The effects of antibiotic cycling and mixing on antibiotic resistance in intensive care units: a cluster-randomised crossover trial

Duijn, Pleun Joppe van; Verbrugghe, Walter; Jorens, Philippe G.; Spöhr, Fabian; Schedler, Dirk; Deja, Maria; Rothbart, Andreas; Annane, Djillali; Lawrence, Christine; Van, J.C. Nguyen; Misset, Benoît; Jereb, Matjaž; Seme, Katja; Šifrer, Franc; Tomič, Viktorija; Estévez, Francisco; Carneiro, Jandira; Harbarth, Stéphan Juergen; Eijkemans, Marinus Johannes Cornelis; Bonten, Marc J. M.; Goossens, Herman; Malhotra-Kumar, Surbhi; Lammens, Christine; Vila, Jordi; Roca, Ignasi

doi:10.1016/s1473-3099(18)30056-2

Cited by 72 publications

(74 citation statements)

References 23 publications

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“…Cycling was better than standard practice in terms of reducing resistance and mortality in hospitals, according to a more recent meta-analysis of 11 studies (zur Wiesch, Kouyos, Abel, Viechtbauer, & Bonhoeffer, 2014). In a randomized trial conducted in eight intensive care units in five European countries, no difference in the incidence of resistance or the all-cause mortality was found during cycling versus mixing, suggesting no strong difference between the two strategies (van Duijn et al, 2018), in spite of good adherence to the strategies. This result is somewhat disappointing, but in line with theory predicting that any difference in the outcome of cycling and mixing strategy is small compared to the substantial stochastic variation around the expected outcome (Beardmore, Peña-Miller, Gori, & Iredell, 2017).…”

Section: How Useful Are These Theoretical Results For Clinical Practice?mentioning

confidence: 99%

Evolutionary epidemiology models to predict the dynamics of antibiotic resistance

Blanquart

2019

Evolutionary Applications

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The evolution of resistance to antibiotics is a major public health problem and an example of rapid adaptation under natural selection by antibiotics. The dynamics of antibiotic resistance within and between hosts can be understood in the light of mathematical models that describe the epidemiology and evolution of the bacterial population. “Between‐host” models describe the spread of resistance in the host community, and in more specific settings such as hospitalized hosts (treated by antibiotics at a high rate), or farm animals. These models make predictions on the best strategies to limit the spread of resistance, such as reducing transmission or adapting the prescription of several antibiotics. Models can be fitted to epidemiological data in the context of intensive care units or hospitals to predict the impact of interventions on resistance. It has proven harder to explain the dynamics of resistance in the community at large, in particular because models often do not reproduce the observed coexistence of drug‐sensitive and drug‐resistant strains. “Within‐host” models describe the evolution of resistance within the treated host. They show that the risk of resistance emergence is maximal at an intermediate antibiotic dose, and some models successfully explain experimental data. New models that include the complex host population structure, the interaction between resistance‐determining loci and other loci, or integrating the within‐ and between‐host levels will allow better interpretation of epidemiological and genomic data from common pathogens and better prediction of the evolution of resistance.

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Section: How Useful Are These Theoretical Results For Clinical Practice?mentioning

confidence: 99%

Evolutionary epidemiology models to predict the dynamics of antibiotic resistance

Blanquart

2019

Evolutionary Applications

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“…Co-development of new antimicrobials together with specific AST for target microorganisms may help to better position an antibiotic in the clinical market 47 . In an era of pan-drug resistance, we are reconsidering empiric broad-spectrum antibiotic-prescribing policies, and the identification of bacterial resistance to novel antimicrobials using AST may not necessarily mean that an already available, or previously 'shelved' , antibiotic needs to be discarded but that it needs to be considered in alternate prescribing schemes such as antibiotic mixing or cycling 48 . Novel and rapid AST should be versatile and adaptable to changes in the way in which (new) antibiotics are prescribed, both currently and in the future.…”

Section: Cost-effectiveness and Health Economics Studiesmentioning

confidence: 99%

Developmental roadmap for antimicrobial susceptibility testing systems

et al. 2018

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Antimicrobial susceptibility testing (AST) technologies help to accelerate the initiation of targeted antimicrobial therapy for patients with infections and could potentially extend the lifespan of current narrow-spectrum antimicrobials. Although conceptually new and rapid AST technologies have been described, including new phenotyping methods, digital imaging and genomic approaches, there is no single major, or broadly accepted, technological breakthrough that leads the field of rapid AST platform development. This might be owing to several barriers that prevent the timely development and implementation of novel and rapid AST platforms in health-care settings. In this Consensus Statement, we explore such barriers, which include the utility of new methods, the complex process of validating new technology against reference methods beyond the proof-of-concept phase, the legal and regulatory landscapes, costs, the uptake of new tools, reagent stability , optimization of target product profiles, difficulties conducting clinical trials and issues relating to quality and quality control, and present possible solutions.

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“…In one study conducted in an intensive care unit, rotational antibiotic administration was found to offer no benefit in terms of AMR [117] . It has also been suggested that antibiotic rotation will not reduce resistance rates, but only cause fluctuations in resistance [119] . A recent comprehensive study conducted in eight intensive care units in six countries showed that antibiotic rotation did not reduce AMR in Gramnegative bacteria [120] .…”

Section: Antibiotic Rotationmentioning

confidence: 99%