Impact of expert knowledge on the detection of patients at risk of antimicrobial therapy failure by clinical decision support systems

Journal of Biomedical Informatics 94 (2019) 103200

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Impact of expert knowledge on the detection of patients at risk of antimicrobial therapy failure by clinical decision support systems

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Bernardo Cánovas-Seguraa, , Antonio Moralesa, Jose M. Juareza, Manuel Camposa, Francisco Palaciosb ⁎

a b

Computer Science Faculty, University of Murcia, Spain University Hospital of Getafe, Madrid, Spain

ARTICLE INFO

ABSTRACT

Keywords: Clinical decision support systems Antimicrobial susceptibility testing Ontologies Production rules Knowledge representation and reasoning

Antimicrobial Susceptibility Tests (ASTs) are performed in hospitals to detect whether an infectious agent is resistant or susceptible to a set of antimicrobials. When AST results are available, the evaluation of the patient’s antimicrobial therapy is a critical task to ensure its effectiveness against the found microorganism. Since not all the available antimicrobials can be tested in ASTs, clinicians rely on their expert knowledge to complement AST results and prescribe the most appropriate antimicrobials for each infection. Our goal is to help physicians in this task by improving the detection of antimicrobial therapies at risk of failure by Clinical Decision Support Systems (CDSSs). With this aim, we have incorporated the EUCAST expert rules in antimicrobial susceptibility testing into a CDSS to improve the results of ASTs. In order to achieve this, we have combined both ontologies and production rules. Furthermore, we have evaluated the impact of EUCAST expert rules on the detection of antimicrobial therapies at risk of failure. We performed a retrospective study with one year of clinical data, obtaining a total of 148 alerts from which 62 (41.9%) were based on the additional expert knowledge. Furthermore, the evaluation of the clinical relevance of 27 alerts resulted in 8 of them (29.7%) being clinically relevant. Of these, 6 were based on expert knowledge. Finally, an alarm fatigue study suggests that waiting between 48 and 72 h from the reception of the AST results can significantly reduce the number of alerts that are unnecessary in our CDSS because they are already being addressed in the hospital’s daily workflow. In conclusion, we demonstrate that the incorporation of expert knowledge improves the capabilities of CDSSs as regards detecting the risk of antimicrobial therapy failure, which may improve the institutional outcomes in antimicrobial stewardship.

1. Introduction When a bacterial infection is suspected, it is a recommended practice to take a biological sample from the patient and perform a set of tests in order to evaluate the effectiveness of different antimicrobials against the bacteria that are causing the infection. The results of these tests, which are known as Antimicrobial Susceptibility Tests (ASTs), indicate whether the microorganism is resistant, susceptible or has an intermediate response to a set of specific antimicrobials. In this setting, the term resistant (vs. susceptible) to an antimicrobial means that “the antimicrobial activity is associated with a higher than expected likelihood of therapeutic failure (vs. therapeutic success)” [1]. In addition to carrying out these tests, the species of the microorganism causing the infection is also identified. Clinicians make use of these data to estimate

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the clinical outcome of different therapies and prescribe the most specific antimicrobial with which to deal with the infection [2]. When included in Clinical Decision Support Systems (CDSSs), AST results can be used for many different tasks, such as the study of the most common resistance patterns found within the institution and the selection of antimicrobial treatments to prevent outbreaks of multidrugresistant bacteria [3,4]. In this work, we focus on their use to detect patients at risk of antimicrobial therapy failure. Although a precise definition depends on each specific infection [5], we consider antimicrobial therapy failure when the prescribed antimicrobial therapy is unable to improve the health of an infected patient. One of the principal possible causes of this is that the infecting microorganism is resistant to all the treatments being currently prescribed to the patient [6]. Thanks to the AST results, the CDSS can automatically detect this situation and

Corresponding author. E-mail addresses: [email protected] (B. Cánovas-Segura), [email protected] (A. Morales), [email protected] (J.M. Juarez), [email protected] (M. Campos).

https://doi.org/10.1016/j.jbi.2019.103200 Received 18 December 2018; Received in revised form 2 May 2019; Accepted 3 May 2019 Available online 06 May 2019 1532-0464/ © 2019 Elsevier Inc. All rights reserved.

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• Resistant: The antimicrobial activity is associated with a higher than expected likelihood of therapeutic failure. • Intermediate or Indeterminate: The antimicrobial activity is associated with an indeterminate or uncertain therapeutic effect. • Susceptible: The antimicrobial activity is associated with a likelihood

warn physicians by launching an alert, commonly known as a drug-bug mismatch alert. All of the antimicrobials cannot be tested on every biological sample owing to material and time limitations, and it is for this reason that AST results cannot include the complete set of antimicrobials that are available for clinical practice. Experienced physicians overcome this lack of information by using their knowledge about the identified microorganism and the tested antimicrobials to determine whether a treatment is appropriate for the infection. Unfortunately, if a CDSS uses AST results as the only source of microbiological knowledge to detect the risk of antimicrobial therapy failure, its effectiveness will be limited. In this work, we solve the aforementioned limitations in a running CDSS (WASPSS [7–9]) by incorporating expert rules published by the European Committee on Antimicrobial Susceptibility Testing (EUCAST) [10,11]. This improvement complements AST results and increases the knowledge available when the system alerts physicians to a potential risk of antimicrobial therapy failure. Furthermore, we compare the results obtained by the regular CDSS and our proposal using EUCAST in three different experiments: a study of the quantity of knowledge available, an evaluation of the clinical relevance of fired alerts and an estimation of the alarm fatigue produced by different alert configurations. The rest of the paper is structured as follows: Section 2 provides a background about the different terms regarding ASTs that are used along this work, as well as the description of the CDSS used as baseline for the integration of knowledge. Section 3 describes the strategy followed to incorporate the knowledge into the system and an example of its use in practice. Section 4 explains the experimental settings and the detailed objectives of the evaluation. Section 5 shows the results obtained, which are discussed in Section 6. Section 7 comments other related works and their similarities and differences with our approach. Finally, Section 8 presents the conclusions of this work.

of therapeutic success.

