Abstract
Background: Antibiotics are often prescribed empirically to treat infection syndromes before causative bacteria and their susceptibility to antibiotics are identified. Guidelines on empiric antibiotic prescribing are key to effective treatment of infection syndromes, and need to be informed by likely bacterial aetiology and antibiotic resistance patterns. We aimed to create a clinically-relevant composite index of antibiotic resistance for common infection syndromes to inform recommendations at the national level. Methods: To create our index, we used open-access antimicrobial resistance (AMR) surveillance datasets, including the ECDC Surveillance Atlas, CDDEP ResistanceMap, WHO GLASS and the newly-available Pfizer ATLAS dataset. We integrated these with data on aetiology of common infection syndromes, existing empiric prescribing guidelines, and pricing and availability of antibiotics. Results: The ATLAS dataset covered many more bacterial species (287) and antibiotics (52) than other datasets (ranges = 8-11 and 16-32 respectively), but had a similar number of samples per country per year. Using these data, we were able to make empiric prescribing recommendations for bloodstream infection, pneumonia and cellulitis/skin abscess in up to 44 countries. There was insufficient data to make national-level recommendations for the other six syndromes investigated. Results are presented in an interactive web app, where users can visualise underlying resistance proportions to first-line empiric antibiotics for infection syndromes and countries of interest. Conclusions: We found that whilst the creation of a composite resistance index for empiric antibiotic therapy was technically feasible, the ATLAS dataset in its current form can only inform on a limited number of infection syndromes. Other open-access AMR surveillance datasets are largely limited to bloodstream infection specimens and cannot directly inform treatment of other syndromes. With improving availability of international AMR data and better understanding of infection aetiology, this approach may prove useful for informing empiric prescribing decisions in settings with limited local AMR surveillance data.
Highlights
In paragraph 4, it is described that “Providing .. guidelines based on open acces international antimicrobial resistance (AMR) surveillance data may help practitioners”
This work consists of three main objectives, where specific methods applied: 1. To compare antibiotic resistance proportions calculated using the ATLAS dataset with those estimated from other global AMR surveillance datasets
Surveillance data comparison The ATLAS dataset is an open-access dataset on human AMR surveillance data generated by the commercial pharmaceutical company Pfizer that contains granular antibiotic susceptibility data, including ‘raw’ minimum inhibitory concentration (MIC) data, for 633,820 bacterial clinical isolates collected from 77 countries and spanning 14 years[30]
Summary
In paragraph 4, it is described that “Providing .. guidelines based on open acces international AMR surveillance data may help practitioners”. If I understood correctly, the Weighted Incidence Syndromic Combination is exactly the same.[1] I suggest to move the paragraph about these indices from the discussion to the introduction, and make it clear that this is not a novel concept, but that this concept is taken further by developing an easy-to-use tool informed by publicly available resistance data. Methods: To create our index, we used open-access antimicrobial resistance (AMR) surveillance datasets, including the ECDC Surveillance Atlas, CDDEP ResistanceMap, WHO GLASS and the newly-available Pfizer ATLAS dataset We integrated these with data on aetiology of common infection syndromes, existing empiric prescribing guidelines, and pricing and availability of antibiotics. Conclusions: We found that whilst the creation of a composite resistance version 2 (revision)
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