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Infection prevention and control

Users Online: 32 Home. How to cite this article: Chaudhry D, Prajapat B. Intensive care unit bugs in India: How do they differ from the western world?. J Assoc Chest Physicians ; Table 1: Prevalence of microbiologic isolates from Intensive Care Units of different parts of world as depicted by extended prevalence of infection in intensive care 2 and soap study Click here to view. Table 2: Types of organisms in culture-positive infected patients according to geographical region as found in extended prevalence of infection in intensive care 2 study Click here to view.

We need a similar interdisciplinary effort for antimicrobials. Unfortunately, for some drugs e.

In farmed animals, resistance frequency declines with log e antibiotic usage, so a fourfold reduction in usage only halves the prevalence of AMR genes Munk et al. Moreover, historical withdrawals of antibiotics have had patchy impacts on the prevalence of resistance Lipsitch, It is therefore likely that resistance management RM interventions beyond reduced prescribing will be required to tackle the crisis in antibiotic resistance. This synthesis focuses on the problem of antibiotic resistance in bacteria, although the sources used to illustrate good and bad RM practice are varied.

At the outset is important to emphasize where the key challenges lie. Although resistance can evolve by spontaneous mutation or horizontal gene transfer, resistance may be spread to a greater or lesser degree by transmission. There are added complications when bacteria can persist as harmless commensals in the gut Escherichia coli , Enterococcus spp. Klebsiellla spp. For pathogens, antibiotic therapy has two possible outcomes, clearance or failure.

Selection for resistance occurs if infected patients can transmit resistant microbes before clearance or death. For convenience, key aspects of diverse RM interventions have been broken down into five rules.

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It is easy to misunderstand the limitations of resistance management interventions. A fundamental RM principle is that many more options are possible, while frequencies of resistance are still low Boni et al. In cycling, a particular class of antibiotic is used preferentially for a period of time followed by a different class, and so forth Figure 1.

In mixing, multiple distinct antibiotic regimes are prescribed in different patients, to create a spatial mosaic of antibiotic use. While it can be hard to make these approaches fully distinct in the clinic, they rely on different assumptions. This latest trial is not alone in finding little evidence to support cycling and mixing in intensive care Martinez et al. Nevertheless, a number of evolutionary factors may have opposed success.

Combating Antibiotic Resistance: Infection Prevention & Control

First, theory indicates that mixing is only beneficial when initial resistance frequencies are low Bonhoeffer et al. High frequencies of resistance in many patients will generate a substantial force of infection, providing selectable diversity in untreated individuals Box 1, Figure 3 , especially if they can be colonized asymptomatically by MDR commensals.

Second, multidrug RM should use chemistries with independent modes of action. Increasing the number of antibiotic regimens in a mixing strategy should also be beneficial Figure 3 , although this has not been modelled explicitly. Successful trials of mixing strategies have employed six rather than three regimens and deployed structurally distinct carbapenems that can only be overcome by different resistance genes Takesue et al.

Nevertheless, since reversing resistance is particularly difficult, deployment of preventative strategies may be considered a success if they can stabilize levels of resistance van Duijn et al.


A core aim of mixing strategies is the reduction in transmission of bacteria resistant to antibiotic A to new patients being treated with antibiotic A. It follows that deployment of mixing should consider the transmission networks of bacterial targets. Deploying mixing within a single ward is likely to be less powerful since transmission networks are likely to extend to the whole hospital: clinical trials of a standardized mixing regime were much more effective when deployed at a hospital level, rather than on a single ward Takesue et al.

Comparatively isolated wards such as intensive care units may also be difficult RM targets if they have relatively closed transmission networks, in other words if most transmission is from healthcare workers and patients within that ward. If a large part of this transmission is from patients with a high resistance burden, then this will make RM even more challenging. Transmission networks that include susceptible bacteria from unexposed individuals should be more amenable to mixing RM Figure 3. More open transmission networks are known to reduce the residence time of commensal, resistant nosocomial specialists Birgy et al.

Cycling RM and rotations, in general, rely on substantial fitness costs periodically driving down the frequency of resistance Figure 1 ; Forrester et al. If fitness costs can be magnified, then these approaches should be more powerful. It would be valuable to explore whether similar interactions exist in vivo for clinically important mutations. Resistance mutations in Pseudomonas aeruginosa, mutations in penicillin binding proteins in Streptococcus spp.

Plasmids themselves can be readily maintained in bacterial populations by conjugation. This means that withdrawal of a drug can lead to resistance declining very slowly, so that resistance persists at a level that enables a rapid response to selection when drug exposure resumes. It is also significant that the fitness costs associated with mutations or resistance plasmids are not necessarily stable.