This organisation also regularly publishes a set of rules, entitled EUCAST expert rules in antimicrobial susceptibility testing[10,11], which are based on recent discoveries as regards antimicrobial resistance and on the data that are available about the spread of new resistance phenotypes. These rules are classified into three different types:

• Intrinsic resistance rules. These rules indicate which antibiotics are •

•

2. Background In this section, we first provide a basic background in antimicrobial susceptibility testing and the EUCAST rules, which are the knowledge source selected to being incorporated into WASPSS, a CDSS that is also described in the later part of the section.

clinically useless against bacteria species as a result of their innate characteristics. Antimicrobial susceptibility testing is, therefore, unnecessary, although it may be performed as part of factory-defined panels of test agents. Interpretive rules. These rules suggest additional resistances/susceptibilities depending on the results obtained from the AST. These rules are based on different studies, case reports and/or microbiological data. They include a degree of evidence, based on the clinical or experimental evidence available, which is graded as follows [10]: A. There is good clinical evidence that reporting the test result as susceptible will lead to clinical failures. B. Evidence is weak and based on only a few case reports or on experimental models. C. There is no clinical evidence, but microbiological data suggest that the clinical use of the agent should be discouraged. Exceptional phenotypes rules. These rules associate microorganism species with very rare or never reported antibiotic resistances or susceptibilities, which might indicate a failure during bacteria identification when they are present in the test results. If the results are confirmed, EUCAST experts recommend sending the strain to a reference laboratory or another laboratory with expertise in resistance mechanisms for independent confirmation.

We have chosen these EUCAST expert rules so as to increase the amount of knowledge available for CDSSs. Despite the fact that EUCAST guidelines are thoroughly followed by laboratories in order to obtain the AST results, EUCAST expert rules require a later evaluation of these results, which is a tedious task without the help of computerised tools. Furthermore, they include the knowledge tacitly applied by experts in their daily work that may be useful for an automated CDSS. The types of rules suggested by EUCAST can be used to analyse AST results from different perspectives. In the following, we discuss the utility of these types of rules when included in a CDSS:

2.1. Antimicrobial resistance Each antimicrobial is known to be effective against specific groups of microorganisms, and therefore identifying the species of the microorganism causing an infection is a key factor in order to provide an effective treatment. A culture is a sample taken from the infected patient that is placed in a substrate in which microorganisms can grow easily. Once the culture is performed, its is possible to identify the species of the infecting microorganism. However, while belonging to the same species, bacteria are capable of developing or acquiring resistance mechanisms against different antimicrobials. For this reason, ASTs are needed to be performed after the culture in order to the estimate the clinical response of the microorganism against specific antimicrobials, which is known as the resistance phenotype (or susceptibility phenotype) of the microorganism. There are two different standards followed by most clinical microbiologists when performing ASTs: the guidelines provided by the Clinical Laboratory Standards Institute (CLSI) [12] from the United States, and those provided by the European Committee on Antimicrobial Susceptibility Testing (EUCAST) [13]. These standards detail the steps to be followed when performing ASTs, as well as the instructions for interpreting their results. Due to our clinical setting, we have focused on EUCAST guidelines. They define three possible results when estimating the clinical response of a microorganism against an antimicrobial, as indicated in [1]:

• In the first place, most of the knowledge that is available in intrinsic

• • 2

resistance rules is widely known by experts, since they include wellknown resistance patterns for each bacteria species. However, the spread of resistant strains and the discovery of new resistance mechanisms motivate their periodical revision [11]. Consequently, their inclusion in a CDSS not only provides the system with a relevant knowledge source but may also help physicians and microbiologists to keep up to date with intrinsic resistances. In the second place, interpretive rules infer new resistance patterns based on the results of the ASTs previously performed on the microorganism. They are based on the latest research studies, and their inclusion may, therefore, provide interesting resistance phenotypes that might not be known by most physicians. Finally, exceptional phenotypes rules indicate rare resistance patterns which may be related to a misidentification of the organism’s species, or that should be reported to specialised laboratories when they are found for validation. For example, one rule states that if an

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Enterococcus faecium is resistant to quinupristin-dalfopristin it is necessary to consider the “…likelihood of misidentification, especially if also susceptible to ampicillin” [10]. From the perspective of a CDSS, they may be useful as regards providing microbiologists with possible organism misclassification alerts.

hierarchical relationships between bacteria species, genera and families, in addition to different groups of antimicrobials. These relationships are tedious and cumbersome to model using production rules only. In order to deal with this problem, we use a semantic clinical taxonomy created by modelling terms and relationships using ontologies. On the one hand, the terms related to bacteria and their relationships (e.g. the family of bacteria to which each species belongs) have been extracted from the NCBI taxonomy [15]. This freely-available taxonomy is a classification of organisms, curated periodically, that includes bacteria species (the lowest taxonomical level used in EUCAST) and even some of their strains. On the other hand, the terms and relationships regarding antimicrobials have been extracted from the Anatomical Therapeutic Chemical (ATC) classification [16]. Both ontologies have been pruned in order to use only those concepts required by the EUCAST rules. For example, the concepts related with bacteria were the only ones used from the NCBI taxonomy. In a similar way, only the terms from the J01 - Antibacterials for systemic use group of ATC were used. A particularity of the ATC classification is that the same chemical compound can have different ATC codes depending on its usage. By selecting the J01 group only, we ensure that those antimicrobials related with the treatment of systemic infections, which cover most of the nomenclature required by EUCAST rules, have a unique ATC code. Some extra terms required for the definition of EUCAST rules were added manually (e.g. Gram-negative bacteria and ureidopenicillin antimicrobials). The terms used in the Electronic Medical Records are then asserted as individuals in the resulting ontologies, linking each of them with the related ontology concepts [17]. The interoperability between production rules and clinical ontologies is not a simple issue and is still being researched. There are different strategies by which to combine them, such as translating ontology relationships into rules [18–20] or extending the ontology axioms to incorporate rule semantics [21,22]. In a preliminary approach, we transformed the ontological relationships into types and rules using the dynamic typing capabilities of the rule engine [23]. In this case, we use a custom operator to check whether two concepts are related within the ontology. This operator can be used in the IF part of a rule and its implementation relies on an ontology reasoner to check these relationships [17]. Fig. 2 shows an example of a EUCAST interpretive rule coded in Drools by using our approach. Each EUCAST rule may have additional information, such as the rule number, the scientific basis or the evidence grade (see Fig. 2a). The type of metadata of each rule is also included within the rule declaration (see Fig. 2b). This allows the CDSS to use it, thus providing more detailed decision support. Moreover, the facts that caused the rule to be fired are linked to the inferred fact. By doing this we can identify precisely the reasons for each inferred result, even in cultures with multiple isolates or in different cultures with similar results.

