ArticleAntimicrobial agents and chemotherapy2013
Time series analysis as a tool to predict the impact of antimicrobial restriction in antibiotic stewardship programs using the example of multidrug-resistant Pseudomonas aeruginosa.
Article in Antimicrobial agents and chemotherapy, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Usefulness of dynamic regression time series models for studying the relationship between antimicrobial consumption and bacterial antimicrobial resistance in hospitals: a systematic review.Antimicrobial resistance and infection control · 2023Pooled it
- Predicting future hospital antimicrobial resistance prevalence using machine learning.Communications medicine · 2024Article
- Leveraging Large Data, Statistics, and Machine Learning to Predict the Emergence of ResistantPharmacy (Basel, Switzerland) · 2024Article
- Systematic review of time lag between antibiotic use and rise of resistant pathogens among hospitalized adults in Europe.JAC-antimicrobial resistance · 2023Review
- Article
- Semi-Automated Visualization and ANalysis of Trends: A "SAVANT" for Facilitating Antimicrobial Stewardship Using Antistaphylococcal Resistance and Consumption as a Prototype.Open forum infectious diseases · 2018Article
- The Impact of Reducing Antibiotics on the Transmission of Multidrug-Resistant Organisms.Infection control and hospital epidemiology · 2017Article
- Antibiotic Selection Pressure Determination through Sequence-Based Metagenomics.Antimicrobial agents and chemotherapy · 2015Article
- Clinical and treatment-related risk factors for nosocomial colonisation with extensively drug-resistant Pseudomonas aeruginosa in a haematological patient population: a matched case control study.BMC infectious diseases · 2014Article
- Mathematical model to quantify the effects of risk factors on carbapenem-resistant Acinetobacter baumannii.Antimicrobial agents and chemotherapy · 2014Article
- Evaluation of phenotypic detection methods for metallo-β-lactamases (MBLs) in clinical isolates of Pseudomonas aeruginosa.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2014Article
- Rapid intrinsic fluorescence method for direct identification of pathogens in blood cultures.mBio · 2013Article
- Effect of metallo-β-lactamase production and multidrug resistance on clinical outcomes in patients with Pseudomonas aeruginosa bloodstream infection: a retrospective cohort study.BMC infectious diseases · 2013Article
- Article
- [Multiresistant gram-negative bacteria. A bacterial challenge of the twenty-first century].Medizinische Klinik, Intensivmedizin und Notfallmedizin · 2013Review
Corrections and comments
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The association between antimicrobial consumption and resistance in nonfermentative Gram-negative bacteria is well-known. Antimicrobial restriction, implemented in clinical routines by antibiotic stewardship programs (ASPs), is considered a means to reduce resistance rates. Whether and how antimicrobial restriction can accomplish this goal is still unknown though. This leads to an element of uncertainty when designing strategies for ASPs. From January 2002 until December 2011, an observational study was performed at the University Hospital Tübingen, Tübingen, Germany, to investigate the association between antimicrobial use and resistance rates in Pseudomonas aeruginosa. Transfer function models were used to determine such associations and to simulate antimicrobial restriction strategies. Various positive associations between antimicrobial consumption and resistance were observed in our setting. Surprisingly, impact estimations of different antimicrobial restriction strategies revealed relatively low intervention expenses to effectively attenuate the observed increase in resistance. For example, a simulated intervention of an annual 4% reduction in the use of meropenem over 3 years from 2009 until 2011 yielded a 62.5% attenuation (95% confidence interval, 15% to 110%) in the rising trend of multidrug-resistant Pseudomonas aeruginosa (three- and four-class-resistant P. aeruginosa [34MRGN-PA]). Time series analysis models derived from past data may be a tool to predict the outcome of antimicrobial restriction strategies, and could be used to design ASPs.
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