A methodology based on multiple criteria decision analysis for combining antibiotics in empirical therapy

Highlights

• A method for selecting empiric antibiotic therapy can be based on a optimization model.

• Multiple criteria are needed for development of new antibiotic combinations.

• This method can be used to design and adapt clinical protocols to local settings.

Abstract

Background

The current situation of critical progression in resistance to more effective antibiotics has forced the reuse of old highly toxic antibiotics and, for several reasons, the extension of the indications of combined antibiotic therapy as alternative options to broad spectrum empirical mono-therapy. A key aspect for selecting an appropriate and adequate antimicrobial therapy is that prescription must be based on local epidemiology and knowledge since many aspects, such as prevalence of microorganisms and effectiveness of antimicrobials, change from hospitals, or even areas and services within a single hospital. Therefore, the selection of combinations of antibiotics requires the application of a methodology that provides objectivity, completeness and reproducibility to the analysis of the detailed microbiological, epidemiological, pharmacological information on which to base a rational and reasoned choice.

Methods

We proposed a methodology for decision making that uses a multiple criteria decision analysis (MCDA) to support the clinician in the selection of an efficient combined empiric therapy. The MCDA includes a multi-objective constrained optimization model whose criteria are the maximum efficacy of therapy, maximum activity, the minimum activity overlapping, the minimum use of restricted antibiotics, the minimum toxicity of antibiotics and the activity against the most prevalent and virulent bacteria. The decision process can be defined in 4 steps: (1) selection of clinical situation of interest, (2) definition of local optimization criteria, (3) definition of constraints for reducing combinations, (4) manual sorting of solutions according to patient's clinical conditions, and (5) selection of a combination.

Experiments and results

In order to show the application of the methodology to a clinical case, we carried out experiments with antibiotic susceptibility tests in blood samples taken during a five years period at a university hospital. The validation of the results consists of a manual review of the combinations and experiments carried out by an expert physician that has explained the most relevant solutions proposed according to current clinical knowledge and their use.

Conclusion

We show that with the decision process proposed, the physician is able to select the best combined therapy according to different criteria such as maximum efficacy, activity and minimum toxicity. A method for the recommendation of combined antibiotic therapy developed on the basis of a multi-objective optimization model may assist the physicians in the search for alternatives to the use of broad-spectrum antibiotics or restricted antibiotics for empirical therapy. The decision proposed can be easily reproduced for any local epidemiology and any different clinical settings.

Introduction

Infections are currently considered to be one of the main causes of global morbidity [8]. Antibiotics have, since their creation, been recognized as the principal weapon against infection. However, the time has shown that the continuous use of antibiotics leads the organism to generate resistance to them. This is a threat to the effective treatment of bacterium infectious diseases, which is influenced by a huge amount of factors [10].

Some of these factors depend on the patient's health status, such as being immunocompromised (e.g., having HIV, neoplasia, etc.), or undergoing long periods of hospitalization with prolonged treatments and permanent devices (e.g., peripheral lines or catheters), which may stimulate the growth of resistant bacterial strains.

In a context of diagnostic uncertainty and prognosis, physicians should prescribe antimicrobial therapy in order to provide an appropriate and adequate initial therapy to hospitalized patients with serious infections and minimize the onset of bacterial resistance. As a general rule, a mono-therapy regimen is accepted as recommended, on the basis of the available evidence adapted to the local epidemiology. In practice, the objective of optimizing treatment and the current problem of resistance leads to the increasingly frequent prescription of antibiotic combinations and consequently to an increase in the variability of prescriptions, which reflects a lack of common criteria. As a consequence, the role of local recommendations increases their usefulness and need, while their definition is increasingly complex and laborious, requiring more frequent periodic updates.

