Evaluating the Dose, Indication and Agreement with Guidelines of Antimicrobial Use in Companion Animal Practice with Natural Language Processing
Hur, Brian, Laura Y. Hardefeldt, Karin M. Verspoor, Timothy Baldwin, and James R. Gilkerson
01/02/2022
Background
As antimicrobial prescribers, veterinarians contribute to the emergence of MDR pathogens. Antimicrobial stewardship programmes are an effective means of reducing the rate of development of antimicrobial resistance. A key component of antimicrobial stewardship programmes is selecting an appropriate antimicrobial agent for the presenting complaint and using an appropriate dose rate for an appropriate duration.
Objectives
To describe antimicrobial usage, including dose, for common indications for antimicrobial use in companion animal practice.
Methods
Natural language processing (NLP) techniques were applied to extract and analyse clinical records.
Results
A total of 343 668 records for dogs and 109 719 records for cats administered systemic antimicrobials from 1 January 2013 to 31 December 2017 were extracted from the database. The NLP algorithms extracted dose, duration of therapy and diagnosis completely for 133 046 (39%) of the records for dogs and 40 841 records for cats (37%). The remaining records were missing one or more of these elements in the clinical data. The most common reason for antimicrobial administration was skin disorders (n = 66 198, 25%) and traumatic injuries (n = 15 932, 19%) in dogs and cats, respectively. Dose was consistent with guideline recommendations in 73% of cases where complete clinical data were available.
Conclusions
Automated extraction using NLP methods is a powerful tool to evaluate large datasets and to enable veterinarians to describe the reasons that antimicrobials are administered. However, this can only be determined when the data presented in the clinical record are complete, which was not the case in most instances in this dataset. Most importantly, the dose administered varied and was often not consistent with guideline recommendations.
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