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

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.