Tag Archives: antibiograms

“The Annual Ritual”

A lot of diagnostic clinical microbiology laboratories create an annual antibiogram at the start of each year in order to inform laboratory users of local susceptibility rates for common microbe/antibiotic combinations. Here is a link to the one for my own laboratory.

It is a time honoured tradition, a ritual of sorts… There would be uproar from the clinicians if we didn’t produce it.

And yet such antibiograms are fundamentally flawed…

They are overly simplistic because resistance rates can vary markedly in different patient cohorts and different sample types.

Take the following examples (based on my local data searches):

  • Antibiotic resistance rates for urinary isolates differ markedly according to age and sex. Urinary isolates from young women have much lower resistance rates to uropathogens than old men, with the difference being up to 25% depending on what microbe/antibiotic is being tested. This has very obvious implications for empirical antibiotic choices for UTI in different population cohorts.
  • Staphylococcus aureus resistance rates to mupirocin are much higher in young people with recurrent skin infections than in the (elderly) cohort about to go elective¬† joint replacement.
  • MRSA rate as a percentage of total Staphylococcus aureus isolates is significantly higher in superficial wound swabs than it is in blood cultures.

These are just a few examples of many, but the common theme here is that different exposure rates to particular antibiotics in different population cohorts lead to different resistance rates.

So I suspect the days are numbered of static antibiograms shown in table form on an A4 sheet of paper.

So last year!

I see the future being an electronic interactive antibiogram, possibly in the form of a smartphone “app”. The clinician enters a few important variables, such as patient age, sex, sampling site, and community/hospital patient, along with the microbe isolated. The app then calculates a more accurate antibiogram based on the particular cohort that this patient falls into.

This is the future, I am sure of it.

The only downside to such an approach is by splitting the total susceptibility data available into different cohorts, the sample size for analysis goes down, which can then lead to bigger margins of error in the results for less common microbe/antimicrobial combinations. This however could be addressed in the app by adding a disclaimer to resistance rates calculated from small sample numbers.

And maybe an interactive electronic antibiogram is in existence already, in an ultra-progressive laboratory somewhere… If so, please let me know!

I had better get started on creating that app!

Michael