In this work, we focus on the integration of intrinsic and interpretive EUCAST rules into a CDSS. In [10], the authors divide EUCAST rules into 13 tables and consider each row as a different rule. Each individual rule is, therefore, denominated as X.Y, where X is the table and Y is the row in which the rule is described. Moreover, the headings of some tables contain knowledge that is relevant for our work, such as “Gramnegative bacteria are also intrinsically resistant to benzylpenicillin, cefoxitin, …”, which was extracted from the heading of the table containing intrinsic resistances in non-fermentative Gram-negative bacteria [10]. The terms used to describe this knowledge include bacterial species and antimicrobials (e.g. “IF a Streptococcus pneumoniae is resistant to levofloxacin or moxifloxacin, THEN …”), along with other more abstract terms, such as Gram-negative bacteria, a wide group of bacteria species, and Fluoroquinolones, which is a group of antimicrobials that share a similar chemical structure. 2.2. Technological background The CDSS used to evaluate the impact of the incorporation of expert knowledge is the Wise Antimicrobial Stewardship Program Support System (WASPSS) [7,8]. It is focused on antimicrobial stewardship in hospitals and gathers data from several information systems (electronic health records, microbiology, pharmacy, etc.), in addition to providing the team responsible for antimicrobial stewardship in the institution with reports, alerts and graphics. WASPSS has been developed in collaboration with the University Hospital of Getafe and is currently being evaluated in nine other Spanish public hospitals. WASPSS receives the AST results from the microbiology system and allows clinicians to define alerts based on them, such as those regarding the risk of therapy failure owing to the detection of multidrug-resistant bacteria or specific resistance phenotypes that they need to monitor. It also includes the drug-bug mismatch alert, which is fired when the AST results obtained in a laboratory indicate that an infectious agent is resistant to the current therapy being administered to the patient. The results of this work are intended to be incorporated into WASPSS to detect more cases that might currently go unnoticed owing to the limitations of the AST results discussed above. Fig. 1 depicts a schema with the modules available in WASPSS and the new module that is being explained and evaluated in this work. WASPSS relies on an open-source reasoning engine to execute production rules (i.e. Drools [14]). This engine makes it possible for physicians and knowledge engineers to define complex rules in order to provide customized alerts. It is also intended to be helpful as regards enhancing WASPSS by incorporating extra knowledge, as occurs in this work.

3.2. Example of use of the incorporated knowledge

3. Methods

We illustrate the utility of the EUCAST rules for CDSS by introducing an example of a fictitious clinical case. This example is graphically represented in Fig. 3, starting with a patient hospitalised with symptoms of pneumonia (Step 1). First, the physician orders a culture and an AST with the aim of identifying and characterising the microorganism causing his/her infection (Step 2). However, the patient’s condition signifies that he/she cannot remain without treatment until the results are available, and the physician prescribes ciprofloxacin, an antimicrobial frequently used as an empiric treatment for pneumonia (Step 3). When the results of the culture become available (Step 4), they indicate that the infectious agent belongs to the Streptococcus pneumoniae species. Moreover, the AST results indicate that it is resistant to two

In this section, we describe the strategy followed to model the expert knowledge and incorporate it into WASPSS, along with an example of how this knowledge can be useful in a clinical case. 3.1. Knowledge modelling The characteristics of our technological background motivated us to choose production rules as the principal technique for modelling the new knowledge. Furthermore, the use of production rules makes it possible to explain the reasoning process to clinicians, which is of great value when providing grounded decision support. However, EUCAST expert rules are defined by using many complex 3

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Fig. 1. Schema with the modules available in WASPSS and the new module with EUCAST expert knowledge that is being evaluated in this work.

Fig. 2. Example of the translation of a EUCAST rule (a) into Drools code (b) using a custom operator. Note the way in which the metadata is coded and the use of the custom operator isOfType to perform queries regarding ontology concepts. 4

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Fig. 3. Practical example of the use of the EUCAST knowledge incorporated into a CDSS. The extra knowledge is used to increase the resistance phenotypes known for a microorganism and is, therefore, capable of detecting the risk of failure of a larger number of antimicrobial therapies.

antimicrobials called levofloxacin and moxifloxacin. Physicians have tacit knowledge that allows them to deal with this situation. However, the initial CDSS would not be able to detect that an inappropriate antimicrobial is being administered because ciprofloxacin was not included into the AST results (Step 5a). Using our proposed module that makes use of expert knowledge, AST results are inserted into the rule engine in order to infer new resistance phenotypes. Thanks to the ontology and the custom operator,

the engine detects that the microorganism belongs to the Streptococcus genus and it is able to launch the intrinsic resistance rule 4.5 from [10]. This rule indicates that any Streptococcus is intrinsically resistant to fusidic acid and aminoglycosides, a group of antimicrobials. Thanks to the ontology, the system is also able to infer that the microorganism is resistant to the specific members of the aminoglycosides group, such as amikacin or gentamicin. Furthermore, the engine can detect that the microorganism is a Gram-positive bacteria, and the heading of the 5