When choosing the combination of antibiotics, the physician has one or several of the following objectives: (a) a synergistic action in the case of targeted treatments; (b) to broaden the spectrum of antimicrobial treatment in empirical treatments; (c) to decrease the probability of selecting resistant strains; (d) to prescribe an optimized treatment for specific microorganisms on the basis of observational information, and (e) in extreme cases, to prescribe a rescue treatment when confronted with therapeutic failure.

The choice of which antibiotics to combine is not a trivial task and it requires the application of a methodology that provides objectivity, completeness and reproducibility to the analysis. Due to these and other factors, we believe that an approach based on multiple criteria decision analysis (MCDA) is important. MCDA methods are designed to support decision makers in the evaluation of alternatives that include multiple criteria in an explicit manner [2].

What is more, it is important to note that the set of solutions that is presented to the decision-maker must be both feasible and suitable. To this end, the decision-maker have to define the main preferences that may be available a priori (before the decision is made), interactively (during the decision making) or a posteriori (after making the decision). In a posteriori approaches, in which our work is located, the decision-maker selects a solution from a given generated set of trade-off solutions based on his/her preferences, which are typically found by the optimization of an objective function or a set of objective functions.

The priority objective of a clinical cure involves aspects such as dosage, route of administration, duration or treatment of the specific infection in a given patient, taking into account the patient's physiological state. In this problem, there are additional secondary objectives, such as (1) achieving a cure at a minimal cost to the patient, (2) minimizing damage to the environment, also known as the ecological effect, by avoiding the selection of resistant microorganisms, and (3) mitigating the harm to the patient as the result of unforeseen adverse reactions to a drug.

In addition, when selecting antibiotics for empirical therapy there is a balance between seeking the most effective treatment while also following the local policies for antibiotic use at the hospital, which may include protecting some antibiotics owing to factors such as their high cost or toxicity. Current policies also include the protection of very powerful antibiotics, such as carbapenems, and new antibiotics in order to lengthen their useful life by preventing bacteria from developing resistance mechanisms to them.

One key factor is precisely the spectrum of antibiotics, which must be considered in two different dimensions: (a) that antibiotics are, by definition, active for a group of bacteria, and (b) that according to the local cumulative antibiogram, their spectrum is not effective for some bacteria. A broad spectrum antibiotic is able to fight (or be active) against at least 3 groups of bacteria or one large group (e.g., gram-positive bacteria). On the contrary, an antibiotic has a reduced spectrum if it is only active against some bacteria or a reduced number of bacteria.

From a clinical point of view, it is vital to start an appropriate therapy as early as possible in order to minimize morbidity, risks and complications, even in the absence of information concerning the microorganism that may be causing the infection. When a therapy is initiated without this information, it is called empirical therapy and is usually based on heuristics and expert rules. Information regarding previous patients that can guide the prescription of antibiotics is provided in the yearly cumulative antibiogram [35] whose limitations are well known [26]. For example, current hospital cumulative antibiograms do not take into account cross-resistance among different antibiotics, which would appear to be important when selecting initial combination regimens for serious gram-negative bacillary infections [9]. For empirical therapy, broad spectrum antibiotic are usually used, while narrow spectrum antibiotics are normally used in targeted or directed therapies.

In this research, we propose a multiple criteria decision analysis process to help the clinician in the selection of combined empirical antibiotic therapies. The validation consists of manual review by an expert physician of all combinations of antibiotics, and their comparison to reference combination in the literature. We show that the physician is able to select the best combined therapy according to different criteria, such as maximum coverage and efficacy or minimum toxicity.

The remainder of this paper is structured as follows. Section 2 provides a background to the method proposed and introduces related works. In Section 3, we define decision making process, the clinical criteria for antibiotic selection, the database and the optimization model for the problem. Section 4 shows the results obtained, while Section 5 motivates the applicability and clinical consistence of the results according to several scenarios. Finally Section 6 provides the conclusions reached after the research was carried out. In Appendix A we formally describe the multi-objective constrained combinatorial optimization model, and in Appendix B we show the formal criteria and constrains used for the combined empirical antibiotic therapy.