SynthesisPLoS pathogens2014
Cycling empirical antibiotic therapy in hospitals: meta-analysis and models.
Synthesis in PLoS pathogens, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
53 citing papers in PubMed, 2 syntheses or guidelines pooled it, 104 citations in OpenAlex.
- The Effect of Antibiotic-Cycling Strategy on Antibiotic-Resistant Bacterial Infections or Colonization in Intensive Care Units: A Systematic Review and Meta-Analysis.Worldviews on evidence-based nursing · 2020Pooled it
- Pooled it
- Trial
- Cyclic-Linear Hybrid Peptides as Molecular Transporters of Chemotherapeutic Agents.Chemical biology letters · 2026Article
- Fitness seascapes are necessary for realistic modeling of the evolutionary response to drug therapy.Science advances · 2025Article
- Ceftobiprole in cystic fibrosis: a case series.JAC-antimicrobial resistance · 2025Article
- Dynamic collateral sensitivity profiles highlight opportunities and challenges for optimizing antibiotic treatments.PLoS biology · 2025Article
- Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs.eLife · 2024Article
- Modelling the implementation of narrow versus broader spectrum antibiotics in the empiric treatment of E. coli bacteraemia.Scientific reports · 2024Article
- Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs.bioRxiv : the preprint server for biology · 2024Article
- Tumour Growth Mechanisms Determine Effectiveness of Adaptive Therapy in Glandular Tumours.Interdisciplinary sciences, computational life sciences · 2024Article
- An agent-based model on antimicrobial de-escalation in intensive care units: Implications on clinical trial design.PloS one · 2024Article
- A stochastic analysis of the interplay between antibiotic dose, mode of action, and bacterial competition in the evolution of antibiotic resistance.PLoS computational biology · 2023Article
- Antifungal alternation can be beneficial for durability but at the cost of generalist resistance.Communications biology · 2023Article
- Towards evolutionary predictions: Current promises and challenges.Evolutionary applications · 2023Review
- Association between Antibiotic Use and Hospital-OnsetAntibiotics (Basel, Switzerland) · 2022Article
- Burden of Antimicrobial Resistance: Compared to What?Epidemiologic reviews · 2022Article
- Spatial structure impacts adaptive therapy by shaping intra-tumoral competition.Communications medicine · 2022Article
- Finding the right sequence of drugs.eLife · 2021Article
- Antibiotic treatment protocols revisited: the challenges of a conclusive assessment by mathematical modelling.Journal of the Royal Society, Interface · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rise of resistance together with the shortage of new broad-spectrum antibiotics underlines the urgency of optimizing the use of available drugs to minimize disease burden. Theoretical studies suggest that coordinating empirical usage of antibiotics in a hospital ward can contain the spread of resistance. However, theoretical and clinical studies came to different conclusions regarding the usefulness of rotating first-line therapy (cycling). Here, we performed a quantitative pathogen-specific meta-analysis of clinical studies comparing cycling to standard practice. We searched PubMed and Google Scholar and identified 46 clinical studies addressing the effect of cycling on nosocomial infections, of which 11 met our selection criteria. We employed a method for multivariate meta-analysis using incidence rates as endpoints and find that cycling reduced the incidence rate/1000 patient days of both total infections by 4.95 [9.43-0.48] and resistant infections by 7.2 [14.00-0.44]. This positive effect was observed in most pathogens despite a large variance between individual species. Our findings remain robust in uni- and multivariate metaregressions. We used theoretical models that reflect various infections and hospital settings to compare cycling to random assignment to different drugs (mixing). We make the realistic assumption that therapy is changed when first line treatment is ineffective, which we call "adjustable cycling/mixing". In concordance with earlier theoretical studies, we find that in strict regimens, cycling is detrimental. However, in adjustable regimens single resistance is suppressed and cycling is successful in most settings. Both a meta-regression and our theoretical model indicate that "adjustable cycling" is especially useful to suppress emergence of multiple resistance. While our model predicts that cycling periods of one month perform well, we expect that too long cycling periods are detrimental. Our results suggest that "adjustable cycling" suppresses multiple resistance and warrants further investigations that allow comparing various diseases and hospital settings.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.