic agents. The prevalence of antibiotic resistance is especially high in intensive care units with frequently reported outbreaks of multidrug-resistant organisms. In addition to classical infection prevention protocols and surveillance programs, counterintuitive interventions, such as selective decontamination with antibiotics and antibiotic rotation have been applied and investigated to control the emergence of antibiotic resistance. This review provides an overview of selective oropharyngeal and digestive tract decontamination, decolonization of methicillin-resistant Staphylococcus aureus and antibiotic rotation as strategies to modulate antibiotic resistance in the intensive care unit.

Selective digestive decontamination (SDD) and selective oropharyngeal decontamination (SOD) have been associated with reduced mortality and lower ICU-acquired bacteremia and ventilator-associated pneumonia rates in areas with low levels of antibiotic resistance. However, the effect of selective decontamination (SDD/SOD) in areas where multidrug-resistant Gram-negative bacteria are endemic is less clear. It will be important to determine whether SDD/SOD improves patient outcome in such settings and how these measures affect the epidemiology of multidrug-resistant Gram-negative bacteria. Here we review the current evidence on the effects of SDD/SOD on antibiotic resistance development in individual ICU patients as well as the effect on ICU ecology, the latter including both ICU-level antibiotic resistance and antibiotic resistance development during long-term use of SDD/SOD.