Evidence map›Paper›PMID 28182774›Full record

ArticlePloS one2017

Benefits and unintended consequences of antimicrobial de-escalation: Implications for stewardship programs.

Josie Hughes, Xi Huo, Lindsey Falk, Amy Hurford, Kunquan Lan, Bryan Coburn, Andrew Morris, Jianhong Wu

Abstract read
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. The Relative Impact of Community and Hospital Antibiotic Use on the Selection of Extended-spectrum Beta-lactamase-producing Escherichia coli.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2019
    Article
  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Josie HughesCentre for Disease Modelling, York University, Toronto, Ontario, Canada.ORCID http://orcid.org/0000-0001-7875-9015
Xi HuoCentre for Disease Modelling, York University, Toronto, Ontario, Canada.
Lindsey FalkDalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.ORCID http://orcid.org/0000-0002-9671-4045
Amy HurfordDepartment of Biology and Department of Mathematics and Statistics, Memorial University of Newfoundland, St. John's, Newfoundland, Canada.
Kunquan LanDepartment of Mathematics, Ryerson University, Toronto, Ontario, Canada.
Bryan CoburnDepartment of Laboratory Medicine & Pathobiology, University of Toronto, Toronto, Ontario, Canada.
Andrew MorrisDepartment of Medicine, Sinai Health System & University Health Network, Toronto, Ontario, Canada.
Jianhong WuCentre for Disease Modelling, York University, Toronto, Ontario, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sequential antimicrobial de-escalation aims to minimize resistance to high-value broad-spectrum empiric antimicrobials by switching to alternative drugs when testing confirms susceptibility. Though widely practiced, the effects de-escalation are not well understood. Definitions of interventions and outcomes differ among studies. We use mathematical models of the transmission and evolution of Pseudomonas aeruginosa in an intensive care unit to assess the effect of de-escalation on a broad range of outcomes, and clarify expectations. In these models, de-escalation reduces the use of high-value drugs and preserves the effectiveness of empiric therapy, while also selecting for multidrug-resistant strains and leaving patients vulnerable to colonization and superinfection. The net effect of de-escalation in our models is to increase infection prevalence while also increasing the probability of effective treatment. Changes in mortality are small, and can be either positive or negative. The clinical significance of small changes in outcomes such as infection prevalence and death may exceed more easily detectable changes in drug use and resistance. Integrating harms and benefits into ranked outcomes for each patient may provide a way forward in the analysis of these tradeoffs. Our models provide a conceptual framework for the collection and interpretation of evidence needed to inform antimicrobial stewardship.

Indexed as

Anti-Infective AgentsCommunicable Diseases, EmergingCritical PathwaysCross InfectionDisease ProgressionDose-Response Relationship, DrugDrug SubstitutionHealth PlanningHumansIntensive Care UnitsPseudomonas aeruginosaPseudomonas InfectionsRisk AssessmentTreatment OutcomeWithholding TreatmentAnti-Infective Agents

Identifiers

PMID28182774
PMCPMC5300270

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.