Informed switching strongly decreases the prevalence of antibiotic resistance in hospital wards

Abstract

Antibiotic resistant nosocomial infections are an important cause of mortality and morbidity in hospitals. Antibiotic cycling has been proposed to contain this spread by a coordinated use of different antibiotics. Theoretical work, however, suggests that often the random deployment of drugs ("mixing") might be the better strategy. We use an epidemiological model for a single hospital ward in order to assess the performance of cycling strategies which take into account the frequency of antibiotic resistance in the hospital ward. We assume that information on resistance frequencies stems from microbiological tests, which are performed in order to optimize individual therapy. Thus the strategy proposed here represents an optimization at population-level, which comes as a free byproduct of optimizing treatment at the individual level. We find that in most cases such an informed switching strategy outperforms both periodic cycling and mixing, despite the fact that information on the frequency of resistance is derived only from a small sub-population of patients. Furthermore we show that the success of this strategy is essentially a stochastic phenomenon taking advantage of the small population sizes in hospital wards. We find that the performance of an informed switching strategy can be improved substantially if information on resistance tests is integrated over a period of one to two weeks. Finally we argue that our findings are robust against a (moderate) preexistence of doubly resistant strains and against transmission via environmental reservoirs. Overall, our results suggest that switching between different antibiotics might be a valuable strategy in small patient populations, if the switching strategies take the frequencies of resistance alleles into account.

Figure 1. Flow chart of the model.

Figure 2. Relative change in prevalence of resistance mutations and of inappropriately treated patients compared to mixing for different snapshot-based alternative strategies.

Points correspond to the mean over 10⁴ simulations, error-bars correspond to the 95% confidence interval of the mean, inferred through 1000 bootstrap samples. Color indicates the prevalence of the resistant strains (p_A0+p_0B) among incoming infected and colonized patients (black: 2% green: 10% blue: 20%).

Figure 3. Relative change in prevalence of resistance mutations and of inappropriately treated patients compared to mixing for different ISS that integrate resistance frequencies over time.

Points correspond to the mean over 10⁴ simulations, error-bars correspond to the 95% confidence interval of the mean, inferred through 1000 bootstrap samples. Color indicates the prevalence of the resistant strains (p_A0+p_0B) among incoming infected and colonized patients (black: 2% green: 10% blue: 20%).

Figure 4. Relative change of resistance prevalence(left column) and inappropriately treated patients (right column) compared to mixing for ISS₇ (green points) and ISS_Last (black points).

The figures show the change induced by ISS₇ and ISS_Last as a function of the resistance prevalence among incoming carriers, p_A0+p_0B, (x-axes) and for different fractions of carriers, p_C, among incoming patients (rows). Points correspond to the mean over 10⁴ simulations, error-bars correspond to the 95% confidence interval of the mean, inferred through 1000 bootstrap samples.

Figure 5. Relative change of resistance prevalence and inappropriately treated patients compared to mixing for ISS₇ (green points) and ISS_Last (black points).

The figures show the change induced by ISS₇ and ISS_Last as a function of the rate of progression, r_P, (x-axes). The right panel shows how treatment frequency increases as a function of the r_P. Points correspond to the mean over 10⁴ simulations, error-bars correspond to the 95% confidence interval of the mean, inferred through 1000 bootstrap samples.

Figure 6. Relative change of resistance prevalence and inappropriately treated patients compared to mixing for ISS₇ (green points) and ISS_Last (black points).

The figure shows the change conferred by ISS₇ and ISS_Last as a function of p_AB, the prevalence of the doubly resistant strain among incoming carriers (x-axes). Points correspond to the mean over 10⁴ simulations, error-bars correspond to the 95% confidence interval of the mean, inferred through 1000 bootstrap samples.

Figure 7. Relative change of resistance prevalence and inappropriately treated patients compared to mixing for ISS₇ (green points) and ISS_Last (black points).

The figure shows the change conferred by ISS₇ and ISS_Last for different relative impacts of the environmental reservoir (x-axis). The relative impact of the reservoir is measured as the relative fraction of the force of infection that is mediated via the reservoir. Points correspond to the mean over 10⁴ simulations, error-bars correspond to the 95% confidence interval of the mean, inferred through 1000 bootstrap samples.

Figure 8. Relative change of resistance prevalence and inappropriately treated patients compared to mixing for ISS₇ (green points) and ISS_Last (black points).

The figure shows the change conferred by ISS₇ and ISS_Last as a function of N, the number of beds in the ward. In order to keep the R₀ constant across different population sizes the transmission rate is assumed to be inversely proportional to the number of beds (i.e. β∼1/N). Points correspond to the mean over 10⁴/(N/20) simulations (the number of samples was chosen inversely proportional to the population size because, with the Gillespie algorithm used, simulation time increases proportionally with population size whereas the level of stochastic variation decreases with population size), error-bars correspond to the 95% confidence interval of the mean, inferred through 1000 bootstrap samples.

Figure 9. Relative change of resistance prevalence and inappropriately treated patients compared to mixing for ISS₇ (green points) and ISS_Last (black points).

The figure shows the change conferred by ISS₇ and ISS_Last for an asymmetric scenario in which p_A0 = 0.1*2/3, p_0B = 0.1*1/3, c_A0 = 0.2 *1/3 and c_0B = 0.2*1/3 (i.e. the less costly mutant is more abundant). The x-axis corresponds to the mixing frequency M (see Table 3). Note that the mixing and ISS₇ depend on M, whereas ISS_Last is independent of M. Points correspond to the mean over 10⁴ simulations, error-bars correspond to the 95% confidence interval of the mean, inferred through 1000 bootstrap samples.