Benefits and unintended consequences of antimicrobial de-escalation: Implications for stewardship programs.
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.
Centre for Disease Modelling, York University, Toronto, Ontario, Canada.
Josie
http://orcid.org/0000-0001-7875-9015
J
Hughes
Centre for Disease Modelling, York University, Toronto, Ontario, Canada.|Department of Mathematics, Ryerson University, Toronto, Ontario, Canada.
Xi
X
Huo
Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
Lindsey
http://orcid.org/0000-0002-9671-4045
L
Falk
Department of Biology and Department of Mathematics and Statistics, Memorial University of Newfoundland, St. John's, Newfoundland, Canada.
Amy
A
Hurford
Department of Mathematics, Ryerson University, Toronto, Ontario, Canada.
Kunquan
K
Lan
Department of Laboratory Medicine & Pathobiology, University of Toronto, Toronto, Ontario, Canada.|Department of Medicine, Sinai Health System & University Health Network, Toronto, Ontario, Canada.|Department of Medicine, University of Toronto, Toronto, Ontario, Canada.
Bryan
B
Coburn
Department of Medicine, Sinai Health System & University Health Network, Toronto, Ontario, Canada.|Department of Medicine, University of Toronto, Toronto, Ontario, Canada.
Andrew
A
Morris
Centre for Disease Modelling, York University, Toronto, Ontario, Canada.
Jianhong
J
Wu
D000890:Anti-Infective Agents / Q000008:administration & dosage*; D021821:Communicable Diseases, Emerging / Q000453:epidemiology; D019091:Critical Pathways / Q000458:organization & administration*; D003428:Cross Infection; D018450:Disease Progression; D004305:Dose-Response Relationship, Drug; D057915:Drug Substitution; D006285:Health Planning / Q000592:standards; D006801:Humans; D007362:Intensive Care Units / Q000458:organization & administration*; D011552:Pseudomonas Infections / Q000188:drug therapy* / Q000453:epidemiology / Q000473:pathology / Q000635:transmission; D011550:Pseudomonas aeruginosa / Q000187:drug effects / Q000254:growth & development; D018570:Risk Assessment; D016896:Treatment Outcome; D028761:Withholding Treatment
D000890:Anti-Infective Agents