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“Intrinsic resistance in Gram-positive bacteria” table from [10] also indicates that these microorganisms are resistant to aztreonam, temocillin, polymyxin B/colistin and nalidixic acid. Moreover, the resistance patterns for the Streptococcus species were updated in [11], in which their resistance to ceftazidime was added to their intrinsic phenotype. With regard to interpretive rules, the module is able to launch rule 13.4 from [10], which suggests that “IF (a Streptococcus pneumoniae) is resistant to levofloxacin or moxifloxacin, THEN report as resistant to all fluoroquinolones”. Once again, the fluoroquinolones group is queried in the ontology and the engine infers new resistances to a large number of specific antimicrobials, including ciprofloxacin, the treatment currently being prescribed (Step 5b). This enables the CDSS to identify many more phenotypes of resistance than those present in the original AST results. Indeed, the patient was being treated with ciprofloxacin before knowing the infecting microorganism and its resistance patterns. With our proposal, the system is now able to detect that the antimicrobial therapy is at risk of failure owing to a drug-bug mismatch and, thanks to the use of production rules, it can provide the meta-data available in the original source. In this case, the system can notify, along with the alert, that the rule has an evidence grade of B, which means that the rule is based on “…only a few case reports or on experimental models.” [10]. This, therefore, enables clinicians to make a better-informed decision about the risks of continuing with the ciprofloxacin therapy.

Furthermore, it may occur that the inferences from different EUCAST rules overlap with each other. For example, a microorganism from the Staphylococcus aureus species is intrinsically resistant to temocillin, as stated in [11], yet interpretive rule 8.1 [10] suggests that if a Staphylococcus is resistant to isoxazolyl-penicillins, then it should be resistant to all -lactams, a group of antimicrobials to which temocillin belongs. In this experiment, we have prioritised intrinsic rules over interpretative rules (e.g. the last example has been counted as an intrinsic resistance phenotype), because intrinsic resistance phenotypes have a stronger scientific basis and acceptance than interpretive rules. All the experiments were executed in an Intel Xeon E3 at 3.60 Ghz with 8 GB of RAM and by running Windows 10, Drools 7.5.0 and HermiT 1.3.8. It took 10 s to build the knowledge base with the EUCAST rules and those required to detect the bug-drug mismatch alerts, 2,430 s to recover all the one-year data from the database, wrap it into objects and insert them as facts into the knowledge base, and 43 s to fire all the rules. In order to obtain a broader view of the impact of incorporating this knowledge, we have performed our experiments from three different perspectives: the quantity of new knowledge obtained (i.e. how much extra knowledge is generated?), the clinical relevance of the fired alerts (i.e. how relevant is the extra knowledge to clinical practice?) and the alarm fatigue that the new alerts may cause to clinicians (i.e. how should these alerts be launched for a better acceptance within the clinical workflow?). The details of each experiment are described below.

4. Experiments

4.2. Quantitative experiment

In this section, we describe the experiments carried out to evaluate the relevance of incorporating EUCAST expert knowledge into WASPSS for the detection of patients at risk of antimicrobial therapy failure. The objective of our experiments is to measure the impact of incorporating this knowledge from a clinical practice perspective. That is, we wish to compare the results obtained with and without expert knowledge and estimate their relevance for clinical practice.

The objective of our first experiment was to evaluate the quantity of new knowledge generated. We, therefore, measured the number of resistance phenotypes inferred and the number of new drug-bug mismatch alerts fired thanks to the incorporated knowledge, in comparison to the number of phenotypes available in the AST laboratory results and the alerts that were fired with them. Furthermore, we classified them by the kind of EUCAST rule used (intrinsic or interpretive) and, in the case of interpretive rules, by their grade of evidence (named A, B or C).

4.1. Experimental settings One year of clinical data was extracted from WASPSS. A total of 2231 medical records with 5661 positive cultures were evaluated. Of these, 9319 microorganisms were isolated, and 104,339 resistance phenotypes were present in AST results. 21,436 antimicrobial treatments were recovered, discarding those treatments used as decontamination in the intensive-care unit because they are not intended for infection treatment. All the EUCAST intrinsic rules [11] have been implemented (44 rules), although not all the interpretive rules [10] were included for several reasons: rules 8.2 to 8.5, 10.4, 11.2 and 11.3 require tests with antimicrobials or resistance mechanisms that are not available in our dataset, while rules 9.1, 9.2, 12.4 and 13.7 are recommendations about therapies or tests rather than inferences of phenotypes. Of the 36 interpretive rules available, 25 of them have, therefore, been implemented. When inferring new resistance phenotypes, it may occur that some of them have already been tested in the laboratory and, in a few cases, that the inferred phenotype did not coincide with that obtained in laboratory (e.g. the AST results indicate that a microorganism is susceptible to ciprofloxacin, but the EUCAST rules indicate that it should be resistant). In this experiment, we have prioritised the results originating from the laboratory, removing any inference already covered by a laboratory finding. The only exceptions were when a microorganism was classified as intermediate in the AST results and the EUCAST rules suggested that is was resistant, since some rules explicitly suggest these changes based on the differences observed between the in vitro and in vivo experiments. However, clinicians avoid the prescription of any antimicrobial found to be either intermediate or resistant and both results are, therefore, similar in clinical practice.

