Tag Archives: antibiotic resistance

“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!


“The dark art of antibiotic resistance surveillance”

This post is best read with a glass of wine…

As a profession, I think we are really not very good at measuring antibiotic resistance/antibiotic susceptibility patterns…

We are very happy to proclaim at the start of presentations “Antibiotic resistance is increasing”, or “In the era of increasing antibiotic resistance.” without providing any data to support this claim.

We need to move away from this type of talk. We are after all, scientists, not politicians.

However antimicrobial resistance surveillance is deceptively difficult. Here are a few reasons why high quality surveillance data is hard work, and requires a lot of thought and planning…

  • It is actually the trends that are critical:- There is a big difference between providing an annual antibiogram to clinicians, and presenting graphs which show changes in antimicrobial susceptibility over time. For example a GP looks at an annual antibiogram provided by the microbiology laboratory and sees that organism X has a resistance rate to antibiotic Y of 10%. Doesn’t sound too bad and certainly a viable treatment option. But if we knew that the resistance rate was 5% last year and 2% the year before that, then we have a problem. It seems obvious, but antimicrobial resistance surveillance is all about trends, not snapshots.
  • Too many permutations:-  There are more than 50 different commercially available antibiotics, and many hundreds of microrganisms identifiable on MALDI-TOF. So the number of antibiotic:microbe combinations is well in to the thousands. So which ones should be measured? The obvious ones are those that are commonly encountered and used, e.g susceptibility of E. coli to trimethoprim, ones that are clinically very important, e.g. susceptibility of Streptoccus pneumoniae to penicillin, or those of great public health importance, e.g. susceptibility of E. coli to meropenem. The ones to be avoided is where the combination falls outside these groups, particularly those where the numbers seen are insufficent to get meaningful data. e.g. Selenomonas spp. susceptibility to ciprofloxacin.  The important thing here is to decide on the microbe:antibiotic combinations to be measured before you start your surveillance program. Otherwise the data is open to exploitation, with people picking antimicrobial resistance surveillance data to suit their particular agenda. e.g. Antibiotic resistance is increasing because Microbe A is becoming more resistant to antibiotic B, and ignore the fact that Microbe C is becoming less resistant to antimicrobial D…
  • Different Definitions:- Because there are different antimicrobial testing standards out there, e.g. CLSI, EUCAST, CDS, etc., one person’s definition of resistant may not be the same as anothers… It is very important to ensure everyone has the same “definitions” of what is resistant and what is not resistant, before you even start. Otherwise you are on a hiding to nothing…
  • Politics:- Everybody has their own wishes and desires, and it is no different when it comes to measuring antibiotic resistance surveillance. Everybody wants to look at different antibiotic:microbe combinations, use different testing methodologies, present the data differently. This can cause problems with not only the accuracy of the data, but also in getting any surveillance data at all, when multiple laboratories are required to work together. When antibiotic resistance data requires the political co-operation of different countries then the difficulties move onto a whole new level altogether. Despite there being a willingness to work together, getting multi-national agreement on surveillance is a monumental task.
  • The goalposts get moved. Every so often the breakpoints get changed, for various reasons, so that an isolate that was once susceptible can become resistant (on paper), and vice versa. This is why using MIC values for surveillance purposes is so important, as it is an objective measurement which has no interpretation applied to it. This facilitates the acquisition of accurate surveillance data over many years.
  • Memory is erased. Sometimes when the laboratory information sytem (LIS) in a microbiology laoratory gets changed, a lot of the historical susceptibility data can get lost, either because it is not compatible with the new system, or not thought to be important enough to keep. Although electronic storage of laboratory data has been around for at least 20 years, as far as I am aware many microbiology laboratories do not have 20 years of data, for exactly this reason. It is a very important point to consider when considering a change of LIS.
  • Biases:- So many things can lead to bias in the surveillance data… Participation bias, sampling bias, patient cohort bias, testing bias, etc.. The list is virtually endless. All these things need to be considered and corrected for as best as possible when performing antimicrobial resistance surveillance.

So it is not easy, by any stretch of the imagination.

Good antimicrobial stewardship programmes should be based on having sound, standardised and objective baseline antimicrobial resistance data against which any interventions can be audited.

The other big area of surveillance which is essential to antimicrobial stewardship programmes is antimicrobial usage data. This data goes hand in hand with antimicrobial resistance surveillance.

Although it’s easy to talk about increasing antibiotic resistance, it is actually very difficult to measure properly…


“Time makes fools of us all”

"Ceftaroline fosamil, upsetting my traditional teaching on MRSA"
“Ceftaroline fosamil, upsetting my traditional teaching on MRSA”

Over the years I have been used to teaching everyone that MRSA is resistant to all cephalosporins, or indeed, all beta-lactams.

However I have been having to revise that statement recently with the advent of the new “5th generation” cephalosporins, ceftaroline and ceftobiprole.

These broad spectrum antibiotics have activity against MRSA by having enhanced affinity for the PBP2A binding protein. As new antibiotics go, they are not overly expensive. Their main downside is that they both need to be given intra-venously.

They have actually been around for a while now, but only more recently available in New Zealand.

So will I use these “new” antibiotics in clinical practice?

Possibly, although I always think  that new broad spectrum antibiotics such as these should be protected from widespread use in order to minimise resistance selection.

However if they are not used at all, they will be commercially unviable and be withdrawn from the market. A catch 22 situation.

I now have to revise my lectures. MRSA is resistant to most beta-lactams, with a couple of exceptions….”

Time makes fools of us all.