Ongoing selection commonly produces compensatory mutations that reduce these costs de Vos et al. Since high prevalence of resistance implies multiple cycles of selection, this Rule may interact with Rule 1; it is better to act when resistance is rare and fitness costs are high. Mutation supply is the product of mutation rate and bacterial population size. However, in order for mutations to be effective in overcoming resistance, they must confer a phenotype that can overcome the prevailing concentration of drug or drugs. Aside from mixing and cycling, the other key multidrug RM strategy is combination therapy, the simultaneous use of more than one drug in an individual Figure 1.

Combinations work because the simultaneous occurrence of multiple resistance mutations in a single microbe is very unlikely, that is, combinations reduce the supply of effective mutations. For instance, if mutations conferring resistance to rifampicin occur in 1 in 10 6 bacterial cells, and to a second drug 1 in 10 8 cells, then provided drugs have independent modes of action, and cells with mutations conferring resistance to both drugs occur a rate equivalent to the product of these frequencies, that is, 1 in 10 14 cells.

Nevertheless, it is not clear whether there are additional risks of using these drug classes in combinations versus single drug treatments Tamma et al. Drug combinations targeting P. Mutation supply principles also apply to drug design. Here, the availability of new genes on mobile genetic elements MGEs , such as plasmids, can replace mutation in terms of the critical supply of genetic novelty. Thus, small increases in carriage of resistance MGEs could have profound consequences in terms of providing the essential variation upon which selection can act.

Although horizontal gene transfer can undermine the value of combinations Bonhoeffer et al. Reducing mutation supply by reducing pathogen population size, on the other hand, is beneficial. This complementary action ensures that population sizes are controlled much more effectively with combinations. While combinations are powerful, their deployment can be more challenging, because there are more assumptions to be met in comparison with other RM strategies Roush, ; Figure 2.

First, combined drugs should have similar persistence and efficacy. Potentially, both these factors contributed to the rise of partial artemisinin resistance Boni et al.

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A second complication particularly associated with combinations is how different drugs interact. Drugs can act independently, interact synergistically to increase efficacy or interact antagonistically to reduce efficacy. Synergistic interactions are tempting in terms of improved toxicity Paul et al.

Assessing the broader resistance management consequences of combinations is challenging because of discrepancies between in vitro and in vivo work or between clinical studies. Pharmacodynamic experiments have demonstrated that combinations can limit evolution of resistance Thomas et al. Unfortunately, too few clinical trials, or large reviews, have investigated effects of combinations on evolution of resistance in addition to clinical outcomes Paul et al.

To conclude, the RM benefits of combinations are not obvious from routine clinical outcomes Paul et al. Higher doses of toxins or of antibiotics can impose more intense selection pressure on mutations that confer resistance Costelloe et al. It follows that using the lowest dose possible to achieve a treatment effect could slow the spread resistance Blanquart, ; Kouyos et al.

While some of the theoretical basis of the MPC has been criticized, for example, the assumption that selection only takes place above minimal inhibitory concentrations Day et al. Biological details can be important here. In management of TB, pharmacogenetic factors warn against low doses. It is important to make a distinction between treatment dose and treatment duration. There is increasing evidence that shorter courses with more effective delivery e.

Shorter courses can help prevent selection for resistance to fluoroquinolones Rees et al. Active interventions to reduce the prevalence of resistance after it has arisen could be important complementary RM options. Other solutions are still experimental Table 1. It is possible to engineer bacteriophage to selectively kill cells carrying particular resistance genes Bikard et al. The main constraints here are that bacteriophage has a narrow host range and rapidly selects for resistance.

However, a conventional phage cocktail effectively targeting a species is likely to be far more attractive to regulators and manufacturers than a genetically modified phage cocktail targeting resistant genotypes only. Nevertheless, experiments in a mouse model show that natural conjugation is insufficient to displace targeted plasmids. Without toxin—antitoxin systems, these unstable vectors are lost rapidly, eventually producing hosts without target plasmid or vector Kamruzzaman et al. Despite the drawback of needing an antibiotic driver, this technology could potentially remove key traits from individuals when resistance is a barrier to surgery or chemotherapy, for instance.

In general a common limitation of all biotechnological approaches is their reliance on a plasmid or bacteriophage vehicles to spread a genetically modified GM tool between resistant bacteria Table 1. There has been intensive debate on which RM interventions are best, particularly in regard to the mixing and cycling of antibiotics Beardmore et al. However, this has been challenged by recent theory.

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