4.3. Clinical relevance experiment In our second experiment, we evaluated the clinical relevance of the risks detected. An expert clinician from the Intensive Care Unit (ICU) graded the drug-bug mismatch alerts fired according to their clinical relevance. We used a five-point Likert scale to provide grades between Irrelevant and High clinical relevance. In addition to each grade, a brief explanation of why the clinician made the decision regarding each alert was provided. It is worth noting that these evaluations were made a posteriori, by evaluating each patient’s entire medical record so as to grade the clinical relevance of the alert (and the related infection) in the final outcome. Owing to the complexity of the evaluation and the clinical staff’s time limitations, we performed the quality evaluation of the alerts occurring in the ICU service only. This ward has been selected because it is the most relevant for antimicrobial stewardship owing to the large number of different antimicrobials used on it. 4.4. Alarm fatigue experiment In our final experiment, we studied the number of alerts generated by varying the conditions required for their activation. Our objective was to reduce the well-known alarm fatigue that takes place when there are too many alerts, most of which are of low relevance. In this case, users tend to ignore all of them, which decreases the usability and acceptance of the CDSS [24]. Fig. 4 depicts a timeline showing the events that must occur for a drug-bug mismatch alert to be launched. The CDSS checks the treatments that were administered to a patient from 24 h before the collection of 6

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Fig. 4. Timeline with an example of activation and deactivation of a drug-bug mismatch alert. The interval tdelay has been varied from 0 to 120 h in our experiment in order to detect the best values with which to reduce the alarm fatigue.

the sample for the culture to tdelay hours after the AST results are available. If the microorganism found in the culture is resistant to all the treatments that comprise the antimicrobial therapy, the system considers that it could be inappropriate for the infection and launches a drug-bug mismatch alert. The CDSS will deactivate the alert when it detects that the antimicrobial therapy has changed and it includes a treatment to which the microorganism found is not resistant (or when the antimicrobial therapy ends). As indicated in Fig. 4, the time interval checked in order to launch an alert includes two time periods before and after the time during which the culture and the AST are being performed. These are intended to avoid providing alerts about situations that are not a real risk or that are already managed timely by the daily hospital workflow. For example, if the patient was treated with an effective antimicrobial a short time before the sample was taken, then the infection could have cleared up by the time the results of the AST became available. Similarly, if clinicians change the therapy appropriately a few hours after the AST results are available, by simply following their internal workflows, then there is no need to launch an extra alert in the CDSS. The time waited between the reception of the AST results and the launch of the alert, denoted as tdelay in Fig. 4, is currently set at 72 h. In this experiment, we have tested with tdelay values from 0 to 120 h in order to find the best configuration with which to avoid a large number of alerts, which might decrease the usability of the system.

classified by their evidence grade and the kind of phenotype inferred. It is worth noting that most of them (6,406 phenotypes, 45.2% of all the interpretive phenotypes) have the highest degree of evidence (i.e. graded as A in [10]). Similar results for this kind of rules were obtained in a previous experiment [23], in which only interpretive rules were applied to a different dataset. Fig. 7 summarizes the quantity of drug-bug mismatch alerts (148 alerts) that would have been launched based on the AST results only (86 alerts, 58.1% from total) and on the results extended with expert knowledge (62 alerts, 41.9% from total), classified in those based on intrinsic resistances only (31 alerts, 50.0% from those based on expert knowledge), interpretive rules only (2 alerts, 3.2% from those based on expert knowledge), or those based on both AST results and intrinsic (25 alerts, 40.3% from those based on expert knowledge) or interpretive EUCAST inferences (4 alerts, 6.5% from those based on expert knowledge). 5.2. Clinical relevance experiment Fig. 8 summarizes the scores given by our specialist to the aforementioned subset of alerts fired. A total of 27 alerts, those fired for the ICU ward, were chosen for clinical evaluation. 15 of them (55.6%) were based on laboratory results only, 11 of them (40.7%) were based on intrinsic resistance rules, and 1 of them (3.7%) was based on interpretive rules. Of the alerts based exclusively on laboratory results, 2 of them (13.3%) were graded with the highest clinical relevance score, while 11 of them (73.3%) were considered irrelevant. Of the 12 alerts obtained using extra knowledge, 3 of them (25.0%) were marked with the highest score, while 6 of them (50.0%) were considered as irrelevant. We consider relevant alerts to be those graded with 3 or more points. These marks were obtained for a total of 8 ICU alerts (29.6%), of which 6 (75.0%) use expert knowledge and only 2 (25.0%) are based exclusively on laboratory results. The p-value obtained after performing a Mann–Whitney–Wilcoxon Test between the scores of the two groups of alerts was 0.18.

5. Results 5.1. Quantitative experiment Fig. 5 shows the number of previously available AST results (104,339 phenotypes) and the new resistance phenotypes inferred (137,143 phenotypes). The latter are grouped by rule type (intrinsic or interpretive), and classified according to the kind of phenotype expressed (resistant, susceptible or intermediate). The number of intrinsic resistance phenotypes (122,972 phenotypes) is superior even to the number of resistance phenotypes available from the laboratory. The number of inferences from interpretive rules (14,171 phenotypes) is significantly lower than the others, yet there is still a considerable amount of extra knowledge. As mentioned previously, each interpretive rule has a degree of evidence which depends on the experimental basis of the rule. Fig. 6 shows the number of phenotypes inferred by using interpretive rules

5.3. Alarm fatigue experiment Fig. 9 shows the number of alerts generated according to the time that elapses between the reception of the results of the AST and the moment at which the alert is raised. As we increase the size of this time interval, the number of alerts decreases because new treatments may be 7

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Fig. 5. Number of resistance phenotypes available after executing the expert rules. These are classified by their origin (i.e. obtained in a laboratory or obtained after the application of EUCAST rules) and the kind of phenotype inferred.

