Ecological theory suggests that antimicrobial cycling will not reduce antimicrobial resistance in hospitals

Abstract

Hospital-acquired infections caused by antibiotic-resistant bacteria pose a grave and growing threat to public health. Antimicrobial cycling, in which two or more antibiotic classes are alternated on a time scale of months to years, seems to be a leading candidate in the search for treatment strategies that can slow the evolution and spread of antibiotic resistance in hospitals. We develop a mathematical model of antimicrobial cycling in a hospital setting and use this model to explore the efficacy of cycling programs. We find that cycling is unlikely to reduce either the evolution or the spread of antibiotic resistance. Alternative drug-use strategies such as mixing, in which each treated patient receives one of several drug classes used simultaneously in the hospital, are predicted to be more effective. A simple ecological explanation underlies these results. Heterogeneous antibiotic use slows the spread of resistance. However, at the scale relevant to bacterial populations, mixing imposes greater heterogeneity than does cycling. As a consequence, cycling is unlikely to be effective and may even hinder resistance control. These results may explain the limited success reported thus far from clinical trials of antimicrobial cycling.

Fig. 1.

Schematic diagram of the model and the corresponding differential equations. The color coding associates mathematical terms with ecological processes. Red represents infection, yellow represents supercolonization, blue represents clearance, and black represents influx and efflux.

Fig. 2.

Strain frequencies over time, for a cycling program with a drug switch every 90 days and 80% compliance (α = 0.8). Parameter values: β = 1, c = 0, γ = 0.03, m = 0.7, m₁ = .05, m₂ = .05, τ₁ + τ₂ = 0.5, μ = 0.1, σ = 0.25, and α = 0.8.

Fig. 3.

Fraction of patients carrying resistant bacteria, for cycle lengths of 1 yr, 3 months, and 2 weeks, respectively. Solid lines, total fraction of patients colonized with resistant bacteria under cycling; dashed lines, total fraction of patients colonized with resistant bacteria under a 50-50 mixing regime. By this measure, mixing outperforms cycling. Parameters are as in Fig. 2.

Fig. 4.

Average total resistance as a function of cycle period, calculated numerically. Solid lines, average total fraction of patients colonized with resistant bacteria under cycling; dashed lines, total fraction of patients colonized with resistant bacteria under a 50-50 mixing regime. Parameters are as in Fig. 2.

Fig. 5.

Effects of cycling and mixing on the selective conditions faced by a bacterial clone. Cycling offers greater heterogeneity at the level of the ward, but mixing offers greater heterogeneity at the level of the individual patient.

Fig. 6.

Rate at which novel multiresistant strains are generated, as measured by R₁(t) × R₂(t) dt. (A-C) Symmetric input of strains into the hospital. Parameters are as in Fig. 2. (D-F) Asymmetric input of strains into the hospital. Parameters are as before, but with m₁ = 0.19, m₂ = 0.01, and m = 0.6. Solid lines, cycling; dashed lines, mixing.

Fig. 7.

Average rate of dual resistance acquisition as function of cycle period. (A) Symmetric input of strains into the hospital. (B) Asymmetric input of strains into the hospital. Solid lines, cycling; dashed lines, 50-50 mixing regime. Parameters are as in Fig. 6.