prescribed to which the microorganism that caused the alert is not resistant. Most of the deactivated alerts are in the first 24 h (106 out of 297 alerts, 35.7% of the total number of active alerts), and only a few of them are deactivated after 72 h (11 out of 148 alerts, 7.4% of the total of active alerts). Most of the alerts that were deactivated within the first 24 h are based on AST results only (70 out of 106 deactivations, 66% of them), yet this number is similar to those based on EUCAST knowledge after that time. Note that the difference in active alerts between two consecutive time points in Fig. 9 is not always the same as the total number of alerts deactivated within that interval. For example, 195 alerts were active when considering a time lapse of 24 h, 41 alerts were deactivated within 24 and 48 h, but 155 alerts (rather than 154) were active when considering alerts with 48 h of delay. This is owing to those patients who had not been treated with antimicrobials when the AST results arrived, signifying that no alert was generated until the treatment was prescribed. In the previous example, one of the patients was not treated in the first 24 h after the AST results arrived. However, a treatment with a risk of being ineffective was prescribed after 24 h and before 48 h, which caused a new alert to be fired during that period only.

intrinsic resistances rules. According to our quantitative experiments, expert rules regarding intrinsic resistances generate the largest number of inferred phenotypes when compared to interpretive rules (122,972 from 137,143, that is, 89.7% of the inferred phenotypes). Furthermore, most of the alerts that rely on expert knowledge are based on intrinsic resistance rules (56 from 62, that is, 90.3% of the alerts based on expert knowledge). This kind of knowledge, therefore, seemed to be the most useful at detecting the risk of failure of antimicrobial therapies in our system. The interpretive rules inferred 14,171 resistance phenotypes and were required in only 6 drug-bug mismatch alerts, significantly less than those generated by intrinsic resistance rules or the AST results. This may be owing to the fact that these rules are scarce, in comparison with intrinsic resistance rules, and they require tests with specific antimicrobials that are not always available in AST results. Furthermore, not all the interpretive rules were implemented. However, the main reason for their lower results may be the fact that we considered intrinsic rules more reliable than interpretive ones. For this reason, we prioritise the inferences from intrinsic rules over those from interpretive rules when an overlap happens, which also decreases the number of alerts based on interpretive results in our analysis. With regard to exceptional phenotype rules, they have not been considered owing to the focus of this study. Their immediate use to improve the detection of antimicrobial treatments at risk of failure is questionable, since they do not provide extra resistance phenotypes that could be useful for firing the drug-bug mismatch alert. On the contrary,

6. Discussion 6.1. Impact of each kind of expert rule In the first place, we draw attention to the results concerning the

Fig. 6. Number of resistance phenotypes inferred by using interpretive rules. These are classified by their evidence grade and the kind of phenotype inferred. 8

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Fig. 7. Number of drug-bug mismatch alerts raised. These are classified by the kind of knowledge involved in them.

microorganism is found to be intermediate or resistant in a laboratory, considering it susceptible (or even intermediate if it was found to be resistant) because of a EUCAST rule is discouraged. The reason for this is that, even if resistance to an antimicrobial has never been found for a microorganism species, there may be new unknown resistance mechanisms capable of leading the treatment to fail [10]. 6.2. Clinical relevance With regard to the results of the alarm fatigue study, most alerts seem to be deactivated during the first 48 h. This indicates that most of the risk of therapy failure owing to a drug-bug mismatch is corrected in less than 48 h within the current clinical workflow, that is, without the need for extra alerts. After this time, the number of active alerts continues to decrease, but at a smaller rate. These results provide a hint as to how to define the drug-bug-mismatch alert if it is to be useful in the current clinical context. If the system is configured to launch the alert in less than 48 h after AST results are available, there will be a lot of alerts that, despite being relevant, are already tackled in the current workflow and they are, therefore, unnecessary. On the contrary, if the alert is launched with a delay of at least 48 h, the number of alerts decreases and, as stated in the evaluation of the clinical relevance, many of them may have a clinical impact (e.g. 29.6% in our experiment using 72 h as tdelay ). Our alert is currently configured to be launched after 72 h for clinical recommendation. However, based on these results, it might be interesting to reduce this interval to 48 h in order to obtain a good balance between clinical impact and the number of alerts generated. The alarm fatigue study also indicates that, even considering longer delays (5 days), a considerable number of alerts remain active. This is not a consequence of incorporating EUCAST knowledge, since the number of alerts based only on AST results is similar to those based on the inferred resistance phenotypes. However, this may indicate that the drug-bug mismatch alert should be refined further. Furthermore, with the p-value obtained when comparing the scores of alerts from laboratory and EUCAST knowledge (0.18), we cannot reject the null hypothesis that the clinical relevance of the two kind of alerts are similar. The small number of evaluated alerts (27) may have influenced in not having a clear statistical difference, yet our results suggest better scores for those alerts based on EUCAST knowledge. Some hints with which to refine these results have been obtained from the additional comments attached to the clinical evaluation of the selected subset of alerts. The most common reasons for grading alerts as Irrelevant (17 of 27 alerts, 63.0%) were that the clinical case was

Fig. 8. Score given by expert to the drug-bug mismatch alerts. These are divided into those that could have been raised with the AST results only and those that required the expert knowledge implemented. The grade varies from Irrelevant (1) to High clinical relevance (5).

their incorporation into CDSSs may be interesting as regards improving the detection of inconsistencies in the AST results or rare resistance phenotypes, but those problems are not within the scope of this work. Most of the inferred phenotypes are resistant rather than susceptible to antimicrobials. The main reason for this is that resistant is the most inferred phenotype of EUCAST rules. There are only a few interpretive rules (rules 8.3 and 11.1, with the lowest evidence grade) that may infer a susceptible phenotype when the microorganism is also found to be susceptible to a set of antimicrobials. Moreover, when a 9

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Fig. 9. Number of active alerts considering different delays. The lines indicate the number of alerts fired according to the time lapse used in the firing condition, while the bars indicate the number of rules deactivated within a time interval. The arrival of the AST results are considered to be the starting time.

incorporated when extra information regarding microbiological tests can be successfully integrated into WASPSS. However, a relevant change in our results is not expected owing to the small number of rules that have yet to be implemented and the limited impact of interpretive rules in the alerts. There are also seldom cases that may be undetected by our approach. For example, some single ATC codes indicate a combination of antimicrobials (e.g. ciprofloxacin and tinidazole), and even with separate inferred resistances to both of them no drug-bug mismatch alert would be fired since they are considered as different antimicrobials in the ATC classification. This is not a problem in our particular implementation since these ATC codes are not used in our system, yet it can technically happen and be a limitation in other scenarios. Moreover, our analysis has been made by using the complete results of both cultures and ASTs. However, culture results are usually available sooner than AST ones and, therefore, the intrinsic EUCAST rules could be launched earlier. This approach would improve the results that we obtained in this work, but a prospective clinical study would be required in order to assess its clinical relevance. Additionally, we focus on the drug-bug mismatch alert, since we consider it to be the most useful tool with which to avoid the risk of failure of antimicrobial therapies, but other closely-related alerts may also be useful from the broader perspective of CDSSs. For example, when a microorganism is found in a culture, but no antimicrobial therapy has been prescribed to the patient, the drug-bug mismatch alert will not be launched. In this case, a bug-no-drug alert might be fired to warn physicians about a possible infection not being treated. Furthermore, the clinical evaluation was made a posteriori, that is, after knowing the final diagnosis and development of the disease. Despite the fact that colonizations do not require an antimicrobial treatment and we consider them as irrelevant alerts for our work, a different kind of alert might be generated for each colonization in order to remind physicians to perform control cultures, so as to ensure that the colonization does not turn into an infection. Moreover, there may be other approaches with which to deal with the failure of antimicrobial therapies [6], such as the study of different antimicrobial efficacy measures [25] or the prediction of resistances to different treatments [26]. However, those kinds of alerts and approaches are beyond the scope of this work.

considered to be a contamination (5 of 27, 18.5%) or a colonization (10 of 27, 37.0%). A contamination means that the microorganism is unlikely to be really present in the patient, but it was present in the culture. A colonization, however, implies that the number of bacteria cells found was too low to support that the microorganism is responsible for the patient’s symptoms. In these cases, it is not, therefore, necessary to prescribe an antimicrobial for the microorganism found. The cultures are evaluated as contaminations and colonizations by the clinical staff, but this information is not available in our system. However, it seems possible to infer whether a culture result indicates a colonization or a contamination, which would make it possible to discard most of the irrelevant drug-bug mismatch alerts: some species, such as Staphylococcus epidermidis are commonly considered to be contaminations in blood cultures, and the number of colony-forming units is a value obtained in most cultures that may be used to distinguish between colonizations and infections. Their early identification may improve the relevance of the drug-bug mismatch alerts. Furthermore, the inference of these situations may lead to other alerts that would be useful for antimicrobial stewardship. For example, an excessive number of contaminations on a ward may indicate problems with sterilization or the manipulation of samples. Moreover, during the evaluation of these alerts from a strictly clinical perspective, we assessed that some of them would have a different grade according to other points of view, also relevant within antimicrobial stewardship: the microbiological and the epidemiological perspectives. For example, a colonization of microorganisms from the Burkholderia cepacia species may have no clinical relevance, while they are relevant from a microbiological point of view because they are bacteria commonly acquired within clinical institutions only. Similarly, a colonization by Enterococcus faecalis may not represent a risk for a patient and a drug-bug mismatch alert might, therefore, be irrelevant from a clinical perspective, but their capability of spreading to other patients may represent a threat from the epidemiological point of view. The extension of the AST results with expert knowledge may, therefore, provide extra benefits that are worthy of further study. 6.3. Limitations of the study Although we implemented most of the EUCAST interpretive rules, a small number of them could not been included. They are expected to be 10

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7. Related work

of expert knowledge to improve this task. We have successfully incorporated expert knowledge regarding antimicrobial resistance patterns (EUCAST rules) into a running CDSS by using production rules and ontologies. While production rules have been used to model the relationships between bacteria species and the inferred resistance phenotypes, ontologies have been useful to model the complex relationships in bacteria and antimicrobial taxonomies. The use of our approach has made it possible to detect, based on one year’s worth of data from a hospital, 62 patients with antimicrobial therapies at risk of failure that could only have been found thanks to the incorporated knowledge, while 86 were found without it. Furthermore, after the evaluation of those alerts from the ICU ward by an expert, a greater number of knowledge-based alerts had clinical relevance than those without it (6 out of 12 based on expert knowledge, as opposed to 2 out of 15 when using laboratory data only). Furthermore, the study of the quality of these rules revealed that the prior detection of contaminations and colonizations might decrease the number of alerts that are clinically irrelevant, in addition to the use of a delay of between 48 and 72 h from the reception of the AST results to fire them so as to reduce the alarm fatigue. According to the results obtained, we can conclude that the incorporation of expert knowledge regarding antimicrobial resistance patterns may be a key instrument as regards improving the capabilities of CDSSs focused on antimicrobial stewardship.

Others CDSSs have been making use of AST results to deal with problems of antibiotic prescription and infection diagnosis. The GermWatcher expert system [27,28] evaluates the results of cultures and ASTs with the aim of detecting nosocomial infections, that is, infections caused by microorganisms acquired within a clinical institution. It is based on the guidelines of the Nosocomial Infection Surveillance System from the Centers for Disease Control (CLSI guidelines). The Mercurio system [3] is another example focused on the detection and monitoring of nosocomial infections. It checks the susceptibility tests to ensure that they have been performed according to the CLSI guidelines. On the contrary, we focus on EUCAST rules because it is the standard required in our collaborating hospital. While most of these systems model their knowledge by using rules, there are other approaches to model knowledge and perform reasoning in this scenario. For example, the TREAT system [4,29] is based on causal probabilistic networks that combine local resistance patterns along with other clinical data to prescribe the most appropriate empiric treatment. In [30], authors propose a Bayesian approach to extend ASTs by using the local history of resistance and susceptibility results. Their method is proposed as a complement to the EUCAST interpretive rules, and they intend to incorporate it into the TREAT system. Furthermore, our approach focuses on detecting antimicrobial therapies at risk of failure, which requires the combination of AST results with data from ongoing antimicrobial therapies. Other approaches also take advantage of the combination of these data to provide decision support by different means such as using visualization techniques (e.g. COSARA [31], a system for clinical decision making at the ICU), creating spreadsheets automatically [32] or rising alerts when risk conditions are met, as commented in this work. Data integration is essential for providing decision support in the context of antimicrobial stewardship. Hospitals are encouraged to create multidisciplinary teams, called Antimicrobial Stewardship Program teams (ASP teams), to ensure the correct use of antibiotics within the institution [33,34]. It is crucial the combination of data from different sources such as microbiology, pharmacy and management, in order to provide decision support for these teams. In addition to WASPSS [7,8], the baseline system used to evaluate this work, there are other examples of CDSSs focused on ASP teams. In [32], a framework of medical informatics tools is proposed to generate spreadsheets with the antibiotics used daily and the parameters available to decide on the antimicrobial therapy of specific patients. Another example is The Antimicrobial Prescription Surveillance System [35,36], which is focused on identifying potentially inappropriate antimicrobial prescriptions and reporting them to pharmacists. This system uses a knowledge base of more than 50,000 rules, including drug-bug mismatch rules, based on local and general guidelines. However, we have not found a mention to the EUCAST standard or a study about the relevance of each specific knowledge source in the final alerts, as we do in this work. One of the problems of alert systems is the alarm fatigue discussed before. To deal with it, we considered time intervals between the events causing the alert to ensure that it is really relevant, and that the alert is not fired for a problem that could have been solved within the hospital daily workflow. There can be other different approaches, such as in [35], where the authors train an expert system that uses the feedback given by the ASP team to detect false-positive alerts. According to our results, the clinical relevance of our dug-bug mismatch alert is acceptable, and therefore our approach seems appropriate for this specific problem.

Declaration of Competing Interest None. Acknowledgements This work was partially funded by the Spanish Ministry of Science, Innovation and Universities under the SITSUS project (Ref: RTI2018094832-B-I00), and by the European Fund for Regional Development (EFRD, FEDER). References [1] G. Kahlmeter, D.F. Brown, F.W. Goldstein, A.P. MacGowan, J.W. Mouton, A. Österlund, A. Rodloff, M. Steinbakk, P. Urbaskova, A. Vatopoulos, European harmonization of MIC breakpoints for antimicrobial susceptibility testing of bacteria, J. Antimicrob. Chemother. 52 (2) (2003) 145–148, https://doi.org/10.1093/ jac/dkg312. [2] S. Leekha, C.L. Terrell, R.S. Edson, General principles of antimicrobial therapy, Mayo Clin. Proc. 86 (2) (2011) 156–167, https://doi.org/10.4065/mcp.2010.0639. [3] E. Lamma, P. Mello, A. Nanetti, F. Riguzzi, S. Storari, G. Valastro, Artificial intelligence techniques for monitoring dangerous infections, IEEE Trans. Inf. Technol. Biomed. 10 (1) (2006) 143–155, https://doi.org/10.1109/TITB.2005.855537. [4] L. Leibovici, M. Paul, A.D. Nielsen, E. Tacconelli, S. Andreassen, The TREAT project: decision support and prediction using causal probabilistic networks, Int. J. Antimicrob. Agents 30 (2007) 93–102, https://doi.org/10.1016/j.ijantimicag.2006. 11.027. [5] M. Sánchez García, Early antibiotic treatment failure, Int. J. Antimicrob. Agents 34 (SUPPL. 3) (2009) S14–S19, https://doi.org/10.1016/S0924-8579(09)70552-7. [6] D. Schwartz, U. Wu, R. Lyles, Y. Xiang, P. Kieszkowski, B. Hota, R. Weinstein, Lost in translation? Reliability of assessing inpatient antimicrobial appropriateness with use of computerized case vignettes, Infect. Control Hosp. Epidemiol. 30 (2) (2009) 163–171, https://doi.org/10.1086/593970. [7] F. Palacios, M. Campos, J.M. Juarez, S.E. Cosgrove, E. Avdic, B. Cánovas-Segura, A. Morales, M.E. Martínez-Nu nez, T. Molina-García, P. García-Hierro, J. CachoCalvo, A clinical decision support system for an Antimicrobial Stewardship Program, HEALTHINF 2016 - 9th International Conference on Health Informatics, Proceedings, SciTePress, Rome, 2016, pp. 496–501 doi:10.5220/ 0005824904960501. [8] B. Cánovas-Segura, M. Campos, A. Morales, J.M. Juarez, F. Palacios, Development of a clinical decision support system for antibiotic management in a hospital environment, Prog. Artif. Intell. 5 (3) (2016) 181–197, https://doi.org/10.1007/ s13748-016-0089-x. [9] F. Zerbato, B. Oliboni, C. Combi, M. Campos, J.M. Juarez, BPMN-Based Representation and Comparison of Clinical Pathways for Catheter-Related Bloodstream, Infections, 2015 International Conference on Healthcare Informatics, 2015, pp. 346–355, , https://doi.org/10.1109/ICHI.2015.49. [10] R. Leclercq, R. Cantón, D.F.J. Brown, C.G. Giske, P. Heisig, A.P. Macgowan, J.W. Mouton, P. Nordmann, A.C. Rodloff, G.M. Rossolini, C.J. Soussy, M. Steinbakk,

8. Conclusion In this work, we have tackled the problem of detecting the risk of failure of antimicrobial therapies by CDSSs, and have evaluated the